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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/kaggle/input/titanic/test.csv\n",
"/kaggle/input/titanic/train.csv\n",
"/kaggle/input/titanic/gender_submission.csv\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
"\n",
"import os\n",
"for dirname, _, filenames in os.walk('/kaggle/input'):\n",
" for filename in filenames:\n",
" print(os.path.join(dirname, filename))\n",
"\n",
"# Any results you write to the current directory are saved as output.\n",
"\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.metrics import mean_absolute_error\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.impute import SimpleImputer\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"from sklearn.model_selection import cross_val_score\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
"_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Moran, Mr. James</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>330877</td>\n",
" <td>8.4583</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>McCarthy, Mr. Timothy J</td>\n",
" <td>male</td>\n",
" <td>54.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>17463</td>\n",
" <td>51.8625</td>\n",
" <td>E46</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Palsson, Master. Gosta Leonard</td>\n",
" <td>male</td>\n",
" <td>2.0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>349909</td>\n",
" <td>21.0750</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
" <td>female</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>347742</td>\n",
" <td>11.1333</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
" <td>female</td>\n",
" <td>14.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>237736</td>\n",
" <td>30.0708</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"5 6 0 3 \n",
"6 7 0 1 \n",
"7 8 0 3 \n",
"8 9 1 3 \n",
"9 10 1 2 \n",
"\n",
" Name Sex Age SibSp \\\n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"5 Moran, Mr. James male NaN 0 \n",
"6 McCarthy, Mr. Timothy J male 54.0 0 \n",
"7 Palsson, Master. Gosta Leonard male 2.0 3 \n",
"8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n",
"9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S \n",
"3 0 113803 53.1000 C123 S \n",
"4 0 373450 8.0500 NaN S \n",
"5 0 330877 8.4583 NaN Q \n",
"6 0 17463 51.8625 E46 S \n",
"7 1 349909 21.0750 NaN S \n",
"8 2 347742 11.1333 NaN S \n",
"9 0 237736 30.0708 NaN C "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Load training data and test data\n",
"train_data = pd.read_csv(\"/kaggle/input/titanic/train.csv\")\n",
"test_data = pd.read_csv(\"/kaggle/input/titanic/test.csv\")\n",
"train_data[1:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploratory Data Analysis\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>714.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>446.000000</td>\n",
" <td>0.383838</td>\n",
" <td>2.308642</td>\n",
" <td>29.699118</td>\n",
" <td>0.523008</td>\n",
" <td>0.381594</td>\n",
" <td>32.204208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>257.353842</td>\n",
" <td>0.486592</td>\n",
" <td>0.836071</td>\n",
" <td>14.526497</td>\n",
" <td>1.102743</td>\n",
" <td>0.806057</td>\n",
" <td>49.693429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.420000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>223.500000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>20.125000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7.910400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>446.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.000000</td>\n",
" <td>28.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>14.454200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>668.500000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>38.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>31.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>891.000000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>80.000000</td>\n",
" <td>8.000000</td>\n",
" <td>6.000000</td>\n",
" <td>512.329200</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass Age SibSp \\\n",
"count 891.000000 891.000000 891.000000 714.000000 891.000000 \n",
"mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n",
"std 257.353842 0.486592 0.836071 14.526497 1.102743 \n",
"min 1.000000 0.000000 1.000000 0.420000 0.000000 \n",
"25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n",
"50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n",
"75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n",
"max 891.000000 1.000000 3.000000 80.000000 8.000000 \n",
"\n",
" Parch Fare \n",
"count 891.000000 891.000000 \n",
"mean 0.381594 32.204208 \n",
"std 0.806057 49.693429 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 7.910400 \n",
"50% 0.000000 14.454200 \n",
"75% 0.000000 31.000000 \n",
"max 6.000000 512.329200 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Print summary statistics\n",
"train_data.describe()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"# Plot Survival counts\\nplt.bar([0,1],train_data['Survived'].value_counts())\\nplt.ylabel('Counts')\\nplt.xticks([0, 1], labels=['Not\\nSurvived', 'Survived'])\\nplt.title('Survival counts')\\nplt.show()\\n\\n# Investigate relationship between PassengerId and Survival rate\\n# Bin the Passenger Id data into bins of interval 100\\ncounts = train_data['PassengerId'].count()\\nbin_size = 100\\nbin_range = np.arange(counts,step=bin_size)\\nbin_labels = np.arange(counts/bin_size-1) \\n\\ntrain_data['Id_binned']=pd.cut(x = train_data['PassengerId'],\\n bins = bin_range, \\n labels = bin_labels)\\nId_binned = train_data.groupby('Id_binned').mean()['Survived']\\nplt.bar(bin_labels, Id_binned)\\nplt.xticks(bin_labels, labels=bin_range)\\nplt.ylabel('Survival rate')\\nplt.xlabel('Binned passenger Id')\\nplt.title('Survival rate by Passenger Id')\\nplt.show()\\n\\n# Impact of SibSp on Survival \\nplt.figure()\\nsns.countplot(x='Survived', data=train_data, hue='SibSp')\\nplt.xticks([0, 1], labels=['Not\\nSurvived', 'Survived'])\\nplt.legend(loc='upper right', title='SibSp')\\nplt.title('Impact of SibSp on Survival')\\nplt.show()\\n\\n# Impact of Embarkation port on Survival \\nplt.figure()\\nsns.countplot(x='Survived', data=train_data, hue='Embarked')\\nplt.xticks([0, 1], labels=['Not\\nSurvived', 'Survived'])\\nplt.legend(loc='upper right', title='Embarkation port')\\nplt.title('Survival by Embarkation port')\\nplt.show()\\n\\n# Split data by survival\\na = train_data[train_data['Survived'] == 0]\\nb = train_data[train_data['Survived'] == 1]\\n\\n# Impact of Fare on Survival \\nplt.figure()\\nplt.boxplot([a['Fare'], b['Fare']])\\nplt.ylabel('Fare')\\nplt.xticks([1, 2], ['Not Survived','Survived'])\\nplt.title('Survival by Fare')\\nplt.show()\""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''# Plot Survival counts\n",
"plt.bar([0,1],train_data['Survived'].value_counts())\n",
"plt.ylabel('Counts')\n",
"plt.xticks([0, 1], labels=['Not\\nSurvived', 'Survived'])\n",
"plt.title('Survival counts')\n",
"plt.show()\n",
"\n",
"# Investigate relationship between PassengerId and Survival rate\n",
"# Bin the Passenger Id data into bins of interval 100\n",
"counts = train_data['PassengerId'].count()\n",
"bin_size = 100\n",
"bin_range = np.arange(counts,step=bin_size)\n",
"bin_labels = np.arange(counts/bin_size-1) \n",
"\n",
"train_data['Id_binned']=pd.cut(x = train_data['PassengerId'],\n",
" bins = bin_range, \n",
" labels = bin_labels)\n",
"Id_binned = train_data.groupby('Id_binned').mean()['Survived']\n",
"plt.bar(bin_labels, Id_binned)\n",
"plt.xticks(bin_labels, labels=bin_range)\n",
"plt.ylabel('Survival rate')\n",
"plt.xlabel('Binned passenger Id')\n",
"plt.title('Survival rate by Passenger Id')\n",
"plt.show()\n",
"\n",
"# Impact of SibSp on Survival \n",
"plt.figure()\n",
"sns.countplot(x='Survived', data=train_data, hue='SibSp')\n",
"plt.xticks([0, 1], labels=['Not\\nSurvived', 'Survived'])\n",
"plt.legend(loc='upper right', title='SibSp')\n",
"plt.title('Impact of SibSp on Survival')\n",
"plt.show()\n",
"\n",
"# Impact of Embarkation port on Survival \n",
"plt.figure()\n",
"sns.countplot(x='Survived', data=train_data, hue='Embarked')\n",
"plt.xticks([0, 1], labels=['Not\\nSurvived', 'Survived'])\n",
"plt.legend(loc='upper right', title='Embarkation port')\n",
"plt.title('Survival by Embarkation port')\n",
"plt.show()\n",
"\n",
"# Split data by survival\n",
"a = train_data[train_data['Survived'] == 0]\n",
"b = train_data[train_data['Survived'] == 1]\n",
"\n",
"# Impact of Fare on Survival \n",
"plt.figure()\n",
"plt.boxplot([a['Fare'], b['Fare']])\n",
"plt.ylabel('Fare')\n",
"plt.xticks([1, 2], ['Not Survived','Survived'])\n",
"plt.title('Survival by Fare')\n",
"plt.show()'''"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Feature Analysis**"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"List of all features: ['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked']\n"
]
}
],
"source": [
"# Examine features\n",
"print(\"List of all features:\", train_data.columns.tolist())"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PassengerId int64\n",
"Pclass int64\n",
"Name object\n",
"Sex object\n",
"Age float64\n",
"SibSp int64\n",
"Parch int64\n",
"Ticket object\n",
"Fare float64\n",
"Cabin object\n",
"Embarked object\n",
"dtype: object"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check data types\n",
"test_data.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# Unique values in Pclass: 3\n",
"# Unique values in Name: 891\n",
"# Unique values in Sex: 2\n",
"# Unique values in Ticket: 681\n",
"# Unique values in Cabin: 147\n",
"# Unique values in Embarked: 3\n"
]
}
],
"source": [
"# Calculate the number of unique values in the categorical variables\n",
"categorical_vars = ['Pclass','Name','Sex','Ticket','Cabin','Embarked']\n",
"numerical_vars = ['Age','SibSp','Parch','Fare']\n",
"\n",
"for col in categorical_vars:\n",
" print('# Unique values in {}: {}'.format(col, train_data[col].nunique()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Drop the categorical features 'Ticket' and 'Cabin' for the moment as they have a large number of unique values which is undesirable as it may limit accuracy of the Classification model.\n",
"* Keep the categorical feature 'Name' even though it has a large number of unique values as it will be used for feature Engineering.\n",
"* Keep the categorical features 'Pclass' and 'Sex'.\n",
"* Keep the numerical features 'Age', SibSp, 'Parch' and 'Fare'"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"feature = ['PassengerId', 'Name', 'Pclass', 'Sex', 'Age', 'SibSp',\n",
" 'Parch', 'Fare', 'Embarked']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Setup Random Forest Model\n",
"Creates a random forest model and returns predictions for test_data\n",
"\n",
"Require test_data[feature] and train_data[feature]\n",
"Required argument 'feature' is list of features used in the model\n",
"Optional arguments of 'val_max_leaf_nodes' and 'val_max_depth' which are parameters for the RandomForestClassifier() model"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# Select features for train and test data\n",
"X_ = train_data[feature]\n",
"y = train_data[\"Survived\"]\n",
"X_test_ = test_data[feature]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Perform Feature engineering on the 'Name' feature\n",
"\n",
"First check the counts for each value"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py:376: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" self.obj[key] = _infer_fill_value(value)\n",
"/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py:494: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" self.obj[item] = s\n"
]
},
{
"data": {
"text/plain": [
" Mr 517\n",
" Miss 182\n",
" Mrs 125\n",
" Master 40\n",
" Dr 7\n",
" Rev 6\n",
" Col 2\n",
" Major 2\n",
" Mlle 2\n",
" Lady 1\n",
" Jonkheer 1\n",
" Sir 1\n",
" Mme 1\n",
" Capt 1\n",
" Ms 1\n",
" Don 1\n",
" the Countess 1\n",
"Name: Title, dtype: int64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Extract title from the 'Name' column\n",
"X_.loc[:,'Title'] = X_['Name'].str.split(',').str[1].str.split('.').str[0]\n",
"X_test_.loc[:,'Title'] = X_test_['Name'].str.split(',').str[1].str.split('.').str[0]\n",
"X_['Title'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Combine rare titles into an 'Other' variable "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py:4117: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" errors=errors,\n"
]
}
],
"source": [
"X_.loc[(X_['Title'] != ' Mr') & (X_['Title'] != ' Mrs') & (X_['Title'] != ' Miss') & (X_['Title'] != ' Master'), 'Title'] = 'Other' \n",
"X_test_.loc[(X_['Title'] != ' Mr') & (X_test_['Title'] != ' Mrs') & (X_test_['Title'] != ' Miss') & (X_test_['Title'] != ' Master'), 'Title'] = 'Other' \n",
"X_.drop(columns=['Name'], inplace=True)\n",
"X_test_.drop(columns=['Name'], inplace=True) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Separate into categorical and numerical variables\n",
"* Impute numerical data using columns mean\n",
"* Impute categorical data using most frequent. One hot encode categorical columns"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"categorical_cols = [cname for cname in X_ if\n",
" X_[cname].dtype == \"object\"]\n",
"\n",
"numerical_cols = [cname for cname in X_ if \n",
" X_[cname].dtype in ['int64', 'float64', 'uint8']]\n",
"\n",
"cols = categorical_cols + numerical_cols\n",
"X = X_[cols].copy()\n",
"X_test = X_test_[cols].copy()\n",
"\n",
"# Preprocessing for numerical data\n",
"numerical_transformer = SimpleImputer(strategy='mean')\n",
"\n",
"# Preprocessing for categorical data\n",
"categorical_transformer = Pipeline(steps=[\n",
" ('imputer', SimpleImputer(strategy='most_frequent')),\n",
" ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
" ])\n",
"#categorical_transformer = SimpleImputer(strategy='most_frequent')\n",
"\n",
"# Bundle preprocessing for numerical and categorical data\n",
"preprocessor = ColumnTransformer(\n",
"transformers=[\n",
" ('num', numerical_transformer, numerical_cols),\n",
" ('cat', categorical_transformer, categorical_cols)\n",
" ])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Define a Random Forest model using some default parameters"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"model = RandomForestClassifier(n_estimators = 100, max_depth = 5, random_state=0)\n",
"\n",
"# Bundle preprocessing and modeling code in a pipeline\n",
"my_pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
" ('model', model)\n",
" ])\n",
"\n",
"# Preprocessing of training data, fit model \n",
"my_pipeline.fit(X, y)\n",
"\n",
"\n",
"# Preprocessing of validation data, get predictions\n",
"preds = my_pipeline.predict(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define functions to characterise the accuracy of the model and importance of each feature"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"def get_gini(X, my_pipeline=my_pipeline, numerical_cols=numerical_cols):\n",
" \n",
" \"Calculates Gini importance of each feature\"\n",
" \n",
" cat_features = my_pipeline.named_steps['preprocessor'].transformers_[1][1].named_steps['onehot'].get_feature_names(categorical_cols)\n",
" num_features = numerical_cols\n",
" feature_list = np.append(num_features, cat_features)\n",
" imps = my_pipeline.steps[1][1].feature_importances_\n",
" for i in zip(feature_list, imps):\n",
" print(\"Gini importance of each feature: \", i)\n",
"\n",
"\n",
"def get_cvs(my_pipeline, X, y):\n",
" \n",
" \"Calculates Mean absolute error for a list of features\"\n",
" \n",
" cvs = 1 * cross_val_score(my_pipeline, X, y,\n",
" cv=5,\n",
" scoring='accuracy')\n",
"\n",
" print('Mean Absolute Error: ', feature, cvs.mean(), '\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the importances of each feature, then the accuracy of the model using a cross-validation accuracy score"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gini importance of each feature: ('PassengerId', 0.042840400892992285)\n",
"Gini importance of each feature: ('Pclass', 0.11678843614693533)\n",
"Gini importance of each feature: ('Age', 0.05432331533135518)\n",
"Gini importance of each feature: ('SibSp', 0.04146719365493314)\n",
"Gini importance of each feature: ('Parch', 0.020968146202611906)\n",
"Gini importance of each feature: ('Fare', 0.09036919293067613)\n",
"Gini importance of each feature: ('Sex_female', 0.1915391176334786)\n",
"Gini importance of each feature: ('Sex_male', 0.1436959758930339)\n",
"Gini importance of each feature: ('Embarked_C', 0.011417513842479417)\n",
"Gini importance of each feature: ('Embarked_Q', 0.007242775549603224)\n",
"Gini importance of each feature: ('Embarked_S', 0.00960207484967599)\n",
"Gini importance of each feature: ('Title_ Master', 0.016704672754957305)\n",
"Gini importance of each feature: ('Title_ Miss', 0.03895350657974646)\n",
"Gini importance of each feature: ('Title_ Mr', 0.17785740133929842)\n",
"Gini importance of each feature: ('Title_ Mrs', 0.028865304799823667)\n",
"Gini importance of each feature: ('Title_Other', 0.007364971598399196)\n",
"Mean Absolute Error: ['PassengerId', 'Name', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked'] 0.824913690289373 \n",
"\n"
]
}
],
"source": [
"get_gini(X)\n",
"get_cvs(my_pipeline, X, y)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"from sklearn.model_selection import GridSearchCV\\n\\nparams = { \\n 'model__n_estimators': [50, 100, 200, 500],\\n 'model__max_features': ['auto', 'sqrt', 'log2'],\\n 'model__max_depth' : [None, 4, 5, 6, 7, 8],\\n 'model__criterion' :['gini', 'entropy'],\\n 'model__max_leaf_nodes' :[None, 5, 50, 500]\\n 'model__random_state' :[0]\\n}\\n\\nCV = GridSearchCV(my_pipeline, params, scoring = 'neg_mean_absolute_error', cv=5)\\nCV.fit(X, y) \\n\\nprint('Best score and parameter combination = ')\\n\\nprint(CV.best_score_) \\nprint(CV.best_params_) \\n\\n#y_pred = CV.predict(x_valid_cont)\\n#print('MAE on validation set: %s' % (round(MAE(y_valid, y_pred), 5)))\\n\""
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''from sklearn.model_selection import GridSearchCV\n",
"\n",
"params = { \n",
" 'model__n_estimators': [50, 100, 200, 500],\n",
" 'model__max_features': ['auto', 'sqrt', 'log2'],\n",
" 'model__max_depth' : [None, 4, 5, 6, 7, 8],\n",
" 'model__criterion' :['gini', 'entropy'],\n",
" 'model__max_leaf_nodes' :[None, 5, 50, 500]\n",
" 'model__random_state' :[0]\n",
"}\n",
"\n",
"CV = GridSearchCV(my_pipeline, params, scoring = 'neg_mean_absolute_error', cv=5)\n",
"CV.fit(X, y) \n",
"\n",
"print('Best score and parameter combination = ')\n",
"\n",
"print(CV.best_score_) \n",
"print(CV.best_params_) \n",
"\n",
"#y_pred = CV.predict(x_valid_cont)\n",
"#print('MAE on validation set: %s' % (round(MAE(y_valid, y_pred), 5)))\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"#my_pipeline.get_params().keys()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean Absolute Error: ['PassengerId', 'Name', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked'] 0.8215366267026554 \n",
"\n"
]
}
],
"source": [
"# Redefine model hyperparmeters using best settings from grid search:\n",
"# {'model__criterion': 'gini', 'model__max_depth': 5, 'model__max_features': 'auto', 'model__max_leaf_nodes': 50, 'model__n_estimators': 50}\n",
"\n",
"# Define model\n",
"model = RandomForestClassifier(criterion='gini', max_depth=5, max_features='auto', max_leaf_nodes=50, n_estimators = 50, random_state=0)\n",
"\n",
"# Bundle preprocessing and modeling code in a pipeline\n",
"my_pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
" ('model', model)\n",
" ])\n",
"\n",
"# Preprocessing of training data, fit model \n",
"my_pipeline.fit(X, y)\n",
"\n",
"# Preprocessing of validation data, get predictions\n",
"preds = my_pipeline.predict(X_test)\n",
"\n",
"get_cvs(my_pipeline, X, y)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'# Try with lowerst GINI feaature (embarked) removed\\n\\nfeature = [\\'PassengerId\\', \\'Pclass\\', \\'Sex\\', \\'Age\\', \\'SibSp\\',\\n \\'Parch\\', \\'Fare\\']\\n\\nX_ = train_data[feature]\\nX_test_ = test_data[feature]\\n\\ncategorical_cols = [cname for cname in X_ if\\n X_[cname].dtype == \"object\"]\\n\\nnumerical_cols = [cname for cname in X_ if \\n X_[cname].dtype in [\\'int64\\', \\'float64\\', \\'uint8\\']]\\n\\ncols = categorical_cols + numerical_cols\\nX = X_[cols].copy()\\nX_test = X_test_[cols].copy()\\n\\n# Preprocessing for numerical data\\nnumerical_transformer = SimpleImputer(strategy=\\'mean\\')\\n\\n# Preprocessing for categorical data\\ncategorical_transformer = Pipeline(steps=[\\n (\\'imputer\\', SimpleImputer(strategy=\\'most_frequent\\')),\\n (\\'onehot\\', OneHotEncoder(handle_unknown=\\'ignore\\'))\\n ])\\n#categorical_transformer = SimpleImputer(strategy=\\'most_frequent\\')\\n\\n# Bundle preprocessing for numerical and categorical data\\npreprocessor = ColumnTransformer(\\ntransformers=[\\n (\\'num\\', numerical_transformer, numerical_cols),\\n (\\'cat\\', categorical_transformer, categorical_cols)\\n ])\\n\\n# Define model\\nmodel = RandomForestClassifier(criterion=\\'gini\\', max_depth=5, max_features=\\'auto\\', max_leaf_nodes=50, n_estimators = 50, random_state=0)\\n\\n# Bundle preprocessing and modeling code in a pipeline\\nmy_pipeline = Pipeline(steps=[(\\'preprocessor\\', preprocessor),\\n (\\'model\\', model)\\n ])\\n\\n# Preprocessing of training data, fit model \\nmy_pipeline.fit(X, y)\\n\\n# Preprocessing of validation data, get predictions\\npreds = my_pipeline.predict(X_test)\\n\\nget_mae(my_pipeline, X, y)'"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''# Try with lowerst GINI feaature (embarked) removed\n",
"\n",
"feature = ['PassengerId', 'Pclass', 'Sex', 'Age', 'SibSp',\n",
" 'Parch', 'Fare']\n",
"\n",
"X_ = train_data[feature]\n",
"X_test_ = test_data[feature]\n",
"\n",
"categorical_cols = [cname for cname in X_ if\n",
" X_[cname].dtype == \"object\"]\n",
"\n",
"numerical_cols = [cname for cname in X_ if \n",
" X_[cname].dtype in ['int64', 'float64', 'uint8']]\n",
"\n",
"cols = categorical_cols + numerical_cols\n",
"X = X_[cols].copy()\n",
"X_test = X_test_[cols].copy()\n",
"\n",
"# Preprocessing for numerical data\n",
"numerical_transformer = SimpleImputer(strategy='mean')\n",
"\n",
"# Preprocessing for categorical data\n",
"categorical_transformer = Pipeline(steps=[\n",
" ('imputer', SimpleImputer(strategy='most_frequent')),\n",
" ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
" ])\n",
"#categorical_transformer = SimpleImputer(strategy='most_frequent')\n",
"\n",
"# Bundle preprocessing for numerical and categorical data\n",
"preprocessor = ColumnTransformer(\n",
"transformers=[\n",
" ('num', numerical_transformer, numerical_cols),\n",
" ('cat', categorical_transformer, categorical_cols)\n",
" ])\n",
"\n",
"# Define model\n",
"model = RandomForestClassifier(criterion='gini', max_depth=5, max_features='auto', max_leaf_nodes=50, n_estimators = 50, random_state=0)\n",
"\n",
"# Bundle preprocessing and modeling code in a pipeline\n",
"my_pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
" ('model', model)\n",
" ])\n",
"\n",
"# Preprocessing of training data, fit model \n",
"my_pipeline.fit(X, y)\n",
"\n",
"# Preprocessing of validation data, get predictions\n",
"preds = my_pipeline.predict(X_test)\n",
"\n",
"get_mae(my_pipeline, X, y)'''"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your submission was successfully saved!\n"
]
}
],
"source": [
"# Submit using optimum features\n",
"\n",
"############################################################################\n",
"# Save predictions\n",
"output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': preds})\n",
"output.to_csv('my_submission.csv', index=False)\n",
"print(\"Your submission was successfully saved!\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| 0047/230/47230815.ipynb | s3://data-agents/kaggle-outputs/sharded/010_00047.jsonl.gz |
{"cells":[{"metadata":{},"cell_type":"markdown","source":"## Importing necessary libraries"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom nltk.corpus import stopwords \nfrom nltk.tokenize import word_tokenize \nfrom nltk.stem import WordNetLemmatizer ","execution_count":1,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"wine_df=pd.read_csv('../input/wine-reviews/winemag-data-130k-v2.csv')\nwine_df.drop(columns= ['Unnamed: 0']); # Dropping extra column with non important imformation","execution_count":2,"outputs":[{"output_type":"execute_result","execution_count":2,"data":{"text/plain":" country description \\\n0 Italy Aromas include tropical fruit, broom, brimston... \n1 Portugal This is ripe and fruity, a wine that is smooth... \n2 US Tart and snappy, the flavors of lime flesh and... \n3 US Pineapple rind, lemon pith and orange blossom ... \n4 US Much like the regular bottling from 2012, this... \n... ... ... \n129966 Germany Notes of honeysuckle and cantaloupe sweeten th... \n129967 US Citation is given as much as a decade of bottl... \n129968 France Well-drained gravel soil gives this wine its c... \n129969 France A dry style of Pinot Gris, this is crisp with ... \n129970 France Big, rich and off-dry, this is powered by inte... \n\n designation points price \\\n0 Vulkà Bianco 87 NaN \n1 Avidagos 87 15.0 \n2 NaN 87 14.0 \n3 Reserve Late Harvest 87 13.0 \n4 Vintner's Reserve Wild Child Block 87 65.0 \n... ... ... ... \n129966 Brauneberger Juffer-Sonnenuhr Spätlese 90 28.0 \n129967 NaN 90 75.0 \n129968 Kritt 90 30.0 \n129969 NaN 90 32.0 \n129970 Lieu-dit Harth Cuvée Caroline 90 21.0 \n\n province region_1 region_2 \\\n0 Sicily & Sardinia Etna NaN \n1 Douro NaN NaN \n2 Oregon Willamette Valley Willamette Valley \n3 Michigan Lake Michigan Shore NaN \n4 Oregon Willamette Valley Willamette Valley \n... ... ... ... \n129966 Mosel NaN NaN \n129967 Oregon Oregon Oregon Other \n129968 Alsace Alsace NaN \n129969 Alsace Alsace NaN \n129970 Alsace Alsace NaN \n\n taster_name taster_twitter_handle \\\n0 Kerin O’Keefe @kerinokeefe \n1 Roger Voss @vossroger \n2 Paul Gregutt @paulgwine \n3 Alexander Peartree NaN \n4 Paul Gregutt @paulgwine \n... ... ... \n129966 Anna Lee C. Iijima NaN \n129967 Paul Gregutt @paulgwine \n129968 Roger Voss @vossroger \n129969 Roger Voss @vossroger \n129970 Roger Voss @vossroger \n\n title variety \\\n0 Nicosia 2013 Vulkà Bianco (Etna) White Blend \n1 Quinta dos Avidagos 2011 Avidagos Red (Douro) Portuguese Red \n2 Rainstorm 2013 Pinot Gris (Willamette Valley) Pinot Gris \n3 St. Julian 2013 Reserve Late Harvest Riesling ... Riesling \n4 Sweet Cheeks 2012 Vintner's Reserve Wild Child... Pinot Noir \n... ... ... \n129966 Dr. H. Thanisch (Erben Müller-Burggraef) 2013 ... Riesling \n129967 Citation 2004 Pinot Noir (Oregon) Pinot Noir \n129968 Domaine Gresser 2013 Kritt Gewurztraminer (Als... Gewürztraminer \n129969 Domaine Marcel Deiss 2012 Pinot Gris (Alsace) Pinot Gris \n129970 Domaine Schoffit 2012 Lieu-dit Harth Cuvée Car... Gewürztraminer \n\n winery \n0 Nicosia \n1 Quinta dos Avidagos \n2 Rainstorm \n3 St. Julian \n4 Sweet Cheeks \n... ... \n129966 Dr. H. Thanisch (Erben Müller-Burggraef) \n129967 Citation \n129968 Domaine Gresser \n129969 Domaine Marcel Deiss \n129970 Domaine Schoffit \n\n[129971 rows x 13 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>country</th>\n <th>description</th>\n <th>designation</th>\n <th>points</th>\n <th>price</th>\n <th>province</th>\n <th>region_1</th>\n <th>region_2</th>\n <th>taster_name</th>\n <th>taster_twitter_handle</th>\n <th>title</th>\n <th>variety</th>\n <th>winery</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Italy</td>\n <td>Aromas include tropical fruit, broom, brimston...</td>\n <td>Vulkà Bianco</td>\n <td>87</td>\n <td>NaN</td>\n <td>Sicily & Sardinia</td>\n <td>Etna</td>\n <td>NaN</td>\n <td>Kerin O’Keefe</td>\n <td>@kerinokeefe</td>\n <td>Nicosia 2013 Vulkà Bianco (Etna)</td>\n <td>White Blend</td>\n <td>Nicosia</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Portugal</td>\n <td>This is ripe and fruity, a wine that is smooth...</td>\n <td>Avidagos</td>\n <td>87</td>\n <td>15.0</td>\n <td>Douro</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Roger Voss</td>\n <td>@vossroger</td>\n <td>Quinta dos Avidagos 2011 Avidagos Red (Douro)</td>\n <td>Portuguese Red</td>\n <td>Quinta dos Avidagos</td>\n </tr>\n <tr>\n <th>2</th>\n <td>US</td>\n <td>Tart and snappy, the flavors of lime flesh and...</td>\n <td>NaN</td>\n <td>87</td>\n <td>14.0</td>\n <td>Oregon</td>\n <td>Willamette Valley</td>\n <td>Willamette Valley</td>\n <td>Paul Gregutt</td>\n <td>@paulgwine</td>\n <td>Rainstorm 2013 Pinot Gris (Willamette Valley)</td>\n <td>Pinot Gris</td>\n <td>Rainstorm</td>\n </tr>\n <tr>\n <th>3</th>\n <td>US</td>\n <td>Pineapple rind, lemon pith and orange blossom ...</td>\n <td>Reserve Late Harvest</td>\n <td>87</td>\n <td>13.0</td>\n <td>Michigan</td>\n <td>Lake Michigan Shore</td>\n <td>NaN</td>\n <td>Alexander Peartree</td>\n <td>NaN</td>\n <td>St. Julian 2013 Reserve Late Harvest Riesling ...</td>\n <td>Riesling</td>\n <td>St. Julian</td>\n </tr>\n <tr>\n <th>4</th>\n <td>US</td>\n <td>Much like the regular bottling from 2012, this...</td>\n <td>Vintner's Reserve Wild Child Block</td>\n <td>87</td>\n <td>65.0</td>\n <td>Oregon</td>\n <td>Willamette Valley</td>\n <td>Willamette Valley</td>\n <td>Paul Gregutt</td>\n <td>@paulgwine</td>\n <td>Sweet Cheeks 2012 Vintner's Reserve Wild Child...</td>\n <td>Pinot Noir</td>\n <td>Sweet Cheeks</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>129966</th>\n <td>Germany</td>\n <td>Notes of honeysuckle and cantaloupe sweeten th...</td>\n <td>Brauneberger Juffer-Sonnenuhr Spätlese</td>\n <td>90</td>\n <td>28.0</td>\n <td>Mosel</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Anna Lee C. Iijima</td>\n <td>NaN</td>\n <td>Dr. H. Thanisch (Erben Müller-Burggraef) 2013 ...</td>\n <td>Riesling</td>\n <td>Dr. H. Thanisch (Erben Müller-Burggraef)</td>\n </tr>\n <tr>\n <th>129967</th>\n <td>US</td>\n <td>Citation is given as much as a decade of bottl...</td>\n <td>NaN</td>\n <td>90</td>\n <td>75.0</td>\n <td>Oregon</td>\n <td>Oregon</td>\n <td>Oregon Other</td>\n <td>Paul Gregutt</td>\n <td>@paulgwine</td>\n <td>Citation 2004 Pinot Noir (Oregon)</td>\n <td>Pinot Noir</td>\n <td>Citation</td>\n </tr>\n <tr>\n <th>129968</th>\n <td>France</td>\n <td>Well-drained gravel soil gives this wine its c...</td>\n <td>Kritt</td>\n <td>90</td>\n <td>30.0</td>\n <td>Alsace</td>\n <td>Alsace</td>\n <td>NaN</td>\n <td>Roger Voss</td>\n <td>@vossroger</td>\n <td>Domaine Gresser 2013 Kritt Gewurztraminer (Als...</td>\n <td>Gewürztraminer</td>\n <td>Domaine Gresser</td>\n </tr>\n <tr>\n <th>129969</th>\n <td>France</td>\n <td>A dry style of Pinot Gris, this is crisp with ...</td>\n <td>NaN</td>\n <td>90</td>\n <td>32.0</td>\n <td>Alsace</td>\n <td>Alsace</td>\n <td>NaN</td>\n <td>Roger Voss</td>\n <td>@vossroger</td>\n <td>Domaine Marcel Deiss 2012 Pinot Gris (Alsace)</td>\n <td>Pinot Gris</td>\n <td>Domaine Marcel Deiss</td>\n </tr>\n <tr>\n <th>129970</th>\n <td>France</td>\n <td>Big, rich and off-dry, this is powered by inte...</td>\n <td>Lieu-dit Harth Cuvée Caroline</td>\n <td>90</td>\n <td>21.0</td>\n <td>Alsace</td>\n <td>Alsace</td>\n <td>NaN</td>\n <td>Roger Voss</td>\n <td>@vossroger</td>\n <td>Domaine Schoffit 2012 Lieu-dit Harth Cuvée Car...</td>\n <td>Gewürztraminer</td>\n <td>Domaine Schoffit</td>\n </tr>\n </tbody>\n</table>\n<p>129971 rows × 13 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Top 5 countries that produce most number of wines"},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(8,8))\nsns.countplot(wine_df['country'],order=wine_df.country.value_counts().iloc[:5].index)\nplt.xticks(rotation=90)\nplt.xlabel('Country')\nplt.ylabel('Number of wines produced')\nplt.title(\"Top 5 countries with largest number of wines produced\")\nplt.show()\n","execution_count":3,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x576 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"## U.S.A. makes more number of wines than all the next top 4 producers"},{"metadata":{},"cell_type":"markdown","source":"## Let us find out which of all the countries make best wines based on the point "},{"metadata":{"trusted":true},"cell_type":"code","source":"# Making a function to get the average of the points of wines of a particular country\navg_country_score=pd.DataFrame(columns=['country','points'])\ndef points():\n score=[]\n for i in set(wine_df.country):\n score.append(wine_df[wine_df['country']==i]['points'].mean())\n return score\nscore=points()\navg_country_score['country']=list(set(wine_df.country))\navg_country_score['points']=score\navg_country_score.dropna(inplace=True)\navg_country_score=avg_country_score.sort_values('points',ascending=False)\navg_country_score.head()","execution_count":4,"outputs":[{"output_type":"execute_result","execution_count":4,"data":{"text/plain":" country points\n11 England 91.581081\n16 India 90.222222\n38 Austria 90.101345\n31 Germany 89.851732\n3 Canada 89.369650","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>country</th>\n <th>points</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>11</th>\n <td>England</td>\n <td>91.581081</td>\n </tr>\n <tr>\n <th>16</th>\n <td>India</td>\n <td>90.222222</td>\n </tr>\n <tr>\n <th>38</th>\n <td>Austria</td>\n <td>90.101345</td>\n </tr>\n <tr>\n <th>31</th>\n <td>Germany</td>\n <td>89.851732</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Canada</td>\n <td>89.369650</td>\n </tr>\n </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(8,8))\nsns.barplot(data=avg_country_score.head(),x='country',y='points')\nplt.xticks(rotation=90)\nplt.xlabel(\"Country\")\nplt.ylabel(\"Average of points for all the wines\")\nplt.title(\"Top 5 countries with best testing wines\")\nplt.show()","execution_count":5,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x576 with 1 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qs4EXtBlUZIkaXbDBPe9q+pnM9bd0kUxkiRpsGGC+8okmwEFkOT5wKWdViVJkmY17+A04NXAIcCWSf4AXAC8uNOqJEnSrIYZVX4+8JQkqwLLVdX13ZclSZJmM29wJ1mJ5vSvjWkmYwGgqt7daWWSJOkuhukqPxa4DjgVuLHbciRJ0iDDBPcGVbVz55VIkqR5DTOq/MdJHtZ5JZIkaV7DtLifAOyb5AKarvIAVVVel1uSpAU2THA/o/MqJEnSUOYM7iRrtNfh9vQvSZKWEYNa3F8EdqEZTV40XeRTCti0w7okSdIs5gzuqtqlvd9k4cqRJEmDDHNZz88n2T/JlgtRkCRJmtswp4N9DlgP+FiS3yb5apIDui1LkiTNZpi5yr+fZDGwLfAk4JXAQ4F/67g2SZI0wzBzlZ8IrAr8BPghsG1VXd51YZIk6a6G6So/E7gJ2Ap4OLBVklU6rUqSJM1qmK7y1wMkWQ14KfBZ4P7ASt2WJkmSZhqmq/w1wN8AjwZ+BxxG02U+ryT3AT5D01ov4GXAb4CjaS4TeiGwZ1Vds+SlS5J0zzPMlKerAB8GTq2qW5Zw+/8GfKeqnp/kXsC9gYOAE6vqfUkOBA4E3rKE25Uk6R5pmK7yD9ydDSdZA9gB2Lfdzk3ATUl2B3ZsX3Y4cDIGtyRJQxlmcNrdtSlwBfDZJL9I8pkkqwLrVtWlAO39/TqsQZKkidJlcK8AbA38R1U9CriBplt8KElenuSUJKdcccUVXdUoSVKvDBXcSTZK8pT28SpJVh/ibRcDF1fVT9vlr9AE+WVJ1mu3tR4w6znhVXVIVW1TVdssWrRomDIlSZp4w8xVvj9N6H6qXbUB8I353ldVfwR+n2SLdtVOwK+B44B92nX7AMcuYc2SJN1jDTOq/NXAY4CfAlTVuUmGPS79WuDIdkT5+TTngS8HfDnJfsBFwB5LXLUkSfdQwwT3jVV1U9JcjjvJCjTnZM+rqk4HtpnlqZ2GrlCSJN1umGPci5McBKyS5KnAMcDx3ZYlSZJmM0xwH0hzWtdZwCuAbwFv67IoSZI0u2EmYLkN+HR7kyRJYzTMXOXbAwcDG7WvD1BVtWm3pUmSpJmGGZx2KPB64FTg1m7LkSRJgwwT3NdV1bc7r0SSJM1rzuBOsnX78KQkHwC+Btw49XxVndZxbZIkaYZBLe4PzViefj52AU8efTmSJGmQOYO7qp4EkGTTqjp/+nNJHJgmSdIYDHMe91dmWXfMqAuRJEnzG3SMe0vgocCaSZ477ak1gJW7LkySJN3VoGPcWwC7APcBdp22/npg/y6LkiRJsxt0jPtY4Ngkj6uqnyxgTZIkaQ7zHuM2tCVJWnYMMzhNkiQtIwxuSZJ6ZNCo8n8c9Maq+vDoy5EkSYMMGlW++oJVIUmShjJoVPm7FrIQSZI0v0Fd5R8d9Maqet3oy5EkSYMM6io/dcGqkCRJQxnUVX74QhYiSZLmN6jFDUCSRcBbgIcwbY7yqvKynpIkLbBhzuM+Ejgb2AR4F3Ah8PMOa5IkSXMYJrjvW1WHAjdX1eKqehmwXcd1SZKkWczbVQ7c3N5fmuRZwCXABt2VJEmS5jJMcL8nyZrAG4CP0VyP+/WdViVJkmY1b3BX1Tfbh9cBT+q2HEmSNIgXGZEkqUcMbkmSemRgcCdZLsmeC1WMJEkabGBwV9VtwGsWqBZJkjSPYbrKT0jyxiQbJll76tZ5ZZIk6S6GOR3sZe39q6etK2DT0ZcjSZIGGeZ0sE0WohBJkjS/YS4ysiLw98AO7aqTgU9V1c1zvkmSJHVimK7y/wBWBP69XX5Ju+7vuipKkiTNbpjg3raqHjFt+ftJzuiqIEmSNLdhRpXfmmSzqYUkmwK3dleSJEmayzAt7jcBJyU5HwiwEfDSTquSJEmzmjO4k+xRVccA5wObA1vQBPc5VXXjAtUnSZKmGdRV/tb2/qtVdWNVnVlVZxjakiSNz6Cu8quSnARskuS4mU9W1W7dlSVJkmYzKLifBWwNfAH40MKUI0mSBpkzuKvqJuD/Jnl8VV2xgDVJkqQ5zHs6mKEtSdKyY5jzuCVJ0jLC4JYkqUfmDe4k70+yRpIVk5yY5MokL16I4iRJ0p0N0+J+WlX9CdgFuBh4MM1sapIkaYENE9wrtvfPBI6qqqs7rEeSJA0wzFzlxyc5B/gL8Koki4C/dluWJEmazTAt7ncCjwO2qaqbgT8DzpomSdIYDBPcP6mqa6rqVoCqugH4drdlSZKk2Qy6Otj9gQcAqyR5FM2VwQDWAO69ALVJkqQZBh3jfjqwL7AB8OFp668HDuqwJkmSNIdBc5UfDhye5HlV9dUFrEmSJM1hmFHl30zyImDj6a+vqnd3VZQkSZrdMMF9LHAdcCpwY7flSJKkQYYJ7g2qaufOK5EkSfMa5nSwHyd5WOeVSJKkeQ3T4n4CsG+SC2i6ygNUVT2808okSdJdDBPcz+i8CkmSNJRBE7Cs0V4V7PoFrEeSJA0wqMX9RZpLeZ4KFHfMnEa7vGmHdUmSpFkMmoBll/Z+k4UrR5IkDTLMMW6S7Abs0C6eXFXf7K4kSZI0l3lPB0vyPuAA4Nft7YAk7+26MEmSdFfDtLifCTyyqm4DSHI48AvgrV0WJkmS7mqYCVgA7jPt8ZpdFCJJkuY3TIv7vcAvkpxEM7J8B2xtS5I0FvMGd1UdleRkYNt21Vuq6o+dViVJkmY11Khy4HE0U58WsDzw9c4qkiRJcxpmVPm/A68EzgJ+CbwiySe6LkySJN3VMC3uJwJbVVXB7aPKz+q0KkmSNKthRpX/BnjgtOUNgTO7KUeSJA0yTIv7vsDZSX7WLm8L/CTJcQBVtVtXxUmSpDsbJrjf0XkVkiRpKMOcDrZ4IQqRJEnzG3bmNEmStAwwuCVJ6pE5gzvJie39vyzNByRZPskvknyzXV47yQlJzm3v11qa7UuSdE8yqMW9XpInArsleVSSraffluAzDgDOnrZ8IHBiVW0OnNguS5KkIQwanPYOmlDdAPjwjOcKePJ8G0+yAfAs4P8A/9iu3h3YsX18OHAy8JZhC5Yk6Z5szuCuqq8AX0ny9qr6p7u5/Y8AbwZWn7Zu3aq6tP2MS5Pc725uW5Kke5xhTgf7pyS70VzOE+DkqvrmfO9LsgtweVWdmmTHJS0sycuBlwM88IEPnOfVkiTdMwxzkZH30hyn/nV7O6BdN5/taY6PXwh8CXhykiOAy5Ks1257PeDy2d5cVYdU1TZVtc2iRYuG+jKSJE26YU4Hexbw1Ko6rKoOA3Zu1w1UVW+tqg2qamPgBcD3q+rFwHHAPu3L9gGOvVuVS5J0DzTsedz3mfZ4zaX8zPcBT01yLvDUdlmSJA1hmLnK3wv8IslJQGiOdb91ST6kqk6mGT1OVV0F7LREVUqSJGC4wWlHJTmZ5qpgAd5SVX/sujBJknRXw7S4aU/fOq7jWiRJ0jycq1ySpB4xuCVJ6pGhgjvJE5K8tH28KMkm3ZYlSZJmM8wELO+kmUt8aiT5isARXRYlSZJmN0yL+znAbsANAFV1CXeee1ySJC2QYYL7pqoqmiuCkWTVbkuSJElzGSa4v5zkU8B9kuwP/Bfw6W7LkiRJsxlmApYPJnkq8CdgC+AdVXVC55VJkqS7GHYClhMAw1qSpDGbN7iTXE97fHua64BTgDdU1fldFCZJku5qmBb3h4FLgC/SzFX+AuD+wG+Aw4AduypOkiTd2TCD03auqk9V1fVV9aeqOgR4ZlUdDazVcX2SJGmaYYL7tiR7Jlmuve057bmZXeiSJKlDwwT33sBLgMuBy9rHL06yCvCaDmuTJEkzDHM62PnArnM8/aPRliNJkgYZZlT5ysB+wEOBlafWV9XLOqxLkiTNYpiu8i/QjCJ/OrAY2AC4vsuiJEnS7IYJ7gdV1duBG6rqcOBZwMO6LUuSJM1mmOC+ub2/NslWwJrAxp1VJEmS5jTMBCyHJFkLeBtwHLAa8PZOq5IkSbMaGNxJlgP+VFXXAD8ANl2QqiRJ0qwGdpVX1W14rrYkScuMYY5xn5DkjUk2TLL21K3zyiRJ0l0Mc4x76nztV09bV9htLknSghtm5rRNFqIQSZI0v3m7ypPcO8nbkhzSLm+eZJfuS5MkSTMNc4z7s8BNwOPb5YuB93RWkSRJmtMwwb1ZVb2fdiKWqvoLkE6rkiRJsxomuG9qL+FZAEk2A27stCpJkjSrYUaVHwx8B9gwyZHA9sC+HdYkSZLmMMyo8u8lORXYjqaL/ICqurLzyiRJ0l0Mcz3u44CjgOOq6obuS5IkSXMZ5hj3h4C/AX6d5Jgkz0+ycsd1SZKkWQzTVb4YWJxkeeDJwP7AYcAaHdcmSZJmGGZwGu2o8l2BvYCtgcO7LEqSJM1umGPcRwOPpRlZ/gng5PaqYZIkaYEN0+L+LPCiqroVIMn2SV5UVa+e532SJGnEhjnG/Z0kj0zyQpqu8guAr3VemSRJuos5gzvJg4EXAC8ErgKOBlJVT1qg2iRJ0gyDWtznAD8Edq2q8wCSvH5BqpIkSbMadB7384A/Aicl+XSSnfDiIpIkjdWcwV1VX6+qvYAtgZOB1wPrJvmPJE9boPokSdI0886cVlU3VNWRVbULsAFwOnBg55VJkqS7GGbK09tV1dVV9amqenJXBUmSpLktUXBLkqTxMrglSeoRg1uSpB4xuCVJ6hGDW5KkHjG4JUnqEYNbkqQeMbglSeoRg1uSpB4xuCVJ6hGDW5KkHjG4JUnqEYNbkqQeMbglSeoRg1uSpB4xuCVJ6hGDW5KkHjG4JUnqEYNbkqQeMbglSeoRg1uSpB4xuCVJ6hGDW5KkHjG4JUnqEYNbkqQeMbglSeoRg1uSpB4xuCVJ6hGDW5KkHjG4JUnqEYNbkqQeMbglSeoRg1uSpB4xuCVJ6hGDW5KkHjG4JUnqkc6CO8mGSU5KcnaSXyU5oF2/dpITkpzb3q/VVQ2SJE2aLlvctwBvqKr/BWwHvDrJQ4ADgROranPgxHZZkiQNobPgrqpLq+q09vH1wNnAA4DdgcPblx0OPLurGiRJmjQLcow7ycbAo4CfAutW1aXQhDtwvzne8/IkpyQ55YorrliIMiVJWuZ1HtxJVgO+CvxDVf1p2PdV1SFVtU1VbbNo0aLuCpQkqUc6De4kK9KE9pFV9bV29WVJ1mufXw+4vMsaJEmaJF2OKg9wKHB2VX142lPHAfu0j/cBju2qBkmSJs0KHW57e+AlwFlJTm/XHQS8D/hykv2Ai4A9OqxBkqSJ0llwV9WPgMzx9E5dfa4kSZPMmdMkSeoRg1uSpB4xuCVJ6hGDW5KkHjG4JUnqEYNbkqQeMbglSeoRg1uSpB4xuCVJ6hGDW5KkHjG4JUnqEYNbkqQeMbglSeoRg1uSpB4xuCVJ6hGDW5KkHjG4JUnqEYNbkqQeMbglSeoRg1uSpB4xuCVJ6hGDW5KkHjG4JUnqEYNbkqQeMbglSeoRg1uSpB4xuCVJ6hGDW5KkHjG4JUnqEYNbkqQeMbglSeoRg1uSpB4xuCVJ6hGDW5KkHjG4JUnqEYNbkqQeMbglSeoRg1uSpB4xuCVJ6hGDW5KkHjG4JUnqEYNbkqQeMbglSeoRg1uSpB4xuCVJ6hGDW5KkHjG4JUnqEYNbkqQeMbglSeoRg1uSpB4xuCVJ6hGDW5KkHjG4JUnqEYNbkqQeMbglSeoRg1uSpB4xuCVJ6hGDW5KkHjG4JUnqEYNbkqQeMbglSeoRg1uSpB4xuCVJ6hGDW5KkHjG4JUnqEYNbkqQeMbglSeoRg1uSpB4xuCVJ6hGDW5KkHjG4JUnqEYNbkqQeMbglSeoRg1uSpB4xuCVJ6hGDW5KkHjG4JUnqEYNbkqQeMbglSeoRg1uSpB4xuCVJ6pGxBHeSnZP8Jsl5SQ4cRw2SJPXRggd3kuWBTwDPAB4CvDDJQxa6DkmS+mgcLe7HAOdV1flVdRPwJWD3MdQhSVLvjCO4HwD8ftryxe06SZI0j1TVwn5gsgfw9Kr6u3b5JcBjquq1M173cuDl7eIWwG8WtNCltw5w5biLmHDu4+65j7vnPl4YfdvPG1XVotmeWGGhK6FpYW84bXkD4JKZL6qqQ4BDFqqoUUtySlVtM+46Jpn7uHvu4+65jxfGJO3ncXSV/xzYPHPPCegAAAwnSURBVMkmSe4FvAA4bgx1SJLUOwve4q6qW5K8BvgusDxwWFX9aqHrkCSpj8bRVU5VfQv41jg+ewH1tpu/R9zH3XMfd899vDAmZj8v+OA0SZJ09znlqSRJPWJwS5LUIwa3JEk9YnBL0gJL8poka427jnuCJPdL8sCp27jrGYWxjCqfNEnOAuYc5VdVD1/AciZakkXAW2guULPy1PqqevLYipogSVYG9gMeyp3378vGVtRkuj/w8ySnAYcB3y1HCo9Ukt2ADwHrA5cDGwFn0/xs95ot7tHYBdgV+E5727u9fQv4yhjrmkRH0vzn2wR4F3AhzaQ+Go0v0ITK04HFNDMbXj/WiiZQVb0N2Bw4FNgXODfJPyfZbKyFTZZ/ArYD/l9VbQLsBPz3eEsaDYN7BKrqd1X1O2D7qnpzVZ3V3g6k+QWo0blvVR0K3FxVi9uW4HbjLmqCPKiq3g7cUFWHA88CHjbmmiZS28L+Y3u7BVgL+EqS94+1sMlxc1VdBSyXZLmqOgl45LiLGgW7ykdr1SRPqKofASR5PLDqmGuaNDe395cmeRbNPPcbjLGeSTO1f69NshVNqGw8vnImU5LXAfvQXPTiM8CbqurmJMsB5wJvHmd9E+LaJKsBPwCOTHI5zR9IvWdwj9Z+wGFJ1myXrwU8Njha72n37xuAjwFrAK8fb0kT5ZB20NTbaa4hsBrwjvGWNJHWAZ7b9tTdrqpuS7LLmGqaNLsDf6X5/bA3sCbw7rFWNCLOnNaBJGvQ7Nvrxl2LpGVTkuWBdZnWgKqqi8ZXkfrCFvcIJVkJeB5N1+IKSQCoqon4K2+ckry5qt6f5GPMMoK/ql43hrImRpIXV9URSf5xtuer6sMLXdMkay+0dDBwGXBbu7oAz0BZSkmuZ/BZPmssYDmdMLhH61jgOuBU4MYx1zJpzm7vTxlrFZNraizG6mOt4p7jH4At2sFTGqGqWh0gybtpxmh8AQhNd/lE/HzbVT5CSX5ZVVuNuw7p7mi7bl9XVf867lomXZKTgKdW1UQMlloWJflpVT12vnV9ZIt7tH6c5GFVdda4C5k0SY5ncPfXbgtYzkSqqlvbSSsM7u6dD5yc5D+Z1jvnIYmRujXJ3sCXaH53vBC4dbwljYbBPVpPAPZNcgHNf8bQnK7pcaul98H2/rk0E4Qc0S6/kGYSFo3Gj5N8HDgauGFqZVWdNr6SJtJF7e1e7U2j9yLg39pb0Uy+8qKxVjQidpWPUJKNZls/85QP3X1JflBVO8y3TndP24U7UzmlrLTssMU9QlMBneR+TJvnWSO1KMmmVXU+QJJNgEVjrmmS7De1b6ck2XRcxUyqds79N3PXOeH9A2lEJnnefac8HaEkuyU5F7iAZp7nC4Fvj7WoyfN6mmODJyc5GTiJZoSuRmO2ufWPWfAqJt+RwDk4536XJnbefVvcozU1qf1/VdWjkjyJ5hisRqSqvpNkc2DLdtU5VeWpd0spyZY0LZM1kzx32lNrYO9RF+5bVYcmOaCqFgOLkywed1ET5kFVtUeS3avq8CRfBL477qJGweAerZur6qokt09qn+Rfxl3UBHo07SQ3wCOSUFWfH29JvbcFzVXu7kNzpbsp1wP7j6Wiyeac+92b2Hn3De7RmthJ7ZcVSb4AbAaczh2ndhRgcC+FqjoWODbJ46rqJ+Ou5x7AOfe7N7Hz7juqfISSrEozqf3ULD1rAkc6O9LoJDkbeEj5g9uJ9pKS7wH+QnNt+UcA/1BVRwx8o6QFY3CrV5IcQzO716XjrmUSJTm9qh6Z5DnAs2lagSdV1SPGXNpEac+GeC13HPIBnEholGZeO2Jq/SRcO8Ku8hEYMKn91AQsvZ/UfhmyDvDrJD/jzjNO+QtvNFZs758JHFVVV09dLEcj9Q3gUOB47rjIiEZrYq8dYXCPwNSk9loQB4+7gAl3fJJzaLrKX9Web/zXMdc0if5aVR8ddxETboOq2nncRXTBrvIRSrL2LKuvr6qbZ1kvLZPaAT1/aucuvzewRlX9cdx1TZIkLwI2B77HnXuOnFp2RJIcAnxsEq8dYXCPUJILgQ2Ba2i6ye8DXApcDuxfVaeOr7p+83DEwkjyt7Ot93S70UryXuAlwG+Zdj1uZ04bnSS/Bh5EMyHWRF07wq7y0foO8PWq+i5AkqcBOwNfBv4d6P3l5MbFwxELZttpj1cGdgJOw9PtRu05wKZVddO4C5lgzxh3AV2xxT1CSU6pqm1mWzc1WndctUl3R3uu8Rcc/DdaSY4GXltVl4+7lkk389oRVXXRGMsZCVvco3V1krfQXP8VYC/gmiTL48hR9dOfgQePu4gJtC5wTpKf49kRnWivLf8hYH2aw5UbAWfTTO3bawb3aL0IeCfNqR4BftSuWx7Yc4x1SUNJcjx3jCVYHvhfNId6NFrvHHcB9wATe+0Iu8ol3S7JE6ct3kLzB+gLq+rVYypp4iRZDjizqrYady2TbNphyjOAR1XVbUl+VlWPGXdtS8sW9wgleTDwRu46U48jRdULVbU4ySNpeor2pBmR+9XxVjVZ2gA5I8kDJ+F46zJsYq8dYYt7hNq/7D5JM1PP1AUw8DQwLevaPzpfQNOVeBVwNPDGqtporIVNqCTfpxnB/zPghqn1HuNeekkeRDOG4HSaiYSWo7l2xEbAf07C72ODe4SSnFpVjx53HdKSSnIb8ENgv6o6r113flVtOt7KJtOMQxK3a6/NraWQ5JvAQVV15oz12wDvrKpdZ39nfyw37gImzPFJXpVkvSRrT93GXZQ0hOfRXK/4pCSfTrITzfFtdaAN6AuBFdvHP6c5X15Lb+OZoQ1QVacwIdfjtsU9QkkumGV12WpRX7SXpn02TZf5k4HDaSYV+t5YC5swSfYHXg6sXVWbJdkc+GRV7TTm0novyXlV9aAlfa5PDG5Js2p7i/YA9nKA5WglOR14DPDTqnpUu+6sqnrYeCvrvyRHAd+vqk/PWL8f8LSq2ms8lY2OwT0CSd5cVe9vH+9RVcdMe+6fq+qg8VUnaVmT5KdV9dgkv2jPMV4BOG0S5tEetyTrAl8HbqIZKAywDXAv4DmTcMEcg3sEkpxWVVvPfDzbsiQleT9wLfC3wGuBVwG/rqr/PdbCJkg74crUufK/qqrvj7OeUTK4R2Dqr+aZj2dblqR2Epb9gKe1q75bVZ8ZY0nqEUeVj0bN8Xi2ZUn3UEl2T/LqqrqtPQa7EU037kFJnj/m8tQTtrhHIMmtNJMoBFiF5sIMtMsrV9WK46pN0rIjyX8DL6iq37fLp9OM3l8N+KyjyjUMpzwdgapaftw1SOqFe02FdutHVXU1zZUFVx1XUeoXu8olaeGsNX2hql4zbXHRAteinjK4JWnh/LSdfOVOkryCZt5yaV4e45akBZLkfsA3gBu5Y4rTRwMrAc+uqsvGVZv6w+CWpAWW5MnAQ9vFiTrHWN0zuCVJ6hGPcUuS1CMGtyRJPWJwSxMoyf2TfCnJb5P8Osm3kjx4hNvfMcnjR7U9ScMzuKUJkyQ0V0c6uao2q6qHAAcB647wY3YEZg3u9kpXkjpicEuT50nAzVX1yakVVXU68KMkH0jyyyRnJdkLbm89f3PqtUk+nmTf9vGFSd6V5LT2PVsm2Rh4JfD6JKcn+Zskn0vy4SQnAR9Icm6SRe02lktyXpJ1FmoHSJPMv4ylybMVd1yHeLrnAo8EHgGsA/w8yQ+G2N6VVbV1klcBb6yqv0vySeB/quqDAEn2Ax4MPKWqbk1yLbA38BHgKcAZVXXlUn8zSba4pXuQJwBHVdWt7UQfi4Fth3jf19r7U4GNB7zumKq6tX18GM21pgFeBnx2ycuVNBuDW5o8v6KZjWumzPH6W7jz74KVZzx/Y3t/K4N76W6YetBeSOOydqKRxwLfHlSwpOEZ3NLk+T6w0vQ5sZNsC1wD7JVk+fb48w4082P/DnhIkpWSrAkMc2nJ64HV53nNZ4AjgC9Pa4lLWkoGtzRhqpkO8TnAU9vTwX4FHAx8ETgTOIMm3N9cVX9sW8dfbp87EvjFEB9zPPCcqcFpc7zmONrrTC/N95F0Z055KqkTSbYB/rWq5gp2SXeDo8oljVySA4G/pxlZLmmEbHFLktQjHuOWJKlHDG5JknrE4JYkqUcMbkmSesTgliSpRwxuSZJ65P8Dla+U5wSqcmgAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"## On average best testing wines can be found in England followed by India and austria"},{"metadata":{},"cell_type":"markdown","source":"## Preprocessing\n\nThe process of converting data to something a computer can understand is referred to as pre-processing. One of the major forms of pre-processing is to filter out useless data. In natural language processing, useless words (data), are referred to as stop words.\n### Stop-words\nStop Words: A stop word is a commonly used word (such as “the”, “a”, “an”, “in”) that a search engine has been programmed to ignore, both when indexing entries for searching and when retrieving them as the result of a search query.\n\nWe would not want these words to take up space in our database, or taking up valuable processing time. For this, we can remove them easily, by storing a list of words that you consider to stop words. NLTK(Natural Language Toolkit) in python has a list of stopwords stored in 16 different languages.\n\n### Tokenizing \nTokenization is the process by which big quantity of text is divided into smaller parts called tokens.\n These tokens are very useful for finding such patterns as well as is considered as a base step for stemming and lemmatization.\n \n### Lemmatize\nLemmatization is the process of grouping together the different inflected forms of a word so they can be analysed as a single item. Lemmatization is similar to stemming but it brings context to the words. So it links words with similar meaning to one word."},{"metadata":{"trusted":true},"cell_type":"code","source":"# fill nan with empty spaces\nwine_df['description']=wine_df['description'].fillna('')","execution_count":6,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# This function is to remove stopwords from a particular column and to tokenize it\ndef rem_stopwords_tokenize(data,name):\n \n def getting(sen):\n example_sent = sen\n\n stop_words = set(stopwords.words('english')) \n\n word_tokens = word_tokenize(example_sent) \n\n filtered_sentence = [w for w in word_tokens if not w in stop_words] \n\n filtered_sentence = [] \n\n for w in word_tokens: \n if w not in stop_words: \n filtered_sentence.append(w) \n return filtered_sentence\n x=[]\n for i in data[name].values:\n x.append(getting(i))\n data[name]=x\nrem_stopwords_tokenize(wine_df,'description')","execution_count":7,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Making a function to lemmatize all the words\nlemmatizer = WordNetLemmatizer() \ndef lemmatize_all(data,name):\n arr=data[name]\n a=[]\n for i in arr:\n b=[]\n for j in i:\n x=lemmatizer.lemmatize(j,pos='a')\n x=lemmatizer.lemmatize(x)\n b.append(x)\n a.append(b)\n data[name]=a\nlemmatize_all(wine_df,'description')","execution_count":8,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## After all the pre-processing(removing stop words, tokenizing, lammetizing) our data looks something like this"},{"metadata":{"trusted":true},"cell_type":"code","source":"wine_df.sample(5)","execution_count":9,"outputs":[{"output_type":"execute_result","execution_count":9,"data":{"text/plain":" Unnamed: 0 country \\\n51066 51066 US \n12716 12716 Spain \n129806 129806 New Zealand \n45451 45451 US \n10728 10728 US \n\n description designation \\\n51066 [Named, type, surfboard, ,, depicted, label, ,... Rhino Chaser \n12716 [Melon, peach, aroma, fresh, come, touch, vani... Parvus \n129806 [VM, 's, 2010, Cellar, Selection, SB, slightly... Cellar Selection \n45451 [This, unique, blend, Dundee, Hill, Pinot, Noi... Grand Cheval \n10728 [True, advocacy, Malbec, Napa, Valley, ,, prod... NaN \n\n points price province region_1 region_2 \\\n51066 91 42.0 California Sonoma County Sonoma \n12716 88 18.0 Catalonia Alella NaN \n129806 89 20.0 Marlborough NaN NaN \n45451 93 80.0 Oregon Oregon Oregon Other \n10728 91 60.0 California Mount Veeder Napa \n\n taster_name taster_twitter_handle \\\n51066 Virginie Boone @vboone \n12716 Michael Schachner @wineschach \n129806 Joe Czerwinski @JoeCz \n45451 Paul Gregutt @paulgwine \n10728 Virginie Boone @vboone \n\n title variety \\\n51066 Longboard 2013 Rhino Chaser Cabernet Sauvignon... Cabernet Sauvignon \n12716 Alta Alella 2014 Parvus Chardonnay (Alella) Chardonnay \n129806 Villa Maria 2010 Cellar Selection Sauvignon Bl... Sauvignon Blanc \n45451 Domaine Serene 2013 Grand Cheval Pinot Noir-Sy... Pinot Noir-Syrah \n10728 Hess Collection 2011 Cabernet Sauvignon (Mount... Cabernet Sauvignon \n\n winery \n51066 Longboard \n12716 Alta Alella \n129806 Villa Maria \n45451 Domaine Serene \n10728 Hess Collection ","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Unnamed: 0</th>\n <th>country</th>\n <th>description</th>\n <th>designation</th>\n <th>points</th>\n <th>price</th>\n <th>province</th>\n <th>region_1</th>\n <th>region_2</th>\n <th>taster_name</th>\n <th>taster_twitter_handle</th>\n <th>title</th>\n <th>variety</th>\n <th>winery</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>51066</th>\n <td>51066</td>\n <td>US</td>\n <td>[Named, type, surfboard, ,, depicted, label, ,...</td>\n <td>Rhino Chaser</td>\n <td>91</td>\n <td>42.0</td>\n <td>California</td>\n <td>Sonoma County</td>\n <td>Sonoma</td>\n <td>Virginie Boone</td>\n <td>@vboone</td>\n <td>Longboard 2013 Rhino Chaser Cabernet Sauvignon...</td>\n <td>Cabernet Sauvignon</td>\n <td>Longboard</td>\n </tr>\n <tr>\n <th>12716</th>\n <td>12716</td>\n <td>Spain</td>\n <td>[Melon, peach, aroma, fresh, come, touch, vani...</td>\n <td>Parvus</td>\n <td>88</td>\n <td>18.0</td>\n <td>Catalonia</td>\n <td>Alella</td>\n <td>NaN</td>\n <td>Michael Schachner</td>\n <td>@wineschach</td>\n <td>Alta Alella 2014 Parvus Chardonnay (Alella)</td>\n <td>Chardonnay</td>\n <td>Alta Alella</td>\n </tr>\n <tr>\n <th>129806</th>\n <td>129806</td>\n <td>New Zealand</td>\n <td>[VM, 's, 2010, Cellar, Selection, SB, slightly...</td>\n <td>Cellar Selection</td>\n <td>89</td>\n <td>20.0</td>\n <td>Marlborough</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Joe Czerwinski</td>\n <td>@JoeCz</td>\n <td>Villa Maria 2010 Cellar Selection Sauvignon Bl...</td>\n <td>Sauvignon Blanc</td>\n <td>Villa Maria</td>\n </tr>\n <tr>\n <th>45451</th>\n <td>45451</td>\n <td>US</td>\n <td>[This, unique, blend, Dundee, Hill, Pinot, Noi...</td>\n <td>Grand Cheval</td>\n <td>93</td>\n <td>80.0</td>\n <td>Oregon</td>\n <td>Oregon</td>\n <td>Oregon Other</td>\n <td>Paul Gregutt</td>\n <td>@paulgwine</td>\n <td>Domaine Serene 2013 Grand Cheval Pinot Noir-Sy...</td>\n <td>Pinot Noir-Syrah</td>\n <td>Domaine Serene</td>\n </tr>\n <tr>\n <th>10728</th>\n <td>10728</td>\n <td>US</td>\n <td>[True, advocacy, Malbec, Napa, Valley, ,, prod...</td>\n <td>NaN</td>\n <td>91</td>\n <td>60.0</td>\n <td>California</td>\n <td>Mount Veeder</td>\n <td>Napa</td>\n <td>Virginie Boone</td>\n <td>@vboone</td>\n <td>Hess Collection 2011 Cabernet Sauvignon (Mount...</td>\n <td>Cabernet Sauvignon</td>\n <td>Hess Collection</td>\n </tr>\n </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# saving this transformed dataframe to output\nwine_df.to_csv(\"preprocessed_wine_df.csv\")","execution_count":10,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Recommendation top 5 wines based on the current preferences"},{"metadata":{"trusted":true},"cell_type":"code","source":"# function for matching input from user to the best title from the wine_df\nfrom fuzzywuzzy import process\ndef get_matching_name(input_user, df):\n str2match = input_user\n str_options = df.title.to_list()\n highest = process.extractOne( str2match, str_options )\n return highest[0]","execution_count":11,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def set_rec_finder_by_popularity(na,df, number = 5):\n def intersection(lst1, lst2): \n return list(set(lst1) & set(lst2)) \n def find_rec(name):\n x=list(df[df['title']==name]['description'].values)[0]\n score=[]\n for i in range(len(df)):\n score.append([len(intersection(x,df['description'].values[i])),df['title'].values[i]])\n return score\n \n na = get_matching_name(na, df)\n print(f\"User choice matches {na} from the database \\n\")\n recommendations=find_rec(na)\n recommendations.sort(reverse=True)\n recommendations=np.array(recommendations)\n ans=recommendations[:number,1]\n ans2=[]\n for i in ans:\n ans2.append([df[df['title']==i]['points'].values[0],i])\n ans2.sort(reverse=True)\n ans2=np.array(ans2)\n ans2=ans2[:10,1]\n print(f\"Recommended top {number} wines with similar tastes are:- \")\n for i in ans2:\n print(i)","execution_count":12,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x=set_rec_finder_by_popularity(\"Vintner's Reserve Wild\",wine_df, number = 5)","execution_count":13,"outputs":[{"output_type":"stream","text":"User choice matches Sweet Cheeks 2012 Vintner's Reserve Wild Child Block Pinot Noir (Willamette Valley) from the database \n\nRecommended top 5 wines with similar tastes are:- \nKessler-Haak 2013 Four Pinot Noir (Sta. Rita Hills)\nSweet Cheeks 2012 Vintner's Reserve Wild Child Block Pinot Noir (Willamette Valley)\nCoeur d'Alene 2006 Envy Syrah (Washington)\nRicominciare 2009 Cabernet Franc-Cabernet Sauvignon (Uco Valley)\nPorta 2000 Reserve Chardonnay (Cachapoal Valley)\n","name":"stdout"}]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4} | 0047/231/47231158.ipynb | s3://data-agents/kaggle-outputs/sharded/010_00047.jsonl.gz |
{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":52,"outputs":[{"output_type":"stream","text":"/kaggle/input/home-credit-default-risk/application_test.csv\n/kaggle/input/home-credit-default-risk/installments_payments.csv\n/kaggle/input/home-credit-default-risk/application_train.csv\n/kaggle/input/home-credit-default-risk/HomeCredit_columns_description.csv\n/kaggle/input/home-credit-default-risk/POS_CASH_balance.csv\n/kaggle/input/home-credit-default-risk/sample_submission.csv\n/kaggle/input/home-credit-default-risk/bureau.csv\n/kaggle/input/home-credit-default-risk/credit_card_balance.csv\n/kaggle/input/home-credit-default-risk/previous_application.csv\n/kaggle/input/home-credit-default-risk/bureau_balance.csv\n/kaggle/input/homecreditprocessed/train_previous_processed_2.csv\n/kaggle/input/homecreditprocessed/test_bureau.csv\n/kaggle/input/homecreditprocessed/train_bureau2.csv\n/kaggle/input/homecreditprocessed/test_previous.csv\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# modeling \nimport lightgbm as lgb\n\n# utilities\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import KFold\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.preprocessing import LabelEncoder\nimport matplotlib.pyplot as plt\nplt.rcParams['font.size'] = 22\nimport seaborn as sns\n# memory management\nimport gc","execution_count":83,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"train_application = pd.read_csv('../input/home-credit-default-risk/application_train.csv')\ntrain_bureau = pd.read_csv('../input/homecreditprocessed/train_bureau2.csv')\n\n#train_previous = pd.read_csv('../input/homecreditprocessed/train_previous_processed_2.csv')\n\ntest_application_bureau = pd.read_csv('../input/homecreditprocessed/test_bureau.csv')\n#test_application_previous = pd.read_csv('../input/homecreditprocessed/test_previous.csv')","execution_count":54,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#train_application_previous = train_application.merge(train_previous, on = 'SK_ID_CURR', how = 'left')","execution_count":56,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_application_previous_label = train_application_previous['TARGET']\ntrain_application_previous = train_application_previous.drop(['TARGET'], axis=1)","execution_count":38,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#bureau_columns = list(train_bureau.columns)\n#application_columns = list(train_application.columns)\n#original_features = list(set(bureau_columns) & set(application_columns))\n#print(original_features)","execution_count":39,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Remove Collinear Variables in previous_application"},{"metadata":{"trusted":true,"collapsed":true},"cell_type":"code","source":"# Threshold for removing correlated variables\nthreshold = 0.9\n\n# Absolute value correlation matrix\ncorr_matrix = train_application_previous.corr().abs()\ncorr_matrix.head()\n\n# Upper triangle of correlations\nupper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\nupper.head()\n\n# Select columns with correlations above threshold\nto_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n\nprint('There are %d columns to remove.' % (len(to_drop)))\n\ntrain_application_previous = train_application_previous.drop(columns = to_drop)\ntest_application_previous = test_application_previous.drop(columns = to_drop)\n\nprint('Training shape: ', train_application_previous.shape)\nprint('Testing shape: ', test_application_previous.shape)\n\n\ntrain_application_previous.to_csv('train_application_previous_dropped_collinearity.csv' , index = False)\ntest_application_previous.to_csv('test_application_previous.csv', index = False)","execution_count":40,"outputs":[{"output_type":"execute_result","execution_count":40,"data":{"text/plain":" SK_ID_CURR CNT_CHILDREN AMT_INCOME_TOTAL AMT_CREDIT \\\nSK_ID_CURR 1.000000 0.001129 0.001820 0.000343 \nCNT_CHILDREN 0.001129 1.000000 0.012882 0.002145 \nAMT_INCOME_TOTAL 0.001820 0.012882 1.000000 0.156870 \nAMT_CREDIT 0.000343 0.002145 0.156870 1.000000 \nAMT_ANNUITY 0.000433 0.021374 0.191657 0.770138 \n\n AMT_ANNUITY AMT_GOODS_PRICE REGION_POPULATION_RELATIVE \\\nSK_ID_CURR 0.000433 0.000232 0.000849 \nCNT_CHILDREN 0.021374 0.001827 0.025573 \nAMT_INCOME_TOTAL 0.191657 0.159610 0.074796 \nAMT_CREDIT 0.770138 0.986968 0.099738 \nAMT_ANNUITY 1.000000 0.775109 0.118429 \n\n DAYS_BIRTH DAYS_EMPLOYED DAYS_REGISTRATION ... \\\nSK_ID_CURR 0.001500 0.001366 0.000973 ... \nCNT_CHILDREN 0.330938 0.239818 0.183395 ... \nAMT_INCOME_TOTAL 0.027261 0.064223 0.027805 ... \nAMT_CREDIT 0.055436 0.066838 0.009621 ... \nAMT_ANNUITY 0.009445 0.104332 0.038514 ... \n\n client_installments_AMT_PAYMENT_min_sum \\\nSK_ID_CURR 0.001611 \nCNT_CHILDREN 0.059067 \nAMT_INCOME_TOTAL 0.070970 \nAMT_CREDIT 0.132782 \nAMT_ANNUITY 0.140318 \n\n client_installments_AMT_INSTALMENT_min_sum \\\nSK_ID_CURR 0.002339 \nCNT_CHILDREN 0.055789 \nAMT_INCOME_TOTAL 0.076906 \nAMT_CREDIT 0.128531 \nAMT_ANNUITY 0.150905 \n\n client_installments_AMT_PAYMENT_sum_max \\\nSK_ID_CURR 0.003678 \nCNT_CHILDREN 0.037906 \nAMT_INCOME_TOTAL 0.093826 \nAMT_CREDIT 0.194276 \nAMT_ANNUITY 0.187100 \n\n client_installments_AMT_INSTALMENT_sum_max \\\nSK_ID_CURR 0.003268 \nCNT_CHILDREN 0.036924 \nAMT_INCOME_TOTAL 0.095551 \nAMT_CREDIT 0.191479 \nAMT_ANNUITY 0.191980 \n\n client_installments_AMT_PAYMENT_mean_sum \\\nSK_ID_CURR 0.002236 \nCNT_CHILDREN 0.052487 \nAMT_INCOME_TOTAL 0.087334 \nAMT_CREDIT 0.169586 \nAMT_ANNUITY 0.162916 \n\n client_installments_AMT_INSTALMENT_mean_sum \\\nSK_ID_CURR 0.001908 \nCNT_CHILDREN 0.051925 \nAMT_INCOME_TOTAL 0.086902 \nAMT_CREDIT 0.163961 \nAMT_ANNUITY 0.162484 \n\n client_installments_AMT_INSTALMENT_max_sum \\\nSK_ID_CURR 0.000912 \nCNT_CHILDREN 0.029404 \nAMT_INCOME_TOTAL 0.061471 \nAMT_CREDIT 0.135353 \nAMT_ANNUITY 0.106058 \n\n client_installments_AMT_PAYMENT_max_sum \\\nSK_ID_CURR 0.000967 \nCNT_CHILDREN 0.029832 \nAMT_INCOME_TOTAL 0.061982 \nAMT_CREDIT 0.135898 \nAMT_ANNUITY 0.106899 \n\n client_installments_AMT_PAYMENT_sum_sum \\\nSK_ID_CURR 0.001945 \nCNT_CHILDREN 0.038148 \nAMT_INCOME_TOTAL 0.058026 \nAMT_CREDIT 0.114718 \nAMT_ANNUITY 0.101538 \n\n client_installments_AMT_INSTALMENT_sum_sum \nSK_ID_CURR 0.001566 \nCNT_CHILDREN 0.037288 \nAMT_INCOME_TOTAL 0.056434 \nAMT_CREDIT 0.109415 \nAMT_ANNUITY 0.098940 \n\n[5 rows x 996 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>SK_ID_CURR</th>\n <th>CNT_CHILDREN</th>\n <th>AMT_INCOME_TOTAL</th>\n <th>AMT_CREDIT</th>\n <th>AMT_ANNUITY</th>\n <th>AMT_GOODS_PRICE</th>\n <th>REGION_POPULATION_RELATIVE</th>\n <th>DAYS_BIRTH</th>\n <th>DAYS_EMPLOYED</th>\n <th>DAYS_REGISTRATION</th>\n <th>...</th>\n <th>client_installments_AMT_PAYMENT_min_sum</th>\n <th>client_installments_AMT_INSTALMENT_min_sum</th>\n <th>client_installments_AMT_PAYMENT_sum_max</th>\n <th>client_installments_AMT_INSTALMENT_sum_max</th>\n <th>client_installments_AMT_PAYMENT_mean_sum</th>\n <th>client_installments_AMT_INSTALMENT_mean_sum</th>\n <th>client_installments_AMT_INSTALMENT_max_sum</th>\n <th>client_installments_AMT_PAYMENT_max_sum</th>\n <th>client_installments_AMT_PAYMENT_sum_sum</th>\n <th>client_installments_AMT_INSTALMENT_sum_sum</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>SK_ID_CURR</th>\n <td>1.000000</td>\n <td>0.001129</td>\n <td>0.001820</td>\n <td>0.000343</td>\n <td>0.000433</td>\n <td>0.000232</td>\n <td>0.000849</td>\n <td>0.001500</td>\n <td>0.001366</td>\n <td>0.000973</td>\n <td>...</td>\n <td>0.001611</td>\n <td>0.002339</td>\n <td>0.003678</td>\n <td>0.003268</td>\n <td>0.002236</td>\n <td>0.001908</td>\n <td>0.000912</td>\n <td>0.000967</td>\n <td>0.001945</td>\n <td>0.001566</td>\n </tr>\n <tr>\n <th>CNT_CHILDREN</th>\n <td>0.001129</td>\n <td>1.000000</td>\n <td>0.012882</td>\n <td>0.002145</td>\n <td>0.021374</td>\n <td>0.001827</td>\n <td>0.025573</td>\n <td>0.330938</td>\n <td>0.239818</td>\n <td>0.183395</td>\n <td>...</td>\n <td>0.059067</td>\n <td>0.055789</td>\n <td>0.037906</td>\n <td>0.036924</td>\n <td>0.052487</td>\n <td>0.051925</td>\n <td>0.029404</td>\n <td>0.029832</td>\n <td>0.038148</td>\n <td>0.037288</td>\n </tr>\n <tr>\n <th>AMT_INCOME_TOTAL</th>\n <td>0.001820</td>\n <td>0.012882</td>\n <td>1.000000</td>\n <td>0.156870</td>\n <td>0.191657</td>\n <td>0.159610</td>\n <td>0.074796</td>\n <td>0.027261</td>\n <td>0.064223</td>\n <td>0.027805</td>\n <td>...</td>\n <td>0.070970</td>\n <td>0.076906</td>\n <td>0.093826</td>\n <td>0.095551</td>\n <td>0.087334</td>\n <td>0.086902</td>\n <td>0.061471</td>\n <td>0.061982</td>\n <td>0.058026</td>\n <td>0.056434</td>\n </tr>\n <tr>\n <th>AMT_CREDIT</th>\n <td>0.000343</td>\n <td>0.002145</td>\n <td>0.156870</td>\n <td>1.000000</td>\n <td>0.770138</td>\n <td>0.986968</td>\n <td>0.099738</td>\n <td>0.055436</td>\n <td>0.066838</td>\n <td>0.009621</td>\n <td>...</td>\n <td>0.132782</td>\n <td>0.128531</td>\n <td>0.194276</td>\n <td>0.191479</td>\n <td>0.169586</td>\n <td>0.163961</td>\n <td>0.135353</td>\n <td>0.135898</td>\n <td>0.114718</td>\n <td>0.109415</td>\n </tr>\n <tr>\n <th>AMT_ANNUITY</th>\n <td>0.000433</td>\n <td>0.021374</td>\n <td>0.191657</td>\n <td>0.770138</td>\n <td>1.000000</td>\n <td>0.775109</td>\n <td>0.118429</td>\n <td>0.009445</td>\n <td>0.104332</td>\n <td>0.038514</td>\n <td>...</td>\n <td>0.140318</td>\n <td>0.150905</td>\n <td>0.187100</td>\n <td>0.191980</td>\n <td>0.162916</td>\n <td>0.162484</td>\n <td>0.106058</td>\n <td>0.106899</td>\n <td>0.101538</td>\n <td>0.098940</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 996 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"# Bureau remove collinearity"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_application_bureau = train_application.merge(train_bureau, on='SK_ID_CURR', how = 'left')\ntrain_application_bureau_label = train_application_bureau['TARGET']\ntrain_application_bureau = train_application_bureau.drop(['TARGET'],axis=1)","execution_count":5,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_application_bureau.head()","execution_count":6,"outputs":[{"output_type":"execute_result","execution_count":6,"data":{"text/plain":" SK_ID_CURR NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY \\\n0 100002 Cash loans M N Y \n1 100003 Cash loans F N N \n2 100004 Revolving loans M Y Y \n3 100006 Cash loans F N Y \n4 100007 Cash loans M N Y \n\n CNT_CHILDREN AMT_INCOME_TOTAL AMT_CREDIT AMT_ANNUITY AMT_GOODS_PRICE \\\n0 0 202500.0 406597.5 24700.5 351000.0 \n1 0 270000.0 1293502.5 35698.5 1129500.0 \n2 0 67500.0 135000.0 6750.0 135000.0 \n3 0 135000.0 312682.5 29686.5 297000.0 \n4 0 121500.0 513000.0 21865.5 513000.0 \n\n ... client_bureau_balance_STATUS_X_count_max \\\n0 ... 3.0 \n1 ... NaN \n2 ... NaN \n3 ... NaN \n4 ... NaN \n\n client_bureau_balance_STATUS_0_count_max \\\n0 18.0 \n1 NaN \n2 NaN \n3 NaN \n4 NaN \n\n client_bureau_balance_STATUS_C_count_mean \\\n0 2.875 \n1 NaN \n2 NaN \n3 NaN \n4 NaN \n\n client_bureau_balance_MONTHS_BALANCE_count_mean \\\n0 13.75 \n1 NaN \n2 NaN \n3 NaN \n4 NaN \n\n client_bureau_balance_STATUS_X_count_sum \\\n0 15.0 \n1 0.0 \n2 0.0 \n3 NaN \n4 0.0 \n\n client_bureau_balance_STATUS_0_count_sum \\\n0 45.0 \n1 0.0 \n2 0.0 \n3 NaN \n4 0.0 \n\n client_bureau_balance_STATUS_C_count_max \\\n0 13.0 \n1 NaN \n2 NaN \n3 NaN \n4 NaN \n\n client_bureau_balance_MONTHS_BALANCE_count_max \\\n0 22.0 \n1 NaN \n2 NaN \n3 NaN \n4 NaN \n\n client_bureau_balance_STATUS_C_count_sum \\\n0 23.0 \n1 0.0 \n2 0.0 \n3 NaN \n4 0.0 \n\n client_bureau_balance_MONTHS_BALANCE_count_sum \n0 110.0 \n1 0.0 \n2 0.0 \n3 NaN \n4 0.0 \n\n[5 rows x 308 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>SK_ID_CURR</th>\n <th>NAME_CONTRACT_TYPE</th>\n <th>CODE_GENDER</th>\n <th>FLAG_OWN_CAR</th>\n <th>FLAG_OWN_REALTY</th>\n <th>CNT_CHILDREN</th>\n <th>AMT_INCOME_TOTAL</th>\n <th>AMT_CREDIT</th>\n <th>AMT_ANNUITY</th>\n <th>AMT_GOODS_PRICE</th>\n <th>...</th>\n <th>client_bureau_balance_STATUS_X_count_max</th>\n <th>client_bureau_balance_STATUS_0_count_max</th>\n <th>client_bureau_balance_STATUS_C_count_mean</th>\n <th>client_bureau_balance_MONTHS_BALANCE_count_mean</th>\n <th>client_bureau_balance_STATUS_X_count_sum</th>\n <th>client_bureau_balance_STATUS_0_count_sum</th>\n <th>client_bureau_balance_STATUS_C_count_max</th>\n <th>client_bureau_balance_MONTHS_BALANCE_count_max</th>\n <th>client_bureau_balance_STATUS_C_count_sum</th>\n <th>client_bureau_balance_MONTHS_BALANCE_count_sum</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>100002</td>\n <td>Cash loans</td>\n <td>M</td>\n <td>N</td>\n <td>Y</td>\n <td>0</td>\n <td>202500.0</td>\n <td>406597.5</td>\n <td>24700.5</td>\n <td>351000.0</td>\n <td>...</td>\n <td>3.0</td>\n <td>18.0</td>\n <td>2.875</td>\n <td>13.75</td>\n <td>15.0</td>\n <td>45.0</td>\n <td>13.0</td>\n <td>22.0</td>\n <td>23.0</td>\n <td>110.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>100003</td>\n <td>Cash loans</td>\n <td>F</td>\n <td>N</td>\n <td>N</td>\n <td>0</td>\n <td>270000.0</td>\n <td>1293502.5</td>\n <td>35698.5</td>\n <td>1129500.0</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>100004</td>\n <td>Revolving loans</td>\n <td>M</td>\n <td>Y</td>\n <td>Y</td>\n <td>0</td>\n <td>67500.0</td>\n <td>135000.0</td>\n <td>6750.0</td>\n <td>135000.0</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>100006</td>\n <td>Cash loans</td>\n <td>F</td>\n <td>N</td>\n <td>Y</td>\n <td>0</td>\n <td>135000.0</td>\n <td>312682.5</td>\n <td>29686.5</td>\n <td>297000.0</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>4</th>\n <td>100007</td>\n <td>Cash loans</td>\n <td>M</td>\n <td>N</td>\n <td>Y</td>\n <td>0</td>\n <td>121500.0</td>\n <td>513000.0</td>\n <td>21865.5</td>\n <td>513000.0</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 308 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_application_bureau.head()","execution_count":7,"outputs":[{"output_type":"execute_result","execution_count":7,"data":{"text/plain":" SK_ID_CURR NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY \\\n0 100001 Cash loans F N Y \n1 100005 Cash loans M N Y \n2 100013 Cash loans M Y Y \n3 100028 Cash loans F N Y \n4 100038 Cash loans M Y N \n\n CNT_CHILDREN AMT_INCOME_TOTAL AMT_CREDIT AMT_ANNUITY AMT_GOODS_PRICE \\\n0 0 135000.0 568800.0 20560.5 450000.0 \n1 0 99000.0 222768.0 17370.0 180000.0 \n2 0 202500.0 663264.0 69777.0 630000.0 \n3 2 315000.0 1575000.0 49018.5 1575000.0 \n4 1 180000.0 625500.0 32067.0 625500.0 \n\n ... client_bureau_balance_STATUS_X_count_max \\\n0 ... 9.0 \n1 ... 1.0 \n2 ... 40.0 \n3 ... 60.0 \n4 ... NaN \n\n client_bureau_balance_STATUS_0_count_max \\\n0 12.0 \n1 7.0 \n2 34.0 \n3 34.0 \n4 NaN \n\n client_bureau_balance_STATUS_C_count_mean \\\n0 15.714286 \n1 1.666667 \n2 25.750000 \n3 21.916667 \n4 NaN \n\n client_bureau_balance_MONTHS_BALANCE_count_mean \\\n0 24.571429 \n1 7.000000 \n2 57.500000 \n3 46.666667 \n4 NaN \n\n client_bureau_balance_STATUS_X_count_sum \\\n0 30.0 \n1 2.0 \n2 41.0 \n3 133.0 \n4 NaN \n\n client_bureau_balance_STATUS_0_count_sum \\\n0 31.0 \n1 14.0 \n2 79.0 \n3 164.0 \n4 NaN \n\n client_bureau_balance_STATUS_C_count_max \\\n0 44.0 \n1 5.0 \n2 44.0 \n3 62.0 \n4 NaN \n\n client_bureau_balance_MONTHS_BALANCE_count_max \\\n0 52.0 \n1 13.0 \n2 69.0 \n3 70.0 \n4 NaN \n\n client_bureau_balance_STATUS_C_count_sum \\\n0 110.0 \n1 5.0 \n2 103.0 \n3 263.0 \n4 NaN \n\n client_bureau_balance_MONTHS_BALANCE_count_sum \n0 172.0 \n1 21.0 \n2 230.0 \n3 560.0 \n4 NaN \n\n[5 rows x 308 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>SK_ID_CURR</th>\n <th>NAME_CONTRACT_TYPE</th>\n <th>CODE_GENDER</th>\n <th>FLAG_OWN_CAR</th>\n <th>FLAG_OWN_REALTY</th>\n <th>CNT_CHILDREN</th>\n <th>AMT_INCOME_TOTAL</th>\n <th>AMT_CREDIT</th>\n <th>AMT_ANNUITY</th>\n <th>AMT_GOODS_PRICE</th>\n <th>...</th>\n <th>client_bureau_balance_STATUS_X_count_max</th>\n <th>client_bureau_balance_STATUS_0_count_max</th>\n <th>client_bureau_balance_STATUS_C_count_mean</th>\n <th>client_bureau_balance_MONTHS_BALANCE_count_mean</th>\n <th>client_bureau_balance_STATUS_X_count_sum</th>\n <th>client_bureau_balance_STATUS_0_count_sum</th>\n <th>client_bureau_balance_STATUS_C_count_max</th>\n <th>client_bureau_balance_MONTHS_BALANCE_count_max</th>\n <th>client_bureau_balance_STATUS_C_count_sum</th>\n <th>client_bureau_balance_MONTHS_BALANCE_count_sum</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>100001</td>\n <td>Cash loans</td>\n <td>F</td>\n <td>N</td>\n <td>Y</td>\n <td>0</td>\n <td>135000.0</td>\n <td>568800.0</td>\n <td>20560.5</td>\n <td>450000.0</td>\n <td>...</td>\n <td>9.0</td>\n <td>12.0</td>\n <td>15.714286</td>\n <td>24.571429</td>\n <td>30.0</td>\n <td>31.0</td>\n <td>44.0</td>\n <td>52.0</td>\n <td>110.0</td>\n <td>172.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>100005</td>\n <td>Cash loans</td>\n <td>M</td>\n <td>N</td>\n <td>Y</td>\n <td>0</td>\n <td>99000.0</td>\n <td>222768.0</td>\n <td>17370.0</td>\n <td>180000.0</td>\n <td>...</td>\n <td>1.0</td>\n <td>7.0</td>\n <td>1.666667</td>\n <td>7.000000</td>\n <td>2.0</td>\n <td>14.0</td>\n <td>5.0</td>\n <td>13.0</td>\n <td>5.0</td>\n <td>21.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>100013</td>\n <td>Cash loans</td>\n <td>M</td>\n <td>Y</td>\n <td>Y</td>\n <td>0</td>\n <td>202500.0</td>\n <td>663264.0</td>\n <td>69777.0</td>\n <td>630000.0</td>\n <td>...</td>\n <td>40.0</td>\n <td>34.0</td>\n <td>25.750000</td>\n <td>57.500000</td>\n <td>41.0</td>\n <td>79.0</td>\n <td>44.0</td>\n <td>69.0</td>\n <td>103.0</td>\n <td>230.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>100028</td>\n <td>Cash loans</td>\n <td>F</td>\n <td>N</td>\n <td>Y</td>\n <td>2</td>\n <td>315000.0</td>\n <td>1575000.0</td>\n <td>49018.5</td>\n <td>1575000.0</td>\n <td>...</td>\n <td>60.0</td>\n <td>34.0</td>\n <td>21.916667</td>\n <td>46.666667</td>\n <td>133.0</td>\n <td>164.0</td>\n <td>62.0</td>\n <td>70.0</td>\n <td>263.0</td>\n <td>560.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>100038</td>\n <td>Cash loans</td>\n <td>M</td>\n <td>Y</td>\n <td>N</td>\n <td>1</td>\n <td>180000.0</td>\n <td>625500.0</td>\n <td>32067.0</td>\n <td>625500.0</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 308 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#train_application_bureau = pd.get_dummies(train_application_bureau)\n#train_application_bureau.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Threshold for removing correlated variables\nthreshold = 0.9\n\n# Absolute value correlation matrix\ncorr_matrix = train_application_bureau.corr().abs()\ncorr_matrix.head()","execution_count":8,"outputs":[{"output_type":"execute_result","execution_count":8,"data":{"text/plain":" SK_ID_CURR CNT_CHILDREN AMT_INCOME_TOTAL AMT_CREDIT \\\nSK_ID_CURR 1.000000 0.001129 0.001820 0.000343 \nCNT_CHILDREN 0.001129 1.000000 0.012882 0.002145 \nAMT_INCOME_TOTAL 0.001820 0.012882 1.000000 0.156870 \nAMT_CREDIT 0.000343 0.002145 0.156870 1.000000 \nAMT_ANNUITY 0.000433 0.021374 0.191657 0.770138 \n\n AMT_ANNUITY AMT_GOODS_PRICE REGION_POPULATION_RELATIVE \\\nSK_ID_CURR 0.000433 0.000232 0.000849 \nCNT_CHILDREN 0.021374 0.001827 0.025573 \nAMT_INCOME_TOTAL 0.191657 0.159610 0.074796 \nAMT_CREDIT 0.770138 0.986968 0.099738 \nAMT_ANNUITY 1.000000 0.775109 0.118429 \n\n DAYS_BIRTH DAYS_EMPLOYED DAYS_REGISTRATION ... \\\nSK_ID_CURR 0.001500 0.001366 0.000973 ... \nCNT_CHILDREN 0.330938 0.239818 0.183395 ... \nAMT_INCOME_TOTAL 0.027261 0.064223 0.027805 ... \nAMT_CREDIT 0.055436 0.066838 0.009621 ... \nAMT_ANNUITY 0.009445 0.104332 0.038514 ... \n\n client_bureau_balance_STATUS_X_count_max \\\nSK_ID_CURR 0.002939 \nCNT_CHILDREN 0.003205 \nAMT_INCOME_TOTAL 0.060925 \nAMT_CREDIT 0.038554 \nAMT_ANNUITY 0.036626 \n\n client_bureau_balance_STATUS_0_count_max \\\nSK_ID_CURR 0.001397 \nCNT_CHILDREN 0.010392 \nAMT_INCOME_TOTAL 0.114616 \nAMT_CREDIT 0.054135 \nAMT_ANNUITY 0.055830 \n\n client_bureau_balance_STATUS_C_count_mean \\\nSK_ID_CURR 0.002377 \nCNT_CHILDREN 0.019940 \nAMT_INCOME_TOTAL 0.013516 \nAMT_CREDIT 0.044674 \nAMT_ANNUITY 0.048469 \n\n client_bureau_balance_MONTHS_BALANCE_count_mean \\\nSK_ID_CURR 0.004068 \nCNT_CHILDREN 0.026412 \nAMT_INCOME_TOTAL 0.023685 \nAMT_CREDIT 0.071355 \nAMT_ANNUITY 0.071230 \n\n client_bureau_balance_STATUS_X_count_sum \\\nSK_ID_CURR 0.000683 \nCNT_CHILDREN 0.003957 \nAMT_INCOME_TOTAL 0.024900 \nAMT_CREDIT 0.024139 \nAMT_ANNUITY 0.077655 \n\n client_bureau_balance_STATUS_0_count_sum \\\nSK_ID_CURR 0.000555 \nCNT_CHILDREN 0.006427 \nAMT_INCOME_TOTAL 0.039056 \nAMT_CREDIT 0.025210 \nAMT_ANNUITY 0.110749 \n\n client_bureau_balance_STATUS_C_count_max \\\nSK_ID_CURR 0.000206 \nCNT_CHILDREN 0.014367 \nAMT_INCOME_TOTAL 0.049709 \nAMT_CREDIT 0.054067 \nAMT_ANNUITY 0.054657 \n\n client_bureau_balance_MONTHS_BALANCE_count_max \\\nSK_ID_CURR 0.002628 \nCNT_CHILDREN 0.019516 \nAMT_INCOME_TOTAL 0.070643 \nAMT_CREDIT 0.068819 \nAMT_ANNUITY 0.062622 \n\n client_bureau_balance_STATUS_C_count_sum \\\nSK_ID_CURR 0.000252 \nCNT_CHILDREN 0.005527 \nAMT_INCOME_TOTAL 0.024610 \nAMT_CREDIT 0.023609 \nAMT_ANNUITY 0.101982 \n\n client_bureau_balance_MONTHS_BALANCE_count_sum \nSK_ID_CURR 0.000318 \nCNT_CHILDREN 0.006368 \nAMT_INCOME_TOTAL 0.034869 \nAMT_CREDIT 0.029075 \nAMT_ANNUITY 0.119476 \n\n[5 rows x 292 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>SK_ID_CURR</th>\n <th>CNT_CHILDREN</th>\n <th>AMT_INCOME_TOTAL</th>\n <th>AMT_CREDIT</th>\n <th>AMT_ANNUITY</th>\n <th>AMT_GOODS_PRICE</th>\n <th>REGION_POPULATION_RELATIVE</th>\n <th>DAYS_BIRTH</th>\n <th>DAYS_EMPLOYED</th>\n <th>DAYS_REGISTRATION</th>\n <th>...</th>\n <th>client_bureau_balance_STATUS_X_count_max</th>\n <th>client_bureau_balance_STATUS_0_count_max</th>\n <th>client_bureau_balance_STATUS_C_count_mean</th>\n <th>client_bureau_balance_MONTHS_BALANCE_count_mean</th>\n <th>client_bureau_balance_STATUS_X_count_sum</th>\n <th>client_bureau_balance_STATUS_0_count_sum</th>\n <th>client_bureau_balance_STATUS_C_count_max</th>\n <th>client_bureau_balance_MONTHS_BALANCE_count_max</th>\n <th>client_bureau_balance_STATUS_C_count_sum</th>\n <th>client_bureau_balance_MONTHS_BALANCE_count_sum</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>SK_ID_CURR</th>\n <td>1.000000</td>\n <td>0.001129</td>\n <td>0.001820</td>\n <td>0.000343</td>\n <td>0.000433</td>\n <td>0.000232</td>\n <td>0.000849</td>\n <td>0.001500</td>\n <td>0.001366</td>\n <td>0.000973</td>\n <td>...</td>\n <td>0.002939</td>\n <td>0.001397</td>\n <td>0.002377</td>\n <td>0.004068</td>\n <td>0.000683</td>\n <td>0.000555</td>\n <td>0.000206</td>\n <td>0.002628</td>\n <td>0.000252</td>\n <td>0.000318</td>\n </tr>\n <tr>\n <th>CNT_CHILDREN</th>\n <td>0.001129</td>\n <td>1.000000</td>\n <td>0.012882</td>\n <td>0.002145</td>\n <td>0.021374</td>\n <td>0.001827</td>\n <td>0.025573</td>\n <td>0.330938</td>\n <td>0.239818</td>\n <td>0.183395</td>\n <td>...</td>\n <td>0.003205</td>\n <td>0.010392</td>\n <td>0.019940</td>\n <td>0.026412</td>\n <td>0.003957</td>\n <td>0.006427</td>\n <td>0.014367</td>\n <td>0.019516</td>\n <td>0.005527</td>\n <td>0.006368</td>\n </tr>\n <tr>\n <th>AMT_INCOME_TOTAL</th>\n <td>0.001820</td>\n <td>0.012882</td>\n <td>1.000000</td>\n <td>0.156870</td>\n <td>0.191657</td>\n <td>0.159610</td>\n <td>0.074796</td>\n <td>0.027261</td>\n <td>0.064223</td>\n <td>0.027805</td>\n <td>...</td>\n <td>0.060925</td>\n <td>0.114616</td>\n <td>0.013516</td>\n <td>0.023685</td>\n <td>0.024900</td>\n <td>0.039056</td>\n <td>0.049709</td>\n <td>0.070643</td>\n <td>0.024610</td>\n <td>0.034869</td>\n </tr>\n <tr>\n <th>AMT_CREDIT</th>\n <td>0.000343</td>\n <td>0.002145</td>\n <td>0.156870</td>\n <td>1.000000</td>\n <td>0.770138</td>\n <td>0.986968</td>\n <td>0.099738</td>\n <td>0.055436</td>\n <td>0.066838</td>\n <td>0.009621</td>\n <td>...</td>\n <td>0.038554</td>\n <td>0.054135</td>\n <td>0.044674</td>\n <td>0.071355</td>\n <td>0.024139</td>\n <td>0.025210</td>\n <td>0.054067</td>\n <td>0.068819</td>\n <td>0.023609</td>\n <td>0.029075</td>\n </tr>\n <tr>\n <th>AMT_ANNUITY</th>\n <td>0.000433</td>\n <td>0.021374</td>\n <td>0.191657</td>\n <td>0.770138</td>\n <td>1.000000</td>\n <td>0.775109</td>\n <td>0.118429</td>\n <td>0.009445</td>\n <td>0.104332</td>\n <td>0.038514</td>\n <td>...</td>\n <td>0.036626</td>\n <td>0.055830</td>\n <td>0.048469</td>\n <td>0.071230</td>\n <td>0.077655</td>\n <td>0.110749</td>\n <td>0.054657</td>\n <td>0.062622</td>\n <td>0.101982</td>\n <td>0.119476</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 292 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Upper triangle of correlations\nupper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\nupper.head()","execution_count":9,"outputs":[{"output_type":"execute_result","execution_count":9,"data":{"text/plain":" SK_ID_CURR CNT_CHILDREN AMT_INCOME_TOTAL AMT_CREDIT \\\nSK_ID_CURR NaN 0.001129 0.001820 0.000343 \nCNT_CHILDREN NaN NaN 0.012882 0.002145 \nAMT_INCOME_TOTAL NaN NaN NaN 0.156870 \nAMT_CREDIT NaN NaN NaN NaN \nAMT_ANNUITY NaN NaN NaN NaN \n\n AMT_ANNUITY AMT_GOODS_PRICE REGION_POPULATION_RELATIVE \\\nSK_ID_CURR 0.000433 0.000232 0.000849 \nCNT_CHILDREN 0.021374 0.001827 0.025573 \nAMT_INCOME_TOTAL 0.191657 0.159610 0.074796 \nAMT_CREDIT 0.770138 0.986968 0.099738 \nAMT_ANNUITY NaN 0.775109 0.118429 \n\n DAYS_BIRTH DAYS_EMPLOYED DAYS_REGISTRATION ... \\\nSK_ID_CURR 0.001500 0.001366 0.000973 ... \nCNT_CHILDREN 0.330938 0.239818 0.183395 ... \nAMT_INCOME_TOTAL 0.027261 0.064223 0.027805 ... \nAMT_CREDIT 0.055436 0.066838 0.009621 ... \nAMT_ANNUITY 0.009445 0.104332 0.038514 ... \n\n client_bureau_balance_STATUS_X_count_max \\\nSK_ID_CURR 0.002939 \nCNT_CHILDREN 0.003205 \nAMT_INCOME_TOTAL 0.060925 \nAMT_CREDIT 0.038554 \nAMT_ANNUITY 0.036626 \n\n client_bureau_balance_STATUS_0_count_max \\\nSK_ID_CURR 0.001397 \nCNT_CHILDREN 0.010392 \nAMT_INCOME_TOTAL 0.114616 \nAMT_CREDIT 0.054135 \nAMT_ANNUITY 0.055830 \n\n client_bureau_balance_STATUS_C_count_mean \\\nSK_ID_CURR 0.002377 \nCNT_CHILDREN 0.019940 \nAMT_INCOME_TOTAL 0.013516 \nAMT_CREDIT 0.044674 \nAMT_ANNUITY 0.048469 \n\n client_bureau_balance_MONTHS_BALANCE_count_mean \\\nSK_ID_CURR 0.004068 \nCNT_CHILDREN 0.026412 \nAMT_INCOME_TOTAL 0.023685 \nAMT_CREDIT 0.071355 \nAMT_ANNUITY 0.071230 \n\n client_bureau_balance_STATUS_X_count_sum \\\nSK_ID_CURR 0.000683 \nCNT_CHILDREN 0.003957 \nAMT_INCOME_TOTAL 0.024900 \nAMT_CREDIT 0.024139 \nAMT_ANNUITY 0.077655 \n\n client_bureau_balance_STATUS_0_count_sum \\\nSK_ID_CURR 0.000555 \nCNT_CHILDREN 0.006427 \nAMT_INCOME_TOTAL 0.039056 \nAMT_CREDIT 0.025210 \nAMT_ANNUITY 0.110749 \n\n client_bureau_balance_STATUS_C_count_max \\\nSK_ID_CURR 0.000206 \nCNT_CHILDREN 0.014367 \nAMT_INCOME_TOTAL 0.049709 \nAMT_CREDIT 0.054067 \nAMT_ANNUITY 0.054657 \n\n client_bureau_balance_MONTHS_BALANCE_count_max \\\nSK_ID_CURR 0.002628 \nCNT_CHILDREN 0.019516 \nAMT_INCOME_TOTAL 0.070643 \nAMT_CREDIT 0.068819 \nAMT_ANNUITY 0.062622 \n\n client_bureau_balance_STATUS_C_count_sum \\\nSK_ID_CURR 0.000252 \nCNT_CHILDREN 0.005527 \nAMT_INCOME_TOTAL 0.024610 \nAMT_CREDIT 0.023609 \nAMT_ANNUITY 0.101982 \n\n client_bureau_balance_MONTHS_BALANCE_count_sum \nSK_ID_CURR 0.000318 \nCNT_CHILDREN 0.006368 \nAMT_INCOME_TOTAL 0.034869 \nAMT_CREDIT 0.029075 \nAMT_ANNUITY 0.119476 \n\n[5 rows x 292 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>SK_ID_CURR</th>\n <th>CNT_CHILDREN</th>\n <th>AMT_INCOME_TOTAL</th>\n <th>AMT_CREDIT</th>\n <th>AMT_ANNUITY</th>\n <th>AMT_GOODS_PRICE</th>\n <th>REGION_POPULATION_RELATIVE</th>\n <th>DAYS_BIRTH</th>\n <th>DAYS_EMPLOYED</th>\n <th>DAYS_REGISTRATION</th>\n <th>...</th>\n <th>client_bureau_balance_STATUS_X_count_max</th>\n <th>client_bureau_balance_STATUS_0_count_max</th>\n <th>client_bureau_balance_STATUS_C_count_mean</th>\n <th>client_bureau_balance_MONTHS_BALANCE_count_mean</th>\n <th>client_bureau_balance_STATUS_X_count_sum</th>\n <th>client_bureau_balance_STATUS_0_count_sum</th>\n <th>client_bureau_balance_STATUS_C_count_max</th>\n <th>client_bureau_balance_MONTHS_BALANCE_count_max</th>\n <th>client_bureau_balance_STATUS_C_count_sum</th>\n <th>client_bureau_balance_MONTHS_BALANCE_count_sum</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>SK_ID_CURR</th>\n <td>NaN</td>\n <td>0.001129</td>\n <td>0.001820</td>\n <td>0.000343</td>\n <td>0.000433</td>\n <td>0.000232</td>\n <td>0.000849</td>\n <td>0.001500</td>\n <td>0.001366</td>\n <td>0.000973</td>\n <td>...</td>\n <td>0.002939</td>\n <td>0.001397</td>\n <td>0.002377</td>\n <td>0.004068</td>\n <td>0.000683</td>\n <td>0.000555</td>\n <td>0.000206</td>\n <td>0.002628</td>\n <td>0.000252</td>\n <td>0.000318</td>\n </tr>\n <tr>\n <th>CNT_CHILDREN</th>\n <td>NaN</td>\n <td>NaN</td>\n <td>0.012882</td>\n <td>0.002145</td>\n <td>0.021374</td>\n <td>0.001827</td>\n <td>0.025573</td>\n <td>0.330938</td>\n <td>0.239818</td>\n <td>0.183395</td>\n <td>...</td>\n <td>0.003205</td>\n <td>0.010392</td>\n <td>0.019940</td>\n <td>0.026412</td>\n <td>0.003957</td>\n <td>0.006427</td>\n <td>0.014367</td>\n <td>0.019516</td>\n <td>0.005527</td>\n <td>0.006368</td>\n </tr>\n <tr>\n <th>AMT_INCOME_TOTAL</th>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>0.156870</td>\n <td>0.191657</td>\n <td>0.159610</td>\n <td>0.074796</td>\n <td>0.027261</td>\n <td>0.064223</td>\n <td>0.027805</td>\n <td>...</td>\n <td>0.060925</td>\n <td>0.114616</td>\n <td>0.013516</td>\n <td>0.023685</td>\n <td>0.024900</td>\n <td>0.039056</td>\n <td>0.049709</td>\n <td>0.070643</td>\n <td>0.024610</td>\n <td>0.034869</td>\n </tr>\n <tr>\n <th>AMT_CREDIT</th>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>0.770138</td>\n <td>0.986968</td>\n <td>0.099738</td>\n <td>0.055436</td>\n <td>0.066838</td>\n <td>0.009621</td>\n <td>...</td>\n <td>0.038554</td>\n <td>0.054135</td>\n <td>0.044674</td>\n <td>0.071355</td>\n <td>0.024139</td>\n <td>0.025210</td>\n <td>0.054067</td>\n <td>0.068819</td>\n <td>0.023609</td>\n <td>0.029075</td>\n </tr>\n <tr>\n <th>AMT_ANNUITY</th>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>0.775109</td>\n <td>0.118429</td>\n <td>0.009445</td>\n <td>0.104332</td>\n <td>0.038514</td>\n <td>...</td>\n <td>0.036626</td>\n <td>0.055830</td>\n <td>0.048469</td>\n <td>0.071230</td>\n <td>0.077655</td>\n <td>0.110749</td>\n <td>0.054657</td>\n <td>0.062622</td>\n <td>0.101982</td>\n <td>0.119476</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 292 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Select columns with correlations above threshold\nto_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n\nprint('There are %d columns to remove.' % (len(to_drop)))","execution_count":10,"outputs":[{"output_type":"stream","text":"There are 71 columns to remove.\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_application_bureau_dropped = train_application_bureau.drop(columns = to_drop)\ntest_application_bureau_dropped = test_application_bureau.drop(columns = to_drop)","execution_count":11,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_application_bureau_dropped.head()","execution_count":12,"outputs":[{"output_type":"execute_result","execution_count":12,"data":{"text/plain":" SK_ID_CURR NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY \\\n0 100002 Cash loans M N Y \n1 100003 Cash loans F N N \n2 100004 Revolving loans M Y Y \n3 100006 Cash loans F N Y \n4 100007 Cash loans M N Y \n\n CNT_CHILDREN AMT_INCOME_TOTAL AMT_CREDIT AMT_ANNUITY NAME_TYPE_SUITE \\\n0 0 202500.0 406597.5 24700.5 Unaccompanied \n1 0 270000.0 1293502.5 35698.5 Family \n2 0 67500.0 135000.0 6750.0 Unaccompanied \n3 0 135000.0 312682.5 29686.5 Unaccompanied \n4 0 121500.0 513000.0 21865.5 Unaccompanied \n\n ... client_bureau_balance_STATUS_0_count_norm_sum \\\n0 ... 3.255682 \n1 ... 0.000000 \n2 ... 0.000000 \n3 ... NaN \n4 ... 0.000000 \n\n client_bureau_balance_STATUS_C_count_norm_sum \\\n0 1.403409 \n1 0.000000 \n2 0.000000 \n3 NaN \n4 0.000000 \n\n client_bureau_balance_STATUS_X_count_mean \\\n0 1.875 \n1 NaN \n2 NaN \n3 NaN \n4 NaN \n\n client_bureau_balance_STATUS_0_count_mean \\\n0 5.625 \n1 NaN \n2 NaN \n3 NaN \n4 NaN \n\n client_bureau_balance_STATUS_X_count_max \\\n0 3.0 \n1 NaN \n2 NaN \n3 NaN \n4 NaN \n\n client_bureau_balance_STATUS_0_count_max \\\n0 18.0 \n1 NaN \n2 NaN \n3 NaN \n4 NaN \n\n client_bureau_balance_STATUS_C_count_mean \\\n0 2.875 \n1 NaN \n2 NaN \n3 NaN \n4 NaN \n\n client_bureau_balance_STATUS_X_count_sum \\\n0 15.0 \n1 0.0 \n2 0.0 \n3 NaN \n4 0.0 \n\n client_bureau_balance_STATUS_0_count_sum \\\n0 45.0 \n1 0.0 \n2 0.0 \n3 NaN \n4 0.0 \n\n client_bureau_balance_STATUS_C_count_max \n0 13.0 \n1 NaN \n2 NaN \n3 NaN \n4 NaN \n\n[5 rows x 237 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>SK_ID_CURR</th>\n <th>NAME_CONTRACT_TYPE</th>\n <th>CODE_GENDER</th>\n <th>FLAG_OWN_CAR</th>\n <th>FLAG_OWN_REALTY</th>\n <th>CNT_CHILDREN</th>\n <th>AMT_INCOME_TOTAL</th>\n <th>AMT_CREDIT</th>\n <th>AMT_ANNUITY</th>\n <th>NAME_TYPE_SUITE</th>\n <th>...</th>\n <th>client_bureau_balance_STATUS_0_count_norm_sum</th>\n <th>client_bureau_balance_STATUS_C_count_norm_sum</th>\n <th>client_bureau_balance_STATUS_X_count_mean</th>\n <th>client_bureau_balance_STATUS_0_count_mean</th>\n <th>client_bureau_balance_STATUS_X_count_max</th>\n <th>client_bureau_balance_STATUS_0_count_max</th>\n <th>client_bureau_balance_STATUS_C_count_mean</th>\n <th>client_bureau_balance_STATUS_X_count_sum</th>\n <th>client_bureau_balance_STATUS_0_count_sum</th>\n <th>client_bureau_balance_STATUS_C_count_max</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>100002</td>\n <td>Cash loans</td>\n <td>M</td>\n <td>N</td>\n <td>Y</td>\n <td>0</td>\n <td>202500.0</td>\n <td>406597.5</td>\n <td>24700.5</td>\n <td>Unaccompanied</td>\n <td>...</td>\n <td>3.255682</td>\n <td>1.403409</td>\n <td>1.875</td>\n <td>5.625</td>\n <td>3.0</td>\n <td>18.0</td>\n <td>2.875</td>\n <td>15.0</td>\n <td>45.0</td>\n <td>13.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>100003</td>\n <td>Cash loans</td>\n <td>F</td>\n <td>N</td>\n <td>N</td>\n <td>0</td>\n <td>270000.0</td>\n <td>1293502.5</td>\n <td>35698.5</td>\n <td>Family</td>\n <td>...</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>2</th>\n <td>100004</td>\n <td>Revolving loans</td>\n <td>M</td>\n <td>Y</td>\n <td>Y</td>\n <td>0</td>\n <td>67500.0</td>\n <td>135000.0</td>\n <td>6750.0</td>\n <td>Unaccompanied</td>\n <td>...</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>3</th>\n <td>100006</td>\n <td>Cash loans</td>\n <td>F</td>\n <td>N</td>\n <td>Y</td>\n <td>0</td>\n <td>135000.0</td>\n <td>312682.5</td>\n <td>29686.5</td>\n <td>Unaccompanied</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>4</th>\n <td>100007</td>\n <td>Cash loans</td>\n <td>M</td>\n <td>N</td>\n <td>Y</td>\n <td>0</td>\n <td>121500.0</td>\n <td>513000.0</td>\n <td>21865.5</td>\n <td>Unaccompanied</td>\n <td>...</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>NaN</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 237 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_application_bureau_dropped.head()","execution_count":57,"outputs":[{"output_type":"execute_result","execution_count":57,"data":{"text/plain":" NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY CNT_CHILDREN \\\n0 Cash loans F N Y 0 \n1 Cash loans M N Y 0 \n2 Cash loans M Y Y 0 \n3 Cash loans F N Y 2 \n4 Cash loans M Y N 1 \n\n AMT_INCOME_TOTAL AMT_CREDIT AMT_ANNUITY NAME_TYPE_SUITE NAME_INCOME_TYPE \\\n0 135000.0 568800.0 20560.5 Unaccompanied Working \n1 99000.0 222768.0 17370.0 Unaccompanied Working \n2 202500.0 663264.0 69777.0 NaN Working \n3 315000.0 1575000.0 49018.5 Unaccompanied Working \n4 180000.0 625500.0 32067.0 Unaccompanied Working \n\n ... client_bureau_balance_STATUS_0_count_norm_sum \\\n0 ... 2.356557 \n1 ... 2.205128 \n2 ... 1.282872 \n3 ... 4.527848 \n4 ... NaN \n\n client_bureau_balance_STATUS_C_count_norm_sum \\\n0 3.088683 \n1 0.384615 \n2 1.588142 \n3 4.346939 \n4 NaN \n\n client_bureau_balance_STATUS_X_count_mean \\\n0 4.285714 \n1 0.666667 \n2 10.250000 \n3 11.083333 \n4 NaN \n\n client_bureau_balance_STATUS_0_count_mean \\\n0 4.428571 \n1 4.666667 \n2 19.750000 \n3 13.666667 \n4 NaN \n\n client_bureau_balance_STATUS_X_count_max \\\n0 9.0 \n1 1.0 \n2 40.0 \n3 60.0 \n4 NaN \n\n client_bureau_balance_STATUS_0_count_max \\\n0 12.0 \n1 7.0 \n2 34.0 \n3 34.0 \n4 NaN \n\n client_bureau_balance_STATUS_C_count_mean \\\n0 15.714286 \n1 1.666667 \n2 25.750000 \n3 21.916667 \n4 NaN \n\n client_bureau_balance_STATUS_X_count_sum \\\n0 30.0 \n1 2.0 \n2 41.0 \n3 133.0 \n4 NaN \n\n client_bureau_balance_STATUS_0_count_sum \\\n0 31.0 \n1 14.0 \n2 79.0 \n3 164.0 \n4 NaN \n\n client_bureau_balance_STATUS_C_count_max \n0 44.0 \n1 5.0 \n2 44.0 \n3 62.0 \n4 NaN \n\n[5 rows x 236 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>NAME_CONTRACT_TYPE</th>\n <th>CODE_GENDER</th>\n <th>FLAG_OWN_CAR</th>\n <th>FLAG_OWN_REALTY</th>\n <th>CNT_CHILDREN</th>\n <th>AMT_INCOME_TOTAL</th>\n <th>AMT_CREDIT</th>\n <th>AMT_ANNUITY</th>\n <th>NAME_TYPE_SUITE</th>\n <th>NAME_INCOME_TYPE</th>\n <th>...</th>\n <th>client_bureau_balance_STATUS_0_count_norm_sum</th>\n <th>client_bureau_balance_STATUS_C_count_norm_sum</th>\n <th>client_bureau_balance_STATUS_X_count_mean</th>\n <th>client_bureau_balance_STATUS_0_count_mean</th>\n <th>client_bureau_balance_STATUS_X_count_max</th>\n <th>client_bureau_balance_STATUS_0_count_max</th>\n <th>client_bureau_balance_STATUS_C_count_mean</th>\n <th>client_bureau_balance_STATUS_X_count_sum</th>\n <th>client_bureau_balance_STATUS_0_count_sum</th>\n <th>client_bureau_balance_STATUS_C_count_max</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Cash loans</td>\n <td>F</td>\n <td>N</td>\n <td>Y</td>\n <td>0</td>\n <td>135000.0</td>\n <td>568800.0</td>\n <td>20560.5</td>\n <td>Unaccompanied</td>\n <td>Working</td>\n <td>...</td>\n <td>2.356557</td>\n <td>3.088683</td>\n <td>4.285714</td>\n <td>4.428571</td>\n <td>9.0</td>\n <td>12.0</td>\n <td>15.714286</td>\n <td>30.0</td>\n <td>31.0</td>\n <td>44.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Cash loans</td>\n <td>M</td>\n <td>N</td>\n <td>Y</td>\n <td>0</td>\n <td>99000.0</td>\n <td>222768.0</td>\n <td>17370.0</td>\n <td>Unaccompanied</td>\n <td>Working</td>\n <td>...</td>\n <td>2.205128</td>\n <td>0.384615</td>\n <td>0.666667</td>\n <td>4.666667</td>\n <td>1.0</td>\n <td>7.0</td>\n <td>1.666667</td>\n <td>2.0</td>\n <td>14.0</td>\n <td>5.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Cash loans</td>\n <td>M</td>\n <td>Y</td>\n <td>Y</td>\n <td>0</td>\n <td>202500.0</td>\n <td>663264.0</td>\n <td>69777.0</td>\n <td>NaN</td>\n <td>Working</td>\n <td>...</td>\n <td>1.282872</td>\n <td>1.588142</td>\n <td>10.250000</td>\n <td>19.750000</td>\n <td>40.0</td>\n <td>34.0</td>\n <td>25.750000</td>\n <td>41.0</td>\n <td>79.0</td>\n <td>44.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Cash loans</td>\n <td>F</td>\n <td>N</td>\n <td>Y</td>\n <td>2</td>\n <td>315000.0</td>\n <td>1575000.0</td>\n <td>49018.5</td>\n <td>Unaccompanied</td>\n <td>Working</td>\n <td>...</td>\n <td>4.527848</td>\n <td>4.346939</td>\n <td>11.083333</td>\n <td>13.666667</td>\n <td>60.0</td>\n <td>34.0</td>\n <td>21.916667</td>\n <td>133.0</td>\n <td>164.0</td>\n <td>62.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Cash loans</td>\n <td>M</td>\n <td>Y</td>\n <td>N</td>\n <td>1</td>\n <td>180000.0</td>\n <td>625500.0</td>\n <td>32067.0</td>\n <td>Unaccompanied</td>\n <td>Working</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 236 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_application_bureau_dropped.to_csv('train_application_bureau_dropped_collinear.csv',index=False)\ntest_application_bureau_dropped.to_csv('test_application_bureau_dropped_collinear.csv',index=False)","execution_count":13,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_application_bureau_dropped = train_application_bureau_dropped.drop(columns = ['SK_ID_CURR'])\ntest_application_bureau_dropped = test_application_bureau_dropped.drop(columns = ['SK_ID_CURR'])","execution_count":16,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_application_bureau_dropped","execution_count":17,"outputs":[{"output_type":"execute_result","execution_count":17,"data":{"text/plain":" NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY \\\n0 Cash loans M N Y \n1 Cash loans F N N \n2 Revolving loans M Y Y \n3 Cash loans F N Y \n4 Cash loans M N Y \n... ... ... ... ... \n307506 Cash loans M N N \n307507 Cash loans F N Y \n307508 Cash loans F N Y \n307509 Cash loans F N Y \n307510 Cash loans F N N \n\n CNT_CHILDREN AMT_INCOME_TOTAL AMT_CREDIT AMT_ANNUITY \\\n0 0 202500.0 406597.5 24700.5 \n1 0 270000.0 1293502.5 35698.5 \n2 0 67500.0 135000.0 6750.0 \n3 0 135000.0 312682.5 29686.5 \n4 0 121500.0 513000.0 21865.5 \n... ... ... ... ... \n307506 0 157500.0 254700.0 27558.0 \n307507 0 72000.0 269550.0 12001.5 \n307508 0 153000.0 677664.0 29979.0 \n307509 0 171000.0 370107.0 20205.0 \n307510 0 157500.0 675000.0 49117.5 \n\n NAME_TYPE_SUITE NAME_INCOME_TYPE ... \\\n0 Unaccompanied Working ... \n1 Family State servant ... \n2 Unaccompanied Working ... \n3 Unaccompanied Working ... \n4 Unaccompanied Working ... \n... ... ... ... \n307506 Unaccompanied Working ... \n307507 Unaccompanied Pensioner ... \n307508 Unaccompanied Working ... \n307509 Unaccompanied Commercial associate ... \n307510 Unaccompanied Commercial associate ... \n\n client_bureau_balance_STATUS_0_count_norm_sum \\\n0 3.255682 \n1 0.000000 \n2 0.000000 \n3 NaN \n4 0.000000 \n... ... \n307506 NaN \n307507 NaN \n307508 1.619624 \n307509 0.216216 \n307510 3.115737 \n\n client_bureau_balance_STATUS_C_count_norm_sum \\\n0 1.403409 \n1 0.000000 \n2 0.000000 \n3 NaN \n4 0.000000 \n... ... \n307506 NaN \n307507 NaN \n307508 1.838710 \n307509 0.783784 \n307510 7.101613 \n\n client_bureau_balance_STATUS_X_count_mean \\\n0 1.875000 \n1 NaN \n2 NaN \n3 NaN \n4 NaN \n... ... \n307506 NaN \n307507 NaN \n307508 3.250000 \n307509 0.000000 \n307510 1.545455 \n\n client_bureau_balance_STATUS_0_count_mean \\\n0 5.625000 \n1 NaN \n2 NaN \n3 NaN \n4 NaN \n... ... \n307506 NaN \n307507 NaN \n307508 11.750000 \n307509 8.000000 \n307510 5.727273 \n\n client_bureau_balance_STATUS_X_count_max \\\n0 3.0 \n1 NaN \n2 NaN \n3 NaN \n4 NaN \n... ... \n307506 NaN \n307507 NaN \n307508 13.0 \n307509 0.0 \n307510 2.0 \n\n client_bureau_balance_STATUS_0_count_max \\\n0 18.0 \n1 NaN \n2 NaN \n3 NaN \n4 NaN \n... ... \n307506 NaN \n307507 NaN \n307508 12.0 \n307509 8.0 \n307510 9.0 \n\n client_bureau_balance_STATUS_C_count_mean \\\n0 2.875000 \n1 NaN \n2 NaN \n3 NaN \n4 NaN \n... ... \n307506 NaN \n307507 NaN \n307508 14.250000 \n307509 29.000000 \n307510 19.363636 \n\n client_bureau_balance_STATUS_X_count_sum \\\n0 15.0 \n1 0.0 \n2 0.0 \n3 NaN \n4 0.0 \n... ... \n307506 NaN \n307507 NaN \n307508 13.0 \n307509 0.0 \n307510 17.0 \n\n client_bureau_balance_STATUS_0_count_sum \\\n0 45.0 \n1 0.0 \n2 0.0 \n3 NaN \n4 0.0 \n... ... \n307506 NaN \n307507 NaN \n307508 47.0 \n307509 8.0 \n307510 63.0 \n\n client_bureau_balance_STATUS_C_count_max \n0 13.0 \n1 NaN \n2 NaN \n3 NaN \n4 NaN \n... ... \n307506 NaN \n307507 NaN \n307508 19.0 \n307509 29.0 \n307510 33.0 \n\n[307511 rows x 236 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>NAME_CONTRACT_TYPE</th>\n <th>CODE_GENDER</th>\n <th>FLAG_OWN_CAR</th>\n <th>FLAG_OWN_REALTY</th>\n <th>CNT_CHILDREN</th>\n <th>AMT_INCOME_TOTAL</th>\n <th>AMT_CREDIT</th>\n <th>AMT_ANNUITY</th>\n <th>NAME_TYPE_SUITE</th>\n <th>NAME_INCOME_TYPE</th>\n <th>...</th>\n <th>client_bureau_balance_STATUS_0_count_norm_sum</th>\n <th>client_bureau_balance_STATUS_C_count_norm_sum</th>\n <th>client_bureau_balance_STATUS_X_count_mean</th>\n <th>client_bureau_balance_STATUS_0_count_mean</th>\n <th>client_bureau_balance_STATUS_X_count_max</th>\n <th>client_bureau_balance_STATUS_0_count_max</th>\n <th>client_bureau_balance_STATUS_C_count_mean</th>\n <th>client_bureau_balance_STATUS_X_count_sum</th>\n <th>client_bureau_balance_STATUS_0_count_sum</th>\n <th>client_bureau_balance_STATUS_C_count_max</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Cash loans</td>\n <td>M</td>\n <td>N</td>\n <td>Y</td>\n <td>0</td>\n <td>202500.0</td>\n <td>406597.5</td>\n <td>24700.5</td>\n <td>Unaccompanied</td>\n <td>Working</td>\n <td>...</td>\n <td>3.255682</td>\n <td>1.403409</td>\n <td>1.875000</td>\n <td>5.625000</td>\n <td>3.0</td>\n <td>18.0</td>\n <td>2.875000</td>\n <td>15.0</td>\n <td>45.0</td>\n <td>13.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Cash loans</td>\n <td>F</td>\n <td>N</td>\n <td>N</td>\n <td>0</td>\n <td>270000.0</td>\n <td>1293502.5</td>\n <td>35698.5</td>\n <td>Family</td>\n <td>State servant</td>\n <td>...</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Revolving loans</td>\n <td>M</td>\n <td>Y</td>\n <td>Y</td>\n <td>0</td>\n <td>67500.0</td>\n <td>135000.0</td>\n <td>6750.0</td>\n <td>Unaccompanied</td>\n <td>Working</td>\n <td>...</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Cash loans</td>\n <td>F</td>\n <td>N</td>\n <td>Y</td>\n <td>0</td>\n <td>135000.0</td>\n <td>312682.5</td>\n <td>29686.5</td>\n <td>Unaccompanied</td>\n <td>Working</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Cash loans</td>\n <td>M</td>\n <td>N</td>\n <td>Y</td>\n <td>0</td>\n <td>121500.0</td>\n <td>513000.0</td>\n <td>21865.5</td>\n <td>Unaccompanied</td>\n <td>Working</td>\n <td>...</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>307506</th>\n <td>Cash loans</td>\n <td>M</td>\n <td>N</td>\n <td>N</td>\n <td>0</td>\n <td>157500.0</td>\n <td>254700.0</td>\n <td>27558.0</td>\n <td>Unaccompanied</td>\n <td>Working</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>307507</th>\n <td>Cash loans</td>\n <td>F</td>\n <td>N</td>\n <td>Y</td>\n <td>0</td>\n <td>72000.0</td>\n <td>269550.0</td>\n <td>12001.5</td>\n <td>Unaccompanied</td>\n <td>Pensioner</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>307508</th>\n <td>Cash loans</td>\n <td>F</td>\n <td>N</td>\n <td>Y</td>\n <td>0</td>\n <td>153000.0</td>\n <td>677664.0</td>\n <td>29979.0</td>\n <td>Unaccompanied</td>\n <td>Working</td>\n <td>...</td>\n <td>1.619624</td>\n <td>1.838710</td>\n <td>3.250000</td>\n <td>11.750000</td>\n <td>13.0</td>\n <td>12.0</td>\n <td>14.250000</td>\n <td>13.0</td>\n <td>47.0</td>\n <td>19.0</td>\n </tr>\n <tr>\n <th>307509</th>\n <td>Cash loans</td>\n <td>F</td>\n <td>N</td>\n <td>Y</td>\n <td>0</td>\n <td>171000.0</td>\n <td>370107.0</td>\n <td>20205.0</td>\n <td>Unaccompanied</td>\n <td>Commercial associate</td>\n <td>...</td>\n <td>0.216216</td>\n <td>0.783784</td>\n <td>0.000000</td>\n <td>8.000000</td>\n <td>0.0</td>\n <td>8.0</td>\n <td>29.000000</td>\n <td>0.0</td>\n <td>8.0</td>\n <td>29.0</td>\n </tr>\n <tr>\n <th>307510</th>\n <td>Cash loans</td>\n <td>F</td>\n <td>N</td>\n <td>N</td>\n <td>0</td>\n <td>157500.0</td>\n <td>675000.0</td>\n <td>49117.5</td>\n <td>Unaccompanied</td>\n <td>Commercial associate</td>\n <td>...</td>\n <td>3.115737</td>\n <td>7.101613</td>\n <td>1.545455</td>\n <td>5.727273</td>\n <td>2.0</td>\n <td>9.0</td>\n <td>19.363636</td>\n <td>17.0</td>\n <td>63.0</td>\n <td>33.0</td>\n </tr>\n </tbody>\n</table>\n<p>307511 rows × 236 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train = pd.get_dummies(train_application_bureau_dropped)\ntest = pd.get_dummies(test_application_bureau_dropped)","execution_count":18,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.head()","execution_count":25,"outputs":[{"output_type":"execute_result","execution_count":25,"data":{"text/plain":" CNT_CHILDREN AMT_INCOME_TOTAL AMT_CREDIT AMT_ANNUITY \\\n0 0 202500.0 406597.5 24700.5 \n1 0 270000.0 1293502.5 35698.5 \n2 0 67500.0 135000.0 6750.0 \n3 0 135000.0 312682.5 29686.5 \n4 0 121500.0 513000.0 21865.5 \n\n REGION_POPULATION_RELATIVE DAYS_BIRTH DAYS_EMPLOYED DAYS_REGISTRATION \\\n0 0.018801 -9461 -637 -3648.0 \n1 0.003541 -16765 -1188 -1186.0 \n2 0.010032 -19046 -225 -4260.0 \n3 0.008019 -19005 -3039 -9833.0 \n4 0.028663 -19932 -3038 -4311.0 \n\n DAYS_ID_PUBLISH OWN_CAR_AGE ... HOUSETYPE_MODE_terraced house \\\n0 -2120 NaN ... 0 \n1 -291 NaN ... 0 \n2 -2531 26.0 ... 0 \n3 -2437 NaN ... 0 \n4 -3458 NaN ... 0 \n\n WALLSMATERIAL_MODE_Block WALLSMATERIAL_MODE_Mixed \\\n0 0 0 \n1 1 0 \n2 0 0 \n3 0 0 \n4 0 0 \n\n WALLSMATERIAL_MODE_Monolithic WALLSMATERIAL_MODE_Others \\\n0 0 0 \n1 0 0 \n2 0 0 \n3 0 0 \n4 0 0 \n\n WALLSMATERIAL_MODE_Panel WALLSMATERIAL_MODE_Stone, brick \\\n0 0 1 \n1 0 0 \n2 0 0 \n3 0 0 \n4 0 0 \n\n WALLSMATERIAL_MODE_Wooden EMERGENCYSTATE_MODE_No EMERGENCYSTATE_MODE_Yes \n0 0 1 0 \n1 0 1 0 \n2 0 0 0 \n3 0 0 0 \n4 0 0 0 \n\n[5 rows x 360 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>CNT_CHILDREN</th>\n <th>AMT_INCOME_TOTAL</th>\n <th>AMT_CREDIT</th>\n <th>AMT_ANNUITY</th>\n <th>REGION_POPULATION_RELATIVE</th>\n <th>DAYS_BIRTH</th>\n <th>DAYS_EMPLOYED</th>\n <th>DAYS_REGISTRATION</th>\n <th>DAYS_ID_PUBLISH</th>\n <th>OWN_CAR_AGE</th>\n <th>...</th>\n <th>HOUSETYPE_MODE_terraced house</th>\n <th>WALLSMATERIAL_MODE_Block</th>\n <th>WALLSMATERIAL_MODE_Mixed</th>\n <th>WALLSMATERIAL_MODE_Monolithic</th>\n <th>WALLSMATERIAL_MODE_Others</th>\n <th>WALLSMATERIAL_MODE_Panel</th>\n <th>WALLSMATERIAL_MODE_Stone, brick</th>\n <th>WALLSMATERIAL_MODE_Wooden</th>\n <th>EMERGENCYSTATE_MODE_No</th>\n <th>EMERGENCYSTATE_MODE_Yes</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>202500.0</td>\n <td>406597.5</td>\n <td>24700.5</td>\n <td>0.018801</td>\n <td>-9461</td>\n <td>-637</td>\n <td>-3648.0</td>\n <td>-2120</td>\n <td>NaN</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>0</td>\n <td>270000.0</td>\n <td>1293502.5</td>\n <td>35698.5</td>\n <td>0.003541</td>\n <td>-16765</td>\n <td>-1188</td>\n <td>-1186.0</td>\n <td>-291</td>\n <td>NaN</td>\n <td>...</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>0</td>\n <td>67500.0</td>\n <td>135000.0</td>\n <td>6750.0</td>\n <td>0.010032</td>\n <td>-19046</td>\n <td>-225</td>\n <td>-4260.0</td>\n <td>-2531</td>\n <td>26.0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>0</td>\n <td>135000.0</td>\n <td>312682.5</td>\n <td>29686.5</td>\n <td>0.008019</td>\n <td>-19005</td>\n <td>-3039</td>\n <td>-9833.0</td>\n <td>-2437</td>\n <td>NaN</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>0</td>\n <td>121500.0</td>\n <td>513000.0</td>\n <td>21865.5</td>\n <td>0.028663</td>\n <td>-19932</td>\n <td>-3038</td>\n <td>-4311.0</td>\n <td>-3458</td>\n <td>NaN</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 360 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Match the columns in the dataframes\ntrain, test = train.align(test, join = 'inner', axis = 1)\nprint('Training shape: ', train.shape)\nprint('Testing shape: ', test.shape)","execution_count":60,"outputs":[{"output_type":"stream","text":"Training shape: (307511, 357)\nTesting shape: (48744, 357)\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## feature importance"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Initialize an empty array to hold feature importances\nfeature_importances = np.zeros(train.shape[1])\n\n# Create the model with several hyperparameters\nmodel = lgb.LGBMClassifier(objective='binary', boosting_type = 'goss', n_estimators = 10000, class_weight = 'balanced')","execution_count":70,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import re\ntrain_re = train.rename(columns = lambda x:re.sub('[^A-Za-z0-9_]+', '', x))\ntest_re = test.rename(columns = lambda x:re.sub('[^A-Za-z0-9_]+', '', x))","execution_count":71,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_re","execution_count":72,"outputs":[{"output_type":"execute_result","execution_count":72,"data":{"text/plain":" CNT_CHILDREN AMT_INCOME_TOTAL AMT_CREDIT AMT_ANNUITY \\\n0 0 135000.0 568800.0 20560.5 \n1 0 99000.0 222768.0 17370.0 \n2 0 202500.0 663264.0 69777.0 \n3 2 315000.0 1575000.0 49018.5 \n4 1 180000.0 625500.0 32067.0 \n... ... ... ... ... \n48739 0 121500.0 412560.0 17473.5 \n48740 2 157500.0 622413.0 31909.5 \n48741 1 202500.0 315000.0 33205.5 \n48742 0 225000.0 450000.0 25128.0 \n48743 0 135000.0 312768.0 24709.5 \n\n REGION_POPULATION_RELATIVE DAYS_BIRTH DAYS_EMPLOYED \\\n0 0.018850 -19241 -2329 \n1 0.035792 -18064 -4469 \n2 0.019101 -20038 -4458 \n3 0.026392 -13976 -1866 \n4 0.010032 -13040 -2191 \n... ... ... ... \n48739 0.002042 -19970 -5169 \n48740 0.035792 -11186 -1149 \n48741 0.026392 -15922 -3037 \n48742 0.018850 -13968 -2731 \n48743 0.006629 -13962 -633 \n\n DAYS_REGISTRATION DAYS_ID_PUBLISH OWN_CAR_AGE ... \\\n0 -5170.0 -812 NaN ... \n1 -9118.0 -1623 NaN ... \n2 -2175.0 -3503 5.0 ... \n3 -2000.0 -4208 NaN ... \n4 -4000.0 -4262 16.0 ... \n... ... ... ... ... \n48739 -9094.0 -3399 NaN ... \n48740 -3015.0 -3003 NaN ... \n48741 -2681.0 -1504 4.0 ... \n48742 -1461.0 -1364 NaN ... \n48743 -1072.0 -4220 22.0 ... \n\n HOUSETYPE_MODE_terracedhouse WALLSMATERIAL_MODE_Block \\\n0 0 0 \n1 0 0 \n2 0 0 \n3 0 0 \n4 0 0 \n... ... ... \n48739 0 0 \n48740 0 0 \n48741 0 0 \n48742 0 0 \n48743 0 0 \n\n WALLSMATERIAL_MODE_Mixed WALLSMATERIAL_MODE_Monolithic \\\n0 0 0 \n1 0 0 \n2 0 0 \n3 0 0 \n4 0 0 \n... ... ... \n48739 0 0 \n48740 0 0 \n48741 0 0 \n48742 0 0 \n48743 0 0 \n\n WALLSMATERIAL_MODE_Others WALLSMATERIAL_MODE_Panel \\\n0 0 0 \n1 0 0 \n2 0 0 \n3 0 1 \n4 0 0 \n... ... ... \n48739 0 0 \n48740 0 0 \n48741 0 0 \n48742 0 1 \n48743 0 0 \n\n WALLSMATERIAL_MODE_Stonebrick WALLSMATERIAL_MODE_Wooden \\\n0 1 0 \n1 0 0 \n2 0 0 \n3 0 0 \n4 0 0 \n... ... ... \n48739 0 0 \n48740 0 0 \n48741 1 0 \n48742 0 0 \n48743 0 0 \n\n EMERGENCYSTATE_MODE_No EMERGENCYSTATE_MODE_Yes \n0 1 0 \n1 0 0 \n2 0 0 \n3 1 0 \n4 0 0 \n... ... ... \n48739 0 0 \n48740 0 0 \n48741 1 0 \n48742 1 0 \n48743 0 0 \n\n[48744 rows x 357 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>CNT_CHILDREN</th>\n <th>AMT_INCOME_TOTAL</th>\n <th>AMT_CREDIT</th>\n <th>AMT_ANNUITY</th>\n <th>REGION_POPULATION_RELATIVE</th>\n <th>DAYS_BIRTH</th>\n <th>DAYS_EMPLOYED</th>\n <th>DAYS_REGISTRATION</th>\n <th>DAYS_ID_PUBLISH</th>\n <th>OWN_CAR_AGE</th>\n <th>...</th>\n <th>HOUSETYPE_MODE_terracedhouse</th>\n <th>WALLSMATERIAL_MODE_Block</th>\n <th>WALLSMATERIAL_MODE_Mixed</th>\n <th>WALLSMATERIAL_MODE_Monolithic</th>\n <th>WALLSMATERIAL_MODE_Others</th>\n <th>WALLSMATERIAL_MODE_Panel</th>\n <th>WALLSMATERIAL_MODE_Stonebrick</th>\n <th>WALLSMATERIAL_MODE_Wooden</th>\n <th>EMERGENCYSTATE_MODE_No</th>\n <th>EMERGENCYSTATE_MODE_Yes</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>135000.0</td>\n <td>568800.0</td>\n <td>20560.5</td>\n <td>0.018850</td>\n <td>-19241</td>\n <td>-2329</td>\n <td>-5170.0</td>\n <td>-812</td>\n <td>NaN</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>0</td>\n <td>99000.0</td>\n <td>222768.0</td>\n <td>17370.0</td>\n <td>0.035792</td>\n <td>-18064</td>\n <td>-4469</td>\n <td>-9118.0</td>\n <td>-1623</td>\n <td>NaN</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>0</td>\n <td>202500.0</td>\n <td>663264.0</td>\n <td>69777.0</td>\n <td>0.019101</td>\n <td>-20038</td>\n <td>-4458</td>\n <td>-2175.0</td>\n <td>-3503</td>\n <td>5.0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2</td>\n <td>315000.0</td>\n <td>1575000.0</td>\n <td>49018.5</td>\n <td>0.026392</td>\n <td>-13976</td>\n <td>-1866</td>\n <td>-2000.0</td>\n <td>-4208</td>\n <td>NaN</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1</td>\n <td>180000.0</td>\n <td>625500.0</td>\n <td>32067.0</td>\n <td>0.010032</td>\n <td>-13040</td>\n <td>-2191</td>\n <td>-4000.0</td>\n <td>-4262</td>\n <td>16.0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>48739</th>\n <td>0</td>\n <td>121500.0</td>\n <td>412560.0</td>\n <td>17473.5</td>\n <td>0.002042</td>\n <td>-19970</td>\n <td>-5169</td>\n <td>-9094.0</td>\n <td>-3399</td>\n <td>NaN</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>48740</th>\n <td>2</td>\n <td>157500.0</td>\n <td>622413.0</td>\n <td>31909.5</td>\n <td>0.035792</td>\n <td>-11186</td>\n <td>-1149</td>\n <td>-3015.0</td>\n <td>-3003</td>\n <td>NaN</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>48741</th>\n <td>1</td>\n <td>202500.0</td>\n <td>315000.0</td>\n <td>33205.5</td>\n <td>0.026392</td>\n <td>-15922</td>\n <td>-3037</td>\n <td>-2681.0</td>\n <td>-1504</td>\n <td>4.0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>48742</th>\n <td>0</td>\n <td>225000.0</td>\n <td>450000.0</td>\n <td>25128.0</td>\n <td>0.018850</td>\n <td>-13968</td>\n <td>-2731</td>\n <td>-1461.0</td>\n <td>-1364</td>\n <td>NaN</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>48743</th>\n <td>0</td>\n <td>135000.0</td>\n <td>312768.0</td>\n <td>24709.5</td>\n <td>0.006629</td>\n <td>-13962</td>\n <td>-633</td>\n <td>-1072.0</td>\n <td>-4220</td>\n <td>22.0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n<p>48744 rows × 357 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Fit the model twice to avoid overfitting\nfor i in range(2):\n \n # Split into training and validation set\n train_features, valid_features, train_y, valid_y = train_test_split(train_re, train_application_bureau_label, test_size = 0.25, random_state = i)\n \n # Train using early stopping\n model.fit(train_features, train_y, early_stopping_rounds=100, eval_set = [(valid_features, valid_y)], \n eval_metric = 'auc', verbose = 200)\n \n # Record the feature importances\n feature_importances += model.feature_importances_","execution_count":73,"outputs":[{"output_type":"stream","text":"Training until validation scores don't improve for 100 rounds\n[200]\tvalid_0's auc: 0.765183\tvalid_0's binary_logloss: 0.534105\nEarly stopping, best iteration is:\n[163]\tvalid_0's auc: 0.76546\tvalid_0's binary_logloss: 0.542138\nTraining until validation scores don't improve for 100 rounds\n[200]\tvalid_0's auc: 0.76323\tvalid_0's binary_logloss: 0.534144\nEarly stopping, best iteration is:\n[113]\tvalid_0's auc: 0.764531\tvalid_0's binary_logloss: 0.554124\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Make sure to average feature importances! \nfeature_importances = feature_importances / 2\nfeature_importances = pd.DataFrame({'feature': list(train_re.columns), 'importance': feature_importances}).sort_values('importance', ascending = False)","execution_count":75,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"feature_importances","execution_count":76,"outputs":[{"output_type":"execute_result","execution_count":76,"data":{"text/plain":" feature importance\n24 EXT_SOURCE_1 107.00\n25 EXT_SOURCE_2 94.50\n26 EXT_SOURCE_3 91.75\n5 DAYS_BIRTH 81.00\n2 AMT_CREDIT 75.50\n.. ... ...\n159 client_bureau_balance_STATUS_4_count_sum 0.00\n275 OCCUPATION_TYPE_Waitersbarmenstaff 0.00\n160 client_bureau_balance_STATUS_5_count_sum 0.00\n161 client_bureau_balance_STATUS_3_count_norm_sum 0.00\n178 client_bureau_balance_STATUS_5_count_max 0.00\n\n[357 rows x 2 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>feature</th>\n <th>importance</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>24</th>\n <td>EXT_SOURCE_1</td>\n <td>107.00</td>\n </tr>\n <tr>\n <th>25</th>\n <td>EXT_SOURCE_2</td>\n <td>94.50</td>\n </tr>\n <tr>\n <th>26</th>\n <td>EXT_SOURCE_3</td>\n <td>91.75</td>\n </tr>\n <tr>\n <th>5</th>\n <td>DAYS_BIRTH</td>\n <td>81.00</td>\n </tr>\n <tr>\n <th>2</th>\n <td>AMT_CREDIT</td>\n <td>75.50</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>159</th>\n <td>client_bureau_balance_STATUS_4_count_sum</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>275</th>\n <td>OCCUPATION_TYPE_Waitersbarmenstaff</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>160</th>\n <td>client_bureau_balance_STATUS_5_count_sum</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>161</th>\n <td>client_bureau_balance_STATUS_3_count_norm_sum</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>178</th>\n <td>client_bureau_balance_STATUS_5_count_max</td>\n <td>0.00</td>\n </tr>\n </tbody>\n</table>\n<p>357 rows × 2 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Find the features with zero importance\nzero_features = list(feature_importances[feature_importances['importance'] == 0.0]['feature'])\nprint('There are %d features with 0.0 importance' % len(zero_features))\nfeature_importances.tail()","execution_count":77,"outputs":[{"output_type":"stream","text":"There are 103 features with 0.0 importance\n","name":"stdout"},{"output_type":"execute_result","execution_count":77,"data":{"text/plain":" feature importance\n159 client_bureau_balance_STATUS_4_count_sum 0.0\n275 OCCUPATION_TYPE_Waitersbarmenstaff 0.0\n160 client_bureau_balance_STATUS_5_count_sum 0.0\n161 client_bureau_balance_STATUS_3_count_norm_sum 0.0\n178 client_bureau_balance_STATUS_5_count_max 0.0","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>feature</th>\n <th>importance</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>159</th>\n <td>client_bureau_balance_STATUS_4_count_sum</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>275</th>\n <td>OCCUPATION_TYPE_Waitersbarmenstaff</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>160</th>\n <td>client_bureau_balance_STATUS_5_count_sum</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>161</th>\n <td>client_bureau_balance_STATUS_3_count_norm_sum</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>178</th>\n <td>client_bureau_balance_STATUS_5_count_max</td>\n <td>0.0</td>\n </tr>\n </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_re = train_re.drop(columns = zero_features)\ntest_re = test_re.drop(columns = zero_features)\n","execution_count":78,"outputs":[{"output_type":"stream","text":"Training shape: (307511, 357)\nTesting shape: (48744, 357)\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('Training shape: ', train_re.shape)\nprint('Testing shape: ', test_re.shape)","execution_count":86,"outputs":[{"output_type":"stream","text":"Training shape: (307511, 254)\nTesting shape: (48744, 254)\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_re.to_csv('train_application_bureau_drop_0importance.csv',index=False)\ntest_re.to_csv('test_application_bureau_drop_0importance.csv',index=False)","execution_count":87,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def plot_feature_importances(df, threshold = 0.9):\n \"\"\"\n Plots 15 most important features and the cumulative importance of features.\n Prints the number of features needed to reach threshold cumulative importance.\n \n Parameters\n --------\n df : dataframe\n Dataframe of feature importances. Columns must be feature and importance\n threshold : float, default = 0.9\n Threshold for prining information about cumulative importances\n \n Return\n --------\n df : dataframe\n Dataframe ordered by feature importances with a normalized column (sums to 1)\n and a cumulative importance column\n \n \"\"\"\n \n plt.rcParams['font.size'] = 18\n \n # Sort features according to importance\n df = df.sort_values('importance', ascending = False).reset_index()\n \n # Normalize the feature importances to add up to one\n df['importance_normalized'] = df['importance'] / df['importance'].sum()\n df['cumulative_importance'] = np.cumsum(df['importance_normalized'])\n\n # Make a horizontal bar chart of feature importances\n plt.figure(figsize = (10, 6))\n ax = plt.subplot()\n \n # Need to reverse the index to plot most important on top\n ax.barh(list(reversed(list(df.index[:15]))), \n df['importance_normalized'].head(15), \n align = 'center', edgecolor = 'k')\n \n # Set the yticks and labels\n ax.set_yticks(list(reversed(list(df.index[:15]))))\n ax.set_yticklabels(df['feature'].head(15))\n \n # Plot labeling\n plt.xlabel('Normalized Importance'); plt.title('Feature Importances')\n plt.show()\n \n # Cumulative importance plot\n plt.figure(figsize = (8, 6))\n plt.plot(list(range(len(df))), df['cumulative_importance'], 'r-')\n plt.xlabel('Number of Features'); plt.ylabel('Cumulative Importance'); \n plt.title('Cumulative Feature Importance');\n plt.show();\n \n importance_index = np.min(np.where(df['cumulative_importance'] > threshold))\n print('%d features required for %0.2f of cumulative importance' % (importance_index + 1, threshold))\n \n return df","execution_count":84,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"norm_feature_importances = plot_feature_importances(feature_importances)","execution_count":85,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 720x432 with 1 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","text":"104 features required for 0.90 of cumulative importance\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"def model(features, test_features, encoding = 'ohe', n_folds = 5):\n \n \"\"\"Train and test a light gradient boosting model using\n cross validation. \n \n Parameters\n --------\n features (pd.DataFrame): \n dataframe of training features to use \n for training a model. Must include the TARGET column.\n test_features (pd.DataFrame): \n dataframe of testing features to use\n for making predictions with the model. \n encoding (str, default = 'ohe'): \n method for encoding categorical variables. Either 'ohe' for one-hot encoding or 'le' for integer label encoding\n n_folds (int, default = 5): number of folds to use for cross validation\n \n Return\n --------\n submission (pd.DataFrame): \n dataframe with `SK_ID_CURR` and `TARGET` probabilities\n predicted by the model.\n feature_importances (pd.DataFrame): \n dataframe with the feature importances from the model.\n valid_metrics (pd.DataFrame): \n dataframe with training and validation metrics (ROC AUC) for each fold and overall.\n \n \"\"\"\n \n # Extract the ids\n train_ids = features['SK_ID_CURR']\n test_ids = test_features['SK_ID_CURR']\n \n # Extract the labels for training\n labels = features['TARGET']\n \n # Remove the ids and target\n features = features.drop(columns = ['SK_ID_CURR', 'TARGET'])\n test_features = test_features.drop(columns = ['SK_ID_CURR'])\n \n \n # One Hot Encoding\n if encoding == 'ohe':\n features = pd.get_dummies(features)\n test_features = pd.get_dummies(test_features)\n \n # Align the dataframes by the columns\n features, test_features = features.align(test_features, join = 'inner', axis = 1)\n \n # No categorical indices to record\n cat_indices = 'auto'\n \n # Integer label encoding\n elif encoding == 'le':\n \n # Create a label encoder\n label_encoder = LabelEncoder()\n \n # List for storing categorical indices\n cat_indices = []\n \n # Iterate through each column\n for i, col in enumerate(features):\n if features[col].dtype == 'object':\n # Map the categorical features to integers\n features[col] = label_encoder.fit_transform(np.array(features[col].astype(str)).reshape((-1,)))\n test_features[col] = label_encoder.transform(np.array(test_features[col].astype(str)).reshape((-1,)))\n\n # Record the categorical indices\n cat_indices.append(i)\n \n # Catch error if label encoding scheme is not valid\n else:\n raise ValueError(\"Encoding must be either 'ohe' or 'le'\")\n \n print('Training Data Shape: ', features.shape)\n print('Testing Data Shape: ', test_features.shape)\n \n # Extract feature names\n feature_names = list(features.columns)\n \n # Convert to np arrays\n features = np.array(features)\n test_features = np.array(test_features)\n \n # Create the kfold object\n k_fold = KFold(n_splits = n_folds, shuffle = False, random_state = 50)\n \n # Empty array for feature importances\n feature_importance_values = np.zeros(len(feature_names))\n \n # Empty array for test predictions\n test_predictions = np.zeros(test_features.shape[0])\n \n # Empty array for out of fold validation predictions\n out_of_fold = np.zeros(features.shape[0])\n \n # Lists for recording validation and training scores\n valid_scores = []\n train_scores = []\n \n # Iterate through each fold\n for train_indices, valid_indices in k_fold.split(features):\n \n # Training data for the fold\n train_features, train_labels = features[train_indices], labels[train_indices]\n # Validation data for the fold\n valid_features, valid_labels = features[valid_indices], labels[valid_indices]\n \n # Create the model\n model = lgb.LGBMClassifier(n_estimators=10000, objective = 'binary', \n class_weight = 'balanced', learning_rate = 0.05, \n reg_alpha = 0.1, reg_lambda = 0.1, \n subsample = 0.8, n_jobs = -1, random_state = 50)\n \n # Train the model\n model.fit(train_features, train_labels, eval_metric = 'auc',\n eval_set = [(valid_features, valid_labels), (train_features, train_labels)],\n eval_names = ['valid', 'train'], categorical_feature = cat_indices,\n early_stopping_rounds = 100, verbose = 200)\n \n # Record the best iteration\n best_iteration = model.best_iteration_\n \n # Record the feature importances\n feature_importance_values += model.feature_importances_ / k_fold.n_splits\n \n # Make predictions\n test_predictions += model.predict_proba(test_features, num_iteration = best_iteration)[:, 1] / k_fold.n_splits\n \n # Record the out of fold predictions\n out_of_fold[valid_indices] = model.predict_proba(valid_features, num_iteration = best_iteration)[:, 1]\n \n # Record the best score\n valid_score = model.best_score_['valid']['auc']\n train_score = model.best_score_['train']['auc']\n \n valid_scores.append(valid_score)\n train_scores.append(train_score)\n \n # Clean up memory\n gc.enable()\n del model, train_features, valid_features\n gc.collect()\n \n # Make the submission dataframe\n submission = pd.DataFrame({'SK_ID_CURR': test_ids, 'TARGET': test_predictions})\n \n # Make the feature importance dataframe\n feature_importances = pd.DataFrame({'feature': feature_names, 'importance': feature_importance_values})\n \n # Overall validation score\n valid_auc = roc_auc_score(labels, out_of_fold)\n \n # Add the overall scores to the metrics\n valid_scores.append(valid_auc)\n train_scores.append(np.mean(train_scores))\n \n # Needed for creating dataframe of validation scores\n fold_names = list(range(n_folds))\n fold_names.append('overall')\n \n # Dataframe of validation scores\n metrics = pd.DataFrame({'fold': fold_names,\n 'train': train_scores,\n 'valid': valid_scores}) \n \n return submission, feature_importances, metrics","execution_count":90,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_re['TARGET'] = train_application_bureau_label\ntrain_re['SK_ID_CURR'] = train_application_bureau['SK_ID_CURR']\ntest_re['SK_ID_CURR'] = test_application_bureau['SK_ID_CURR']","execution_count":88,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission, feature_importances, metrics = model(train_re, test_re)","execution_count":91,"outputs":[{"output_type":"stream","text":"Training Data Shape: (307511, 254)\nTesting Data Shape: (48744, 254)\n","name":"stdout"},{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_split.py:297: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n FutureWarning\n","name":"stderr"},{"output_type":"stream","text":"Training until validation scores don't improve for 100 rounds\n[200]\ttrain's auc: 0.807069\ttrain's binary_logloss: 0.540204\tvalid's auc: 0.764061\tvalid's binary_logloss: 0.555535\n[400]\ttrain's auc: 0.840056\ttrain's binary_logloss: 0.506957\tvalid's auc: 0.764467\tvalid's binary_logloss: 0.534449\nEarly stopping, best iteration is:\n[374]\ttrain's auc: 0.836444\ttrain's binary_logloss: 0.510673\tvalid's auc: 0.764969\tvalid's binary_logloss: 0.536708\nTraining until validation scores don't improve for 100 rounds\n[200]\ttrain's auc: 0.80745\ttrain's binary_logloss: 0.539914\tvalid's auc: 0.765111\tvalid's binary_logloss: 0.55932\n[400]\ttrain's auc: 0.840064\ttrain's binary_logloss: 0.506794\tvalid's auc: 0.765447\tvalid's binary_logloss: 0.538799\nEarly stopping, best iteration is:\n[350]\ttrain's auc: 0.83283\ttrain's binary_logloss: 0.514248\tvalid's auc: 0.765503\tvalid's binary_logloss: 0.543441\nTraining until validation scores don't improve for 100 rounds\n[200]\ttrain's auc: 0.808311\ttrain's binary_logloss: 0.538717\tvalid's auc: 0.757327\tvalid's binary_logloss: 0.556616\nEarly stopping, best iteration is:\n[240]\ttrain's auc: 0.815776\ttrain's binary_logloss: 0.531171\tvalid's auc: 0.757464\tvalid's binary_logloss: 0.551996\nTraining until validation scores don't improve for 100 rounds\n[200]\ttrain's auc: 0.806833\ttrain's binary_logloss: 0.540621\tvalid's auc: 0.764151\tvalid's binary_logloss: 0.555431\nEarly stopping, best iteration is:\n[229]\ttrain's auc: 0.81235\ttrain's binary_logloss: 0.535146\tvalid's auc: 0.764639\tvalid's binary_logloss: 0.551945\nTraining until validation scores don't improve for 100 rounds\n[200]\ttrain's auc: 0.807034\ttrain's binary_logloss: 0.540187\tvalid's auc: 0.764578\tvalid's binary_logloss: 0.555904\n[400]\ttrain's auc: 0.839796\ttrain's binary_logloss: 0.507048\tvalid's auc: 0.764242\tvalid's binary_logloss: 0.535507\nEarly stopping, best iteration is:\n[309]\ttrain's auc: 0.826086\ttrain's binary_logloss: 0.521006\tvalid's auc: 0.765197\tvalid's binary_logloss: 0.544036\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission.to_csv('application_bureau_submission.csv', index = False)\n#public score: 0.75644","execution_count":92,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"norm_feature_importances = plot_feature_importances(feature_importances)","execution_count":99,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 720x432 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","text":"101 features required for 0.90 of cumulative importance\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"# Comparison"},{"metadata":{"trusted":true},"cell_type":"code","source":"train['TARGET'] = train_application_bureau_label\ntrain['SK_ID_CURR'] = train_application_bureau['SK_ID_CURR']\ntest['SK_ID_CURR'] = test_application_bureau['SK_ID_CURR']","execution_count":100,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission2, feature_importances2, metrics = model(train, test)","execution_count":101,"outputs":[{"output_type":"stream","text":"Training Data Shape: (307511, 357)\nTesting Data Shape: (48744, 357)\n","name":"stdout"},{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_split.py:297: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n FutureWarning\n","name":"stderr"},{"output_type":"stream","text":"Training until validation scores don't improve for 100 rounds\n[200]\ttrain's auc: 0.807006\ttrain's binary_logloss: 0.540279\tvalid's auc: 0.764149\tvalid's binary_logloss: 0.555317\nEarly stopping, best iteration is:\n[206]\ttrain's auc: 0.808205\ttrain's binary_logloss: 0.539063\tvalid's auc: 0.764408\tvalid's binary_logloss: 0.554531\nTraining until validation scores don't improve for 100 rounds\n[200]\ttrain's auc: 0.807427\ttrain's binary_logloss: 0.540015\tvalid's auc: 0.765474\tvalid's binary_logloss: 0.559257\n[400]\ttrain's auc: 0.84014\ttrain's binary_logloss: 0.506901\tvalid's auc: 0.76496\tvalid's binary_logloss: 0.538865\nEarly stopping, best iteration is:\n[306]\ttrain's auc: 0.826078\ttrain's binary_logloss: 0.521329\tvalid's auc: 0.765721\tvalid's binary_logloss: 0.54764\nTraining until validation scores don't improve for 100 rounds\n[200]\ttrain's auc: 0.808195\ttrain's binary_logloss: 0.53886\tvalid's auc: 0.757109\tvalid's binary_logloss: 0.556647\nEarly stopping, best iteration is:\n[234]\ttrain's auc: 0.814708\ttrain's binary_logloss: 0.532273\tvalid's auc: 0.757522\tvalid's binary_logloss: 0.552663\nTraining until validation scores don't improve for 100 rounds\n[200]\ttrain's auc: 0.807007\ttrain's binary_logloss: 0.540599\tvalid's auc: 0.763469\tvalid's binary_logloss: 0.555427\nEarly stopping, best iteration is:\n[188]\ttrain's auc: 0.804681\ttrain's binary_logloss: 0.543003\tvalid's auc: 0.763522\tvalid's binary_logloss: 0.556874\nTraining until validation scores don't improve for 100 rounds\n[200]\ttrain's auc: 0.806832\ttrain's binary_logloss: 0.540399\tvalid's auc: 0.76417\tvalid's binary_logloss: 0.556179\nEarly stopping, best iteration is:\n[275]\ttrain's auc: 0.820448\ttrain's binary_logloss: 0.526856\tvalid's auc: 0.764663\tvalid's binary_logloss: 0.547703\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission2.to_csv('application_bureau_submission2.csv', index = False)\n#0.75709","execution_count":102,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"feature_importances2","execution_count":103,"outputs":[{"output_type":"execute_result","execution_count":103,"data":{"text/plain":" feature importance\n0 CNT_CHILDREN 6.2\n1 AMT_INCOME_TOTAL 73.6\n2 AMT_CREDIT 298.8\n3 AMT_ANNUITY 223.0\n4 REGION_POPULATION_RELATIVE 78.4\n.. ... ...\n352 WALLSMATERIAL_MODE_Panel 7.2\n353 WALLSMATERIAL_MODE_Stone, brick 5.4\n354 WALLSMATERIAL_MODE_Wooden 0.8\n355 EMERGENCYSTATE_MODE_No 0.0\n356 EMERGENCYSTATE_MODE_Yes 0.0\n\n[357 rows x 2 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>feature</th>\n <th>importance</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>CNT_CHILDREN</td>\n <td>6.2</td>\n </tr>\n <tr>\n <th>1</th>\n <td>AMT_INCOME_TOTAL</td>\n <td>73.6</td>\n </tr>\n <tr>\n <th>2</th>\n <td>AMT_CREDIT</td>\n <td>298.8</td>\n </tr>\n <tr>\n <th>3</th>\n <td>AMT_ANNUITY</td>\n <td>223.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>REGION_POPULATION_RELATIVE</td>\n <td>78.4</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>352</th>\n <td>WALLSMATERIAL_MODE_Panel</td>\n <td>7.2</td>\n </tr>\n <tr>\n <th>353</th>\n <td>WALLSMATERIAL_MODE_Stone, brick</td>\n <td>5.4</td>\n </tr>\n <tr>\n <th>354</th>\n <td>WALLSMATERIAL_MODE_Wooden</td>\n <td>0.8</td>\n </tr>\n <tr>\n <th>355</th>\n <td>EMERGENCYSTATE_MODE_No</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>356</th>\n <td>EMERGENCYSTATE_MODE_Yes</td>\n <td>0.0</td>\n </tr>\n </tbody>\n</table>\n<p>357 rows × 2 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Find the features with zero importance\nzero_features = list(feature_importances2[feature_importances2['importance'] == 0.0]['feature'])\nprint('There are %d features with 0.0 importance' % len(zero_features))\nzero_features","execution_count":105,"outputs":[{"output_type":"stream","text":"There are 77 features with 0.0 importance\n","name":"stdout"},{"output_type":"execute_result","execution_count":105,"data":{"text/plain":"['FLAG_MOBIL',\n 'FLAG_CONT_MOBILE',\n 'FLAG_DOCUMENT_2',\n 'FLAG_DOCUMENT_4',\n 'FLAG_DOCUMENT_7',\n 'FLAG_DOCUMENT_9',\n 'FLAG_DOCUMENT_10',\n 'FLAG_DOCUMENT_12',\n 'FLAG_DOCUMENT_17',\n 'FLAG_DOCUMENT_19',\n 'FLAG_DOCUMENT_20',\n 'FLAG_DOCUMENT_21',\n 'AMT_REQ_CREDIT_BUREAU_HOUR',\n 'bureau_CREDIT_TYPE_Mobile operator loan_count_norm',\n 'bureau_CREDIT_TYPE_Loan for purchase of shares (margin lending)_count_norm',\n 'bureau_CREDIT_TYPE_Loan for purchase of shares (margin lending)_count',\n 'bureau_CREDIT_ACTIVE_Bad debt_count_norm',\n 'bureau_CREDIT_ACTIVE_Bad debt_count',\n 'bureau_CREDIT_TYPE_Interbank credit_count_norm',\n 'bureau_CREDIT_TYPE_Real estate loan_count_norm',\n 'bureau_CREDIT_TYPE_Real estate loan_count',\n 'bureau_CREDIT_CURRENCY_currency 4_count_norm',\n 'bureau_CREDIT_CURRENCY_currency 4_count',\n 'bureau_CREDIT_CURRENCY_currency 3_count',\n 'bureau_CREDIT_TYPE_Loan for the purchase of equipment_count_norm',\n 'bureau_CREDIT_TYPE_Loan for the purchase of equipment_count',\n 'bureau_CREDIT_TYPE_Cash loan (non-earmarked)_count_norm',\n 'bureau_CREDIT_TYPE_Cash loan (non-earmarked)_count',\n 'bureau_CREDIT_TYPE_Unknown type of loan_count',\n 'bureau_CREDIT_TYPE_Another type of loan_count',\n 'bureau_CREDIT_TYPE_Loan for working capital replenishment_count',\n 'bureau_CREDIT_CURRENCY_currency 2_count',\n 'bureau_CREDIT_ACTIVE_Sold_count',\n 'bureau_CREDIT_TYPE_Microloan_count',\n 'bureau_CNT_CREDIT_PROLONG_min',\n 'bureau_CREDIT_DAY_OVERDUE_min',\n 'client_bureau_balance_STATUS_4_count_norm_sum',\n 'client_bureau_balance_STATUS_4_count_sum',\n 'client_bureau_balance_STATUS_3_count_sum',\n 'client_bureau_balance_STATUS_2_count_sum',\n 'client_bureau_balance_STATUS_5_count_min',\n 'client_bureau_balance_STATUS_4_count_min',\n 'client_bureau_balance_STATUS_3_count_norm_min',\n 'client_bureau_balance_STATUS_3_count_min',\n 'client_bureau_balance_STATUS_4_count_mean',\n 'client_bureau_balance_STATUS_2_count_min',\n 'client_bureau_balance_STATUS_3_count_max',\n 'NAME_CONTRACT_TYPE_Revolving loans',\n 'NAME_TYPE_SUITE_Group of people',\n 'NAME_INCOME_TYPE_Businessman',\n 'NAME_INCOME_TYPE_Student',\n 'OCCUPATION_TYPE_HR staff',\n 'OCCUPATION_TYPE_IT staff',\n 'OCCUPATION_TYPE_Secretaries',\n 'ORGANIZATION_TYPE_Advertising',\n 'ORGANIZATION_TYPE_Electricity',\n 'ORGANIZATION_TYPE_Emergency',\n 'ORGANIZATION_TYPE_Housing',\n 'ORGANIZATION_TYPE_Industry: type 1',\n 'ORGANIZATION_TYPE_Industry: type 10',\n 'ORGANIZATION_TYPE_Industry: type 13',\n 'ORGANIZATION_TYPE_Industry: type 4',\n 'ORGANIZATION_TYPE_Industry: type 5',\n 'ORGANIZATION_TYPE_Industry: type 6',\n 'ORGANIZATION_TYPE_Industry: type 8',\n 'ORGANIZATION_TYPE_Mobile',\n 'ORGANIZATION_TYPE_Religion',\n 'ORGANIZATION_TYPE_Services',\n 'ORGANIZATION_TYPE_Telecom',\n 'ORGANIZATION_TYPE_Trade: type 1',\n 'ORGANIZATION_TYPE_Trade: type 4',\n 'ORGANIZATION_TYPE_Trade: type 5',\n 'ORGANIZATION_TYPE_XNA',\n 'HOUSETYPE_MODE_terraced house',\n 'WALLSMATERIAL_MODE_Mixed',\n 'EMERGENCYSTATE_MODE_No',\n 'EMERGENCYSTATE_MODE_Yes']"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_2 = train.drop(columns = zero_features)\ntest_2 = test.drop(columns = zero_features)","execution_count":106,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission3, feature_importances3, metrics = model(train_2, test_2)","execution_count":107,"outputs":[{"output_type":"stream","text":"Training Data Shape: (307511, 280)\nTesting Data Shape: (48744, 280)\n","name":"stdout"},{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_split.py:297: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n FutureWarning\n","name":"stderr"},{"output_type":"stream","text":"Training until validation scores don't improve for 100 rounds\n[200]\ttrain's auc: 0.807006\ttrain's binary_logloss: 0.540279\tvalid's auc: 0.764149\tvalid's binary_logloss: 0.555317\nEarly stopping, best iteration is:\n[233]\ttrain's auc: 0.813192\ttrain's binary_logloss: 0.534007\tvalid's auc: 0.764695\tvalid's binary_logloss: 0.551381\nTraining until validation scores don't improve for 100 rounds\n[200]\ttrain's auc: 0.807427\ttrain's binary_logloss: 0.540015\tvalid's auc: 0.765474\tvalid's binary_logloss: 0.559257\n[400]\ttrain's auc: 0.840416\ttrain's binary_logloss: 0.506593\tvalid's auc: 0.765236\tvalid's binary_logloss: 0.538743\nEarly stopping, best iteration is:\n[306]\ttrain's auc: 0.826078\ttrain's binary_logloss: 0.521329\tvalid's auc: 0.765721\tvalid's binary_logloss: 0.54764\nTraining until validation scores don't improve for 100 rounds\n[200]\ttrain's auc: 0.808195\ttrain's binary_logloss: 0.53886\tvalid's auc: 0.757109\tvalid's binary_logloss: 0.556647\nEarly stopping, best iteration is:\n[234]\ttrain's auc: 0.814708\ttrain's binary_logloss: 0.532273\tvalid's auc: 0.757522\tvalid's binary_logloss: 0.552663\nTraining until validation scores don't improve for 100 rounds\n[200]\ttrain's auc: 0.807007\ttrain's binary_logloss: 0.540599\tvalid's auc: 0.763469\tvalid's binary_logloss: 0.555427\nEarly stopping, best iteration is:\n[188]\ttrain's auc: 0.804681\ttrain's binary_logloss: 0.543003\tvalid's auc: 0.763522\tvalid's binary_logloss: 0.556874\nTraining until validation scores don't improve for 100 rounds\n[200]\ttrain's auc: 0.806832\ttrain's binary_logloss: 0.540399\tvalid's auc: 0.76417\tvalid's binary_logloss: 0.556179\nEarly stopping, best iteration is:\n[275]\ttrain's auc: 0.820448\ttrain's binary_logloss: 0.526856\tvalid's auc: 0.764663\tvalid's binary_logloss: 0.547703\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission3.to_csv('application_bureau_submission3.csv', index = False)\n#0.75717","execution_count":109,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4} | 0047/231/47231450.ipynb | s3://data-agents/kaggle-outputs/sharded/010_00047.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.012766,
"end_time": "2020-11-19T10:44:44.010824",
"exception": false,
"start_time": "2020-11-19T10:44:43.998058",
"status": "completed"
},
"tags": []
},
"source": [
"# Word cloud generation"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"execution": {
"iopub.execute_input": "2020-11-19T10:44:44.037514Z",
"iopub.status.busy": "2020-11-19T10:44:44.036871Z",
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},
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"start_time": "2020-11-19T10:44:44.021324",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/kaggle/input/email-spam-dataset/Email spam.csv\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load\n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the read-only \"../input/\" directory\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
"\n",
"import os\n",
"for dirname, _, filenames in os.walk('/kaggle/input'):\n",
" for filename in filenames:\n",
" print(os.path.join(dirname, filename))\n",
"\n",
"# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
"# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
"_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
"execution": {
"iopub.execute_input": "2020-11-19T10:44:44.078091Z",
"iopub.status.busy": "2020-11-19T10:44:44.077420Z",
"iopub.status.idle": "2020-11-19T10:44:44.139522Z",
"shell.execute_reply": "2020-11-19T10:44:44.139012Z"
},
"papermill": {
"duration": 0.076875,
"end_time": "2020-11-19T10:44:44.139638",
"exception": false,
"start_time": "2020-11-19T10:44:44.062763",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import regex as re\n",
"import sqlite3\n",
"from wordcloud import WordCloud"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T10:44:44.166603Z",
"iopub.status.busy": "2020-11-19T10:44:44.166024Z",
"iopub.status.idle": "2020-11-19T10:44:44.406795Z",
"shell.execute_reply": "2020-11-19T10:44:44.406274Z"
},
"papermill": {
"duration": 0.256644,
"end_time": "2020-11-19T10:44:44.406917",
"exception": false,
"start_time": "2020-11-19T10:44:44.150273",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"df=pd.read_csv(\"../input/email-spam-dataset/Email spam.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T10:44:44.443669Z",
"iopub.status.busy": "2020-11-19T10:44:44.442984Z",
"iopub.status.idle": "2020-11-19T10:44:44.450663Z",
"shell.execute_reply": "2020-11-19T10:44:44.450091Z"
},
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"duration": 0.033114,
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>text</th>\n",
" <th>spam</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Subject: naturally irresistible your corporate...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Subject: the stock trading gunslinger fanny i...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Subject: unbelievable new homes made easy im ...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Subject: 4 color printing special request add...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Subject: do not have money , get software cds ...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" text spam\n",
"0 Subject: naturally irresistible your corporate... 1\n",
"1 Subject: the stock trading gunslinger fanny i... 1\n",
"2 Subject: unbelievable new homes made easy im ... 1\n",
"3 Subject: 4 color printing special request add... 1\n",
"4 Subject: do not have money , get software cds ... 1"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T10:44:44.477832Z",
"iopub.status.busy": "2020-11-19T10:44:44.476992Z",
"iopub.status.idle": "2020-11-19T10:44:44.480641Z",
"shell.execute_reply": "2020-11-19T10:44:44.481096Z"
},
"papermill": {
"duration": 0.019284,
"end_time": "2020-11-19T10:44:44.481238",
"exception": false,
"start_time": "2020-11-19T10:44:44.461954",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"(5728, 2)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T10:44:44.517575Z",
"iopub.status.busy": "2020-11-19T10:44:44.516861Z",
"iopub.status.idle": "2020-11-19T10:44:44.521384Z",
"shell.execute_reply": "2020-11-19T10:44:44.520625Z"
},
"papermill": {
"duration": 0.028787,
"end_time": "2020-11-19T10:44:44.521536",
"exception": false,
"start_time": "2020-11-19T10:44:44.492749",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 5728 entries, 0 to 5727\n",
"Data columns (total 2 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 text 5728 non-null object\n",
" 1 spam 5728 non-null int64 \n",
"dtypes: int64(1), object(1)\n",
"memory usage: 89.6+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T10:44:44.552095Z",
"iopub.status.busy": "2020-11-19T10:44:44.551470Z",
"iopub.status.idle": "2020-11-19T10:44:44.572487Z",
"shell.execute_reply": "2020-11-19T10:44:44.571915Z"
},
"papermill": {
"duration": 0.03804,
"end_time": "2020-11-19T10:44:44.572592",
"exception": false,
"start_time": "2020-11-19T10:44:44.534552",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Spam count 1368\n",
"Not spam count 4360\n"
]
}
],
"source": [
"print('Spam count',len(df.loc[df['spam']==1]))\n",
"print('Not spam count',len(df.loc[df['spam']==0]))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T10:44:44.604364Z",
"iopub.status.busy": "2020-11-19T10:44:44.603697Z",
"iopub.status.idle": "2020-11-19T10:44:44.629026Z",
"shell.execute_reply": "2020-11-19T10:44:44.628507Z"
},
"papermill": {
"duration": 0.044014,
"end_time": "2020-11-19T10:44:44.629150",
"exception": false,
"start_time": "2020-11-19T10:44:44.585136",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(5728, 2)\n",
"(5695, 2)\n"
]
}
],
"source": [
"print(df.shape)\n",
"\n",
"df=df.drop_duplicates()\n",
"\n",
"print(df.shape)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T10:44:44.661088Z",
"iopub.status.busy": "2020-11-19T10:44:44.658705Z",
"iopub.status.idle": "2020-11-19T10:44:44.665438Z",
"shell.execute_reply": "2020-11-19T10:44:44.664940Z"
},
"papermill": {
"duration": 0.023732,
"end_time": "2020-11-19T10:44:44.665542",
"exception": false,
"start_time": "2020-11-19T10:44:44.641810",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"df = df.reset_index(inplace = False)[['text','spam']]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T10:44:44.699887Z",
"iopub.status.busy": "2020-11-19T10:44:44.698910Z",
"iopub.status.idle": "2020-11-19T10:44:44.702534Z",
"shell.execute_reply": "2020-11-19T10:44:44.702078Z"
},
"papermill": {
"duration": 0.024538,
"end_time": "2020-11-19T10:44:44.702644",
"exception": false,
"start_time": "2020-11-19T10:44:44.678106",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>text</th>\n",
" <th>spam</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Subject: naturally irresistible your corporate...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Subject: the stock trading gunslinger fanny i...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Subject: unbelievable new homes made easy im ...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Subject: 4 color printing special request add...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Subject: do not have money , get software cds ...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" text spam\n",
"0 Subject: naturally irresistible your corporate... 1\n",
"1 Subject: the stock trading gunslinger fanny i... 1\n",
"2 Subject: unbelievable new homes made easy im ... 1\n",
"3 Subject: 4 color printing special request add... 1\n",
"4 Subject: do not have money , get software cds ... 1"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T10:44:44.735896Z",
"iopub.status.busy": "2020-11-19T10:44:44.734835Z",
"iopub.status.idle": "2020-11-19T10:44:44.739244Z",
"shell.execute_reply": "2020-11-19T10:44:44.738718Z"
},
"papermill": {
"duration": 0.02329,
"end_time": "2020-11-19T10:44:44.739353",
"exception": false,
"start_time": "2020-11-19T10:44:44.716063",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 0])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['spam'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T10:44:44.774946Z",
"iopub.status.busy": "2020-11-19T10:44:44.774213Z",
"iopub.status.idle": "2020-11-19T10:44:46.782496Z",
"shell.execute_reply": "2020-11-19T10:44:46.781676Z"
},
"papermill": {
"duration": 2.029451,
"end_time": "2020-11-19T10:44:46.782625",
"exception": false,
"start_time": "2020-11-19T10:44:44.753174",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>index</th>\n",
" <th>text</th>\n",
" <th>spam</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>subject naturally irresistible your corporate ...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>subject the stock trading gunslinger fanny is ...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>subject unbelievable new homes made easy im wa...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" index text spam\n",
"0 0 subject naturally irresistible your corporate ... 1\n",
"1 1 subject the stock trading gunslinger fanny is ... 1\n",
"2 2 subject unbelievable new homes made easy im wa... 1"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clean_desc = []\n",
"for w in range(len(df.text)):\n",
" desc = df['text'][w].lower()\n",
" \n",
" #remove punctuation\n",
" desc = re.sub('[^a-zA-Z]', ' ', desc)\n",
" \n",
" #remove tags\n",
" desc=re.sub(\"</?.*?>\",\" <> \",desc)\n",
" \n",
" #remove digits and special chars\n",
" desc=re.sub(\"(\\\\d|\\\\W)+\",\" \",desc)\n",
" \n",
" clean_desc.append(desc)\n",
"#assign the cleaned descriptions to the data frame\n",
"df['text'] = clean_desc\n",
"df = df.reset_index() \n",
"df.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T10:44:46.816731Z",
"iopub.status.busy": "2020-11-19T10:44:46.816137Z",
"iopub.status.idle": "2020-11-19T10:44:46.818846Z",
"shell.execute_reply": "2020-11-19T10:44:46.818237Z"
},
"papermill": {
"duration": 0.02171,
"end_time": "2020-11-19T10:44:46.818955",
"exception": false,
"start_time": "2020-11-19T10:44:46.797245",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"stop_words = ['is','you','your','and', 'the', 'to', 'from', 'or', 'I', 'for', 'do', 'get', 'not', 'here', 'in', 'im', 'have', 'on', 're', 'new', 'subject']"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T10:44:46.856441Z",
"iopub.status.busy": "2020-11-19T10:44:46.855851Z",
"iopub.status.idle": "2020-11-19T10:44:47.733394Z",
"shell.execute_reply": "2020-11-19T10:44:47.733896Z"
},
"papermill": {
"duration": 0.900247,
"end_time": "2020-11-19T10:44:47.734042",
"exception": false,
"start_time": "2020-11-19T10:44:46.833795",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"word_cloud=WordCloud(width=800,height=800,background_color='black',stopwords=stop_words,min_font_size=20).generate(str(df['text']))\n",
"\n",
"fig=plt.figure(figsize=(8,8),facecolor=None)\n",
"plt.imshow(word_cloud)\n",
"plt.axis('off')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T10:44:47.779474Z",
"iopub.status.busy": "2020-11-19T10:44:47.778832Z",
"iopub.status.idle": "2020-11-19T10:44:49.516602Z",
"shell.execute_reply": "2020-11-19T10:44:49.517090Z"
},
"papermill": {
"duration": 1.765321,
"end_time": "2020-11-19T10:44:49.517240",
"exception": false,
"start_time": "2020-11-19T10:44:47.751919",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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//HSoCmo0jBIsnVNKZD6PHU/NnigxC8LQ0WorEYaOk8piDY6UDbzCb6JVu0kKTjqDNZQA+wpmV6rqPoeg35XZkTi5PHY8icxe5XMA0FTUSMg9bykWhG3j5As4mSxO8uaICRcHBin0zOC5uBRFwVy8CCEETiqF0dQ06+56ZaWbMFNSgkZzEwiBNTSCNTwy9QDHId/dTciZ3xIeLRZFq65yE1FsB7Otdco+QtPGFZ1eV+N+X6XvR1o2qXd3UVFfR+S+uzFbmknt3E3+7Dmskfh1/x6FEERkJac5ROckC0RIhWaW0i7WUkcrfXQxQv+05/ATooV2zsmjXOTchPUkwcCHPcOAnCczvkBjTNZcUWmHJnRCMsZJuZ8eJqweRSq0sZIlYhW1NHOOY2RxrWIHmyHG3kdBQ0npZkgxQPecruvgjJ8vQ4oiV1Ya4RchMjLFQbm9zHLskqdZLbZRSS31oo0+2VU26agRzfhFkBHZz3H5HjZu4lOSOAWZYy234yeIxLmqco/ZWBglKARGIFbmNpVICuk40r66ThCq7kPzh0E67nmcqYO5avjd5BpFQdoWVj6DY011g0yHtBxQBEZLI6EHbsfX0YZaEUWYOlg2TiZHsX+IzO7DpN95DyeZnpvguoavYxHBuzdjtjWiVkQQpuEqlHSGYt8Q2f3HSb/zHvZwYsbTGEuaiTx2L04mS+KplwGIfepD+DesQPGb2CNJUm/vZfRnryPzBfSWemKf+hC+5YsQuoYdT5LZfZjEM6/gpGd/yYSuYa5cQuiuzRhtDaixCMLQ3Vl9KuvKvO8o6e37sEfmnlotfCb+dR0Etq3DaKpDjYYQhgFCIC0LJ5PDTiSxegbIHj5F7tDJKzr/fOOk0+VWzSwIIdAqYkApu3Tj+ln3lYqC8JmMZaar4TAA9ujoFAtyXJ5UejxhZb5Q/H4Uvw+h61Q88ZFZ9xVCIHTDzfwsln7HUpLavhPpSCL33oWvox3f8mWlpKFTpHbtoXChqzyOOM9kGKVHnitzn0kcLnKWelqJUEm1aGBETq8EBYIheuni1JQ43KWxwPlkhAF6OFd2TQeHPs7TSjsqGkHC40rrZkBKSZc8NcV1WiBHv+ykQtTgI4CBOa7MBIIwUQSCBEPjCnCMFAny5PARwE+Q9DyXayyIElQNP4vu+hzBmlaEUBBCYFsFTr70v0kPXLlLVPOFaL39E8SaVzJy/iAXdj6NnZ9wsSiqTuXSTVQtuQ1/rA5FM7DyadKDnfQffZNk35nLzkhloUhg82oqPveoq/zEpGmdoaAaOko0hNneim/VUoaffHpWpQWgBP1EP/oAofu2uIoPJs6r4FqGEfecwW3rGPnuc+SOnJ5WVjUWIbB5FTJfILv3CIEtawjeucHNwAO06hjRx+7ByWTJ7DxI1VeewFjaMn49tSpG+OE7kZZF/McvgT39QKuEg8Q+/iDBuzcjDL38OYxZyNEQ5rJWAlvXMvLtn5I/cX7W5wCgVkSo+NxjBDavBk0tPy8gVAPFNNAqIhhtjQS2riP5i+2MfOe5GZXCdceRc7dkBAjV/S4K3Rcp9vTNuru0isjcpFl46Vi3WcP015SOc/WWlRAIZRpTRRHuO2TbZE+cmr7kYxLFvj6kUy6DtCxSb28ne+gwgTWrCWxcj9nSROj2rQQ3bST93j7iz/0cOzH/ExqJJEOKwjQWjY1FgmGiooqIrCzFrqY+PwebAXlx2kSU64VEMiR7p72mjUWRAio6GvMfA74WHGyGmf7dzpND4qCgTkm8mWB6c9n9bpgxoehaWBAl6BTz9Oz7OUaoEs0MUL/mfhTd5GpaQWhmkNZtHyfWspqR8wfp3PWTMgUoVI3GTY9S03EHxewo8c7D2MU8vkg1kcYOQrWLOPfOD0h0Hp71OkZLPaG7NqFEQzipDPlTF7D6h5CWjVYZxVyx2LWIFAX/ug6iH3+Q4SefnlGZCJ9BxRceJ3jHBoSqIm0Ha3CY/KkL2PEkQtPQG2owl7YgAj70lnqq/tWnGPqnH5M7eGJGOYXPJHjXJnyrlpLddwyrbwjfqqXorQ0ITSX8wDa0qhhGWwO5I6cpXujBWNSIuXwxKILgnRtIvroTe3Cqy00EfFR+4XECt693XV6WTb6rl8K5izjpDMI0MJrqXOVq6BiLmqj66qcY/F/fpXB2ZneMMHRin/4Qga1rXSu9UCR/oYdCZw9OKguKQA0H0eqr0ZvqUHwG0rLJHTq5cArwCpES7FQavRZyx08y8tPnL3dE2bsz5hJX/P4ZE8MUw3BLDq4CoWvTJtXIQgGnUEAIhdHX3phS8jH1ADnjd2InRkm+vZ3Uzt0YTQ0EN28iuGUToW1bEKrK4Le/f12+T1cBTj945nA9NiZ+3PFn6n4SOb7fjcK95vQTjnJlcHO1z3FjjTNZx67MYtJ/3U8lo3KYGpqooAYdc5IbVpSycf0UyJNhfjOfYYGUoHRs1/rqO4NQVKrat2Lq5lyPZuxhqmaAlq0fI9a6hpFz++nc+ROsfPnLWtG6lpqO20kPnuf8Oz+kkB5BSomi6lQt3UTzlo/SuOGDZAYvUMzO/IB9a5aBgNzhU8R/8HOKF/snXD6qil5bScUXP4xvdTtCUQhsWkXq1Z0UznZNPZkiCN/vtmQTqoqTL5B8eTvJl97BTqbdwU+A0HWM1gZin3kEc1kbaixCxecfY6B/CKtv+kwtIQSBDStJvryd+NO/cF2fTbXU/NsvodVUotVWEbpvK+l3DzDynedw0lnUigg1//ZLGG2NqLEIRnMd2UuVoIDwg7cT2LoOhMCOJ4n/8Odk3zuKk8u7MisCYRj4Viym4vOPo9VVodVWEfvkBxn4u28js9PHF/SmOgKbViEUBSebZ+S7z5F+94AbZ7IdV+EqCkLXUKMhV6k31JA7fm7G7+umw3EodHZjLl6E0dxYmkTM3fVf7HNn11p1FYrfhz1NNqVWXeXGUC9hrJ5QqOq02wG0qiqEz1eecIOruOx4Ar2uDqOhntyxmSdgc0UWi+TPXSDf2U3hYg+Vn/o4vmVL0aoqsWbsCFMaQMWVD/qzWQ9jcSlR+me6PSXyhlqBY9ecKVHnZsbBuSprrY9O6mglSiVrxe0MyG4sLAIiRAOLAEG3PEP2OkxGbrlaAwlI20I1/LTc9lEq2tYxfHYfF3Y+PUUBKppJVfsWhKLSe+AV8slBN1YoHRwrz9DpPeQS/QQqGghUtcx+YUVQOH+RoX/+MYVz3W4sSJbcYZZF8WI/8R/8HCfhKlIl6MdcvmjaU2k1lYQfvgOha0gpSb26k/hTL7vxLcsuzaYlMl8gf/I8Q//0I4oX3XiF3lhL+KE73EF0BpxsjuTru1x3mpQUu/vIHXUz9oQQ4DgkX3oHJ+UmmNgjCfLHz1LaAb2hZso59cZawvdvc+M9+QLx7z9P+u333PjhmMXiSGQuT3bfMUa+9zwyV0AIgbl8Mf41y2aU12htcGOKQOH8RdLb97mJNWPnlRJsG5nLY/UNkXp1Z+n8t1A/Q8chc+gwMpfDbG0luG7NzPsKAapa9lH+7HmcTBY1GCSwfu2U718JBvGvXjmtkrCTKfd9VdXxUoOyyxkGods2zXhs9uhxUAShbVvQa6e+G+Oo6tT3cqwgfjpsm2JvnxsPVBTEJfc8Gaf0LisBv2vxXgHaLHN9teROtLGui6vt/cfVPcMsaY7IXaQYpYJalokNrBSbaaadAjlOyn1c4NonYNOx8NmhV4i0iwhFo2nzQ1QsXs/wmb107n4WuzA1mUM1/QSrm3GKeRTDJFS7uGy7UBScYh6hqPhitSS6jsx8Ycsi+dJ27KH4jLsULlyk0NWHv+QW1Rtq3IHhkmSFwJY1qDE37dgaGCb5yg4ozmwVWH2DJF/eQeUXHwdVJbBxFcmXt89oDdqJlJsJOoaEYudEbZfVN4g1UL7dGihljioKSjRUfkIhCGxeg1rhypw/3Ulm16FZ40+5QycpdPbg61iEYugENq8ms/vw9MdokwY/dZZBs+wmb71Zcu70GVK79hL+wO1UfOKj6PW1ZI8ex8nm3DpY00Svr8PX0U76vf1kD0646K2BQTL7D7gF8w8/iCxabpsw20aJhIncdSd6Xe14N5bJOLkcuVNnCN62kch9d2MnR8mf7wRHooSDhO/Yhtm+BGe65BQpSb69HV9HO0ZTI9Vf/iLJN9+h0NXtekJUBTUUxGxtwWhpZvipZ7DjE7FwvbaG2CMPkzt1mnxnt5tMZNsIVUWLRYk8cB+K30/+Yu/0Wa8lChd7cPJ51GiU8L13k3p7u+umVRSErmPFE+PlJJdiEkBFnbY2L0QUcDMhvdYqC4ufADoGfXTSKU+V7G+bPNlp6zjni1tPCToO9Wvupap9C3Yxx+DpXdMqQADdF0LRTBRVo/3+r8x6XlX3z7rdTmXcGNRsONJVTCWrRwn63TZRk357wmfgW7EEoSpIKUuxxam9HMuQkN1/DOfjD6BGw6hVUYwlLTMqQWs4PiW2Yo9OZJBZg/EJV+6Y6Ln8+Bhw6UxbaBq+dR3jSTbZA8enHD9F5KJF4WwXvo5FABiLmxGmjsxNfZmL3f1Iyx0YjbZGwvdvJfnazqsvN7lZsWziz/0ckIS2bCb6oYeJPHC/O3grAsX0IXQNJ5cjc6h8QiYti8SLv0CrrMC3rJ2qz3wCezSJtCzUUBA7nWH0tTeJ3n/P1OvaNqOvvIbR1IDeUE/1Fz+HnRhFOg5qKIR0HBIvv0pww7ppLT1rcIih7/yAiic+im9xG1Wf/zQyl8MpFhGa5i5tJATW0NC0lqBvWTuBjevd+GI6jSxa7nHhEELTKPb2MfKzF6YtmB+j2D9Aascuwvd8gMg9HyC4cb373DQVoWoMfe+H07ZOEwhCRAkSZZTy31mAEFEqkVIyIvt/ySxBOa70deYaalo4VHQWiVUAnJVH5j0DdDZuOSWomQEqF28il+jDH6uncf0HOfvmt6eN540N2sVskv6jb2EXZ3afXa5Q3+odmlO9npOf+CELXZvqtvKZ6E21438XznbNKaPPGU1R7B9CjYbddlWLm8ls3zf9vunslEnt5NR5J5Mt9aychD0p6/CSLEElHECvcztOSMfBGU2PW7IzIigrHxA+AzUcwspNVfiFc93kT5zDt2YZiqET++TD+NYuI/3mXrKHT7p1gTdRAoyTy2EnEjjp9BVnYzqZDMNPP0vm4GEC69ZgtLSghoKApNg/SOFiD7mTp8gdn+r6sYZHGPzmdwhu2Uxg9SrUiihOPk/26DFS23eiRsIzuskL3Rfp/9o3Cd++BV/7UpRQCFkskjl4mNSuPeROnUavrUUxzWkbYRe6uhn45yfxr16Ff9UKjLo6FL+Jk81RON9J/kIndudZfEaRrCpwbPe5WINDDP/4aXwdy9Dr61BDIbdutVCkcPoM+VNnSL+3H2vwMm0QbZv48y9S7OsnsGEtek01aCpOJkuxr39GK1JKiYZOu1jLCbmfHGkkslQIvhYfQTKkGGRqr9b5wo02KpP+VuAGrCSRkgmkkFRSS4xqUiSQyPFOLJcrOHflFON/zZQ9Ox+oqGilbNcq6nBKViCMxUfdTjnz1SVmMrecEpRA5+5nSXQepmXbE1S0rqFp02NcePepKTV/Vi5TigFKhk7vppCe2ZV5OayR0Smp35dDTJO5JXwmajg4cd7LWYElpOO4LstliwDQaitnSmYrKZ9ZkgGK9hUN3lplFKGXXJZCUPXrn5jW7VaGAKFMuDmForg1ldPJk8sz8v0XqAr6MRY1uZbnyqX4OhZjDQyT3X+czJ7DE7HYBWb0tTdIvvm2+wyupi7PsskdP+lmWirKuN6SpVjwbArfTqYYffUNRl9/y/UySNz9HQd/dOWssWLig5hH36BFP0i0wYeiQFrk6fLH6dYkQ9/7oXtOa/p7ctIZ0jt3k969151gloripZQgHdZ+ahmb/p+HePZ3XmP4jOsSlYUC6T37SL93wE2cEgJ3hlQ67grKOmShQGrHTlK79oyXc0y+/5kYpIcgETaL+8iQxMEhQBgDkyIFzsoj85pwIRDU0ERQRNDQUdGI4K66ESBCh9iIRcEtdZAF+uic9wJwgH66aJBtBAizXtxFmgQODio6FnkOy11lmZw6JvW0oAsfGhoaBsyprMcAACAASURBVAHc+tQa0YSJHxsLG4usTNFL57wl7xTIMcBFWkQ77axnKesYG8PcdQ0LjDJMlzzFCAOzn+wKueWUII5Dsvc0xWySrt0/xQjGqFy8kVxykL5Dr5UVyVv5NLnEAIHKBgKVTdekBGW+MC/dLRSfWZbi7sy1s4qkLLtSCc6c0s1llLWUExm2c0EE/ONxOiEE6NrVJWbPMkAXL/Qw8Lf/4i5k/IFNqLEwqG5cVauvJnTvbeRPXSD1+i6y+48vrDJ0nMtPAiajKCiGOsXClgULaVlXPrceSxS6VFfN8qWEKnU+8ccrWHZHJYZPRVEnlIhVcDjwUj/P/PkJMvE5PNcZ7l/RBJqpTl/B4TjgzFPUbbp7n4UMSc7Kw7SK5VRMaqA9RC9d8hRDTF02Csby0K8821FBpVEsopL6S84n0TGoZ6LrjiUKJORwmRKUOMjLLN49kT06s2wZkhyRu2gVHUSpIkzleA/PDMkpCmxyf9VLrxUiOh4/BUiKEQbkRazSOcbKNi73rCayR8v3i1GDnwCOdKsgJxfMK6gYmNTSTFRUcUjuuKbloS7l1lOCkyikhul892mW3Pcl6tfcTyE5zPDZfYw9YLuQY/jce/grG6hfez/ZRD/50fJZhFBUdH+EQibBrKvGz9eK8pcqgiv5fU1WwrOmis+vy0JMSlSRlk3uxLnLFk1fipPN41wmxmePJIj/+CVSb+0lePt6AptWua5jRUHxmfjXdmB2LHLLVH70EsWu6QevmwYBkc1LqP/U7Rg1kSnZl91Pvsbw6zdmCaBc2sZxJKd2jnBy+zC9p9M4lkPLmgj3/Gobmz9Sz8XjSd74xi9Z/17hthpLEueo3I2OgYKCgxy3xmYizSg75IuAJHcFlpqNxVG5G2VOw6ssU4B5suyVryMQ5GeotyuQZ598E4Fy2Y41CYY4LHeO3/eYYr9U0QCkSbJbvjqtB+tSnFLJ/hhD9LBdvjB+7ukYpp+d8iXkJfdcRT2rxBZsLA7LdxllpEyZCgQGJsvEBiqooU60kpBD8+aavSYlqOgmRiCKUDQUTUcPujMFI1hBoLoVaVtIx6KYGcUuTjK7/RFU04+iaO56hJrbecRf0YB0bKRj49hF8snhyyqf9GAnnbueYdGdn6b5tg9TyCRI9Y018JUMntxJsKqFira1tD/464x2HSOXHEQoiitnVRNOscDZN/6lTMbrxViq9xjCN8egtSjf17UKb0wgXxYmrGBpWSSeepn8iXPX52K2g9UzQOKpl0m+sgNzcQvBO9bjW7EEJRpCMQ38G1ei1VYx+Pffodg9e+eVhUSvCtP8lfso9I/S9+N3cfKXDDqnbpzsVt7hR//1GMWsjVWceG/O7UuQT9t8+v9eyap7q3njmxfm9FoJAeHGIMGaAHbBJtGVmvE41VCItoTxRQysvM1od4pcYppVGyIG4YYgRlDDsSXZoRyjPWmkXX7i6uUVFJIFRnvShOoChOoCICE9mCXVm5kmbOEO6mOZhnNFTuqjeaXMpMAuf82Zi+Qn73UlC93O9b5laWmoq8HGuuyzcrCnuJ0VFBrFIgx8nJIHGJghNpsny4DsHm+75q6ocROsIhFpXE7r1o+hqDpC1cYX161aehsVi9YjbQvbKnDxvRcYOr17/LjGjR8i2rwKRdVQVA1ROq512xNIu4hjW9jFPCd+/g8U0jOnTbtI4hcO0xOsoHHjo7Ru/RhnXv8WuZLFZ+czXNjxY/Kjg1Qs3kB1xzYUTQcJjl3EyqeJXziEM02v0euBzOaxUxm0UrmBVhWb24FCQauccEdYQ/EbltFtx0cnFlE1dJRI8DJHzA9OIkV231GyB45jtNS7xfq3r0cYOnpTLZHH7mb4609fNlN1odCjAVS/wfm/e4Hi0ML3d8yOTv+cek+lyKUsQpWGu8btZX4KQhWs+thSNvzKCnS/hpW1iHcmSXROvcdwQ5Btv7WOhnXVCE1BAMneNO/+w0G69/SNv8NNt9Vyx29vIFDtQygCRRVYeZsjPznNe984hmOVJsMC7v6DzQydjNN/dIj1n1uOr8KHZij07B/kpf/0DsX0zfk+eEzFtfJc9+tszboFgoBwS7cK5Oc1QeealGB2uJvuvc/Nuo+UksxgedeU4TN7J1lrMx9nFSZmO3YhR/fe51F1k2L2kvRZ6dB/7B3yySFUY+p6g1Y+Tfd7zzNwYju+aB2aGUBKiZ1Pk0sOurHCG7QqgZPPU+zpR4u5AWdjUeOcjlMCPrS6KsB9NoUL1y+b7VKs4QROIoUaDLjF70tayO6evc3cvOI4bqOCbz6DncoQeewehBD4Vi9DCQUWtIn2bEjbwc4WcHI3Jn4pC0WskThONjttooiqCaJ1Js2rI9S0BQjEdAy/SrTWxAioiPHEldl/Cw3rqrn9/1hH585e9n3rGFbepu3OBjZ+aVXZfnpA4+4/2EysNczbf/seI2dG8Vea3PYba7j/j7fw7O+85lqQQLo/y7m3uuk9MEh6IIsR1tn85VVs+OJKunb20XeoPHt00V2NRJtD7P7nw4xeTGOGdRRN8RTgLYaDQ5okMWpoEItIyGFyZMaVnIKChk4dLdTT5q6lKHvmtZvONSnBfHLoqlZ4T/Zepv/gNEjHmrWYXToW8cv0/yyk49eUHDMfyFye/LGz+FYsASEw21tRYxHs+OwDuW/FYpRwEKTESWUonJmmHdt1QhYscodPoTXUuG7r9csZff7Nua+UMV8ULdLb9xF++E6EoaOGAtdlEdlrQegTXVOKI2lynUNUPbiWoZcPTFgzJaRlXzaJ6UrInTrNxT/7S1eHXZK16o9oPPAbi7jtYw34ghqFrE1qpEg+baHqCsp0zbOnQSiCpQ+2Yhcddv/TYYZPu1mgiQtJGjbW0rBuYvHWujXVNN9Wx5t/sYczr7jv68g52OUc4qP/7/0svq+Zfd86BkD8QpLd/3y4TP8e+N4JWu9oINwYnKIENZ/Gzv99cMrnHrcWEkmXPE1MVBOjhs3iPlIkKFJAoKBjECCEjomNRScnZ3SZXi23dGLMLYmEzO5DhB+4HTUWRm+oJXj7OkZ//taME3AlFCD8wDa3zRqQP3XhxsbCHIf0zoME7tiAGgqg19cQfvhOEj95ZW4lApPWl7uq7ZNQ/Ob4agxOvjDvSwddC4rfoO13HkH1uc0GpJRoIR9Nv3oP1Q+vpTCUKlN6/T/dw+jes/MngJRu271L5VIFD351Mfd+pZWeEyme+h/HObc3Tj5rIyW0rI7wa38789JOkxEKVC2LkerNkOqd5KmxHAaODpcrwdVVKLpCZXuUDV9cMf65EdJRNEGsLYJQJsL+vohB1bIKQrV+dL9GtMX1lija1HTTVH+GwROzh0okEimvrpelx40jRZwD8h2axBIqqCFEDBV1vDYwR4YBuumTXcQZmPfv01OCC0CxZ5Dk67uIfvhehKoSeewe7GSa9LsHpgxiaixM7BMPY5Y6rziZHMkX377hcbD86Qtkduwn9IDbPzTywQ8ghCD5yo4Z3ZFKNITR0oC5pJnUG7ux49M0KNdUwvdtxU6m3RU0hhMzKkS1Mkrk0XtAUUou4Z6rXnD4uuBIiiNpbHMitlEcSZHtnD6d28nfGDepL6Sy4dE6HEvys786xfG3p1pPqjbXohfhxgFzFnZx0rsqoZgpfyeNkI6iCpo212EXyt/r/iPDpHrTuBlfkqUPtrLlq6tBQrI3g5W1MEI6QlWmzVW0CzZ2YWaXmIPNMfkeJzk446K3HjcPGZKckgfQ0FFQxzNUZanzjU3xuk1mPCW4EDgOyZ+/hdFch3/jStRomMovfYzA1nXkDp3EGoojNBWjpQH/xhXojXUIVcEpFBl97nVyx+fRepgrtkPimVfRairxretA8ZtEHr+XwNa15E93UuwdQOYLrpsyGkavr0arqUSNhXFSGdI79k97WqEo+Fa341/bgRUfpXixn8K5i1h9pQ49UqKEghit9fjXdKA1VLurs+fypF7bOePKFAuBky/S9Y+/WGgxpmAGNQy/Si5lEe+9JGtRwKKNMQz/zM2ry5CSXCKPP2ai+bSJ1SwEmOHydnvZ4Rx2webtv97L4ImpYQi7YLuL7TaHuOO31zNybpS3/nIP6YEsdsGmbk01jRtnadh9GYrkr3hldI+rRFHwr20ndPdGtOoYWDbZY2dJ/OT1KRP28ANbMDvaGP7mz8oW8B4rir/ReEpwgXDSWYa/8QwVhSKB29YgfAaBDSsIbFgxZV8pJXYyzegLb5F88Z0Fax5tJ5IMff0pd+2/zavdLM36avT66sseN2vsS0rQVPSaSvSaSgLrpz6DiV0l9mia0effILPnBibnXAVCU1CDPqzE1HR2NWji5ItI6/p/l5lEkfRIkepWP23rogxeyGAXJb6QSscdVdz5maY5z7EdR9J7YJD1n1tO9bIY3Xvc1U2MoE7DJQqre08fVn4VbXc20ntwCDs/YQ3qfg276N67L2oSqPJx+KlT46UWQhHUr6tG83lD1K2A0VpPzW9+kuLACNn9J0AROOnctN2HzCXN+Ja1oAT9ZUrwavGtWoK07Dkt3j0d3hs2C/ZommKprZmdnFtdjpPKTByTSM4a67Ljowx9/SlyR88Qum8rWl0limm6KylIibRsnEyW/KkLJF/e7i53NIMykfkC1sAwCMXttXlp79Cx7Qg3oeXS7bm825ZNVWa9V3s4wfDXniL73lFC92zBaK1H+MyJPqmluJRTKOKkXdkzOw9gzZD4I4sWyZe3Ix0Hc3Ezwu82kRbqpA4rtoMsWjjZHPmT50m++u6sz+JmwddcRcPnP8CZP/1JmaxCU2n8wl2MbD9B6lDndZejkLXZ8cMuHv3ddj76HzpY+1AtubRFtNakvj3EwZf7WXZH5dxOJuHkS+fpeKSNu35/M0eeOkUhY9GytY5Apa/sdR86leDA906w9tMdhOqD9O4fwHEkkYYgFYujvPWXe0l0JskMZhntTrH80UWkB7LkkwUa1tfQcns9hYznyrwV8K1YBJrK8LefJ398dmUUf/YNtLfeK1/p5mpRBJGHtpE/00X+5PmrKhsTcpZBWghxc48y1xtFMKmx49wGXSEmBu+xvoZzQJgGeks9ep27aKq0HezRFMXuPld5Xc76E0wsQTSdrJfdPlnuOd6rqqLVVqI31KBGQwhVRVoWTjqHNRzH6h10u8TMJelFUVBCAfS6KtRYGCXgdzMtpZsAY8eTFHsHsIdHb6pm2rMR7Gig9bc/xNF/92T58xSw9E8+SXz7CYZePnhDZNFNhds+1sDmDzdQ1erHLkj6z6V577k+9r3Qy2f/62oqGkz+/it7sK3Lf18N66vZ+KurqFleQSFd5PQrnQyejLPlN1bz4p+8w8hZd9Kj6gpLH2pl+aOLqFgcQVEV0gMZut8bYO/XDo8Xzbdsq2fTl1cSa4tg5Wx69g1w6EcnuePfbODwj09x6uWJTjaP/9U9KKrCs7/72nV5Vh5zRAiEqSNUlehH7yV8zyZ6/+zJ8WXanFy+LMdBCfgmxiDHwcnmZx4bNBXF0N0xSUpk0XbXnRzbXQiEoaFVV1D7e18gveMgoy9uH9/uZHJl44SUcsag9w1SggJ/pI5w9SIUVSOXHGB04AyO7c3yPG49VN1PtK4d0x/DKuYY7T9FPjNpVitAqArBjgZa/vUHOfYH3yz7QaphP+1//An6f7aX4VdvnEtXCNBMBVVzE4scS1IsuA09dZ+Cogry6bln26qGgqqrbk1vydWpmSpWzprS6Ek1VVRNAQGOLd144CWdYDRTRdFd2eyCg1N0xt2mk8tLdL/rwCpm55Yc5gtVE6lZgqqZ5NLDjPafwrbmK1YoqGhciT9cW/ZpJtFDvPcEN6yjxQxUNq3BF5o9XHEpowNnSA1fvn2e3lxH1RcfRaurdFe3MXTXvVl614e+8VPSO9xJnjANan/vC5ht9aCoWINx+v78SezE1AYLekM10cfvwuxoQwn6kYUixd4hkq/sJLPLLZMz21uIPXE/RksdakUEWSgiJyWa9f7ZkxTOTZRSzKYEb4g7NFa/nCWbP4nhj4AQOFaB/rM7Ob//p5dtFOvhcTOhGn7at36OaO0yFFVHSpvsaD8nd/wL2VG3bCW4rIHGL96NVhHE11RJx3/7bNlYqEUDICBzsueGyi4lFHMOxWkKjYu5K/8d2gVnSobmpRmi4/vm7bKY4HRYeRsu2Wc6RTdX5QcQqmqjfctnMUNVCKHg2AWGuw5xZs+PcOxrT8IQQqG6dSPVrRvLPu878y6J/lNlDf0XgppFt1HZtOaKjjl/4GdzUoLWUJyRH/0CVIXwPZsI3Laaoa8/g1XKFi/2TGRFy3yBwX/4IUo4QOyJ+zEXNU3b/1j4DCq/8AhaXRXJl3dijSRQIyHM9pay31Cxb4j4T15Dq6mg6kuPk373EOl39o/vUuyde4Pt664EFdWgbukdGIHYeANhVfdR3bqJ/rO7yCRu7EDg4XEtRGvbidUvR1Hcn44QGoFoAzVtm7hw8AVAkj0/SPe/vEnlXSvQK4KkjnRP6mcpcbIFRveeJdc1t2W0PK4OIRRqF2/FF66ZGHs0k8rmNfSf3cnowJU37fCYQGbz5E+5MW3/6qVg2+TPdM+4PJydSI3/OxOK30SrrSR/4gKjr+wcd6cmX91dtp+TzJBPXsBJZ5GWjTUYJ3fi6mKCN0AJavhCVVM66Cuajhms8JSgxy2FP1yDuKQtnxACX7hmPDHIyRfJnOjByRbwL6ql+xuv3/RJPL+UCKX0fYlLPnbHpPeDErSKeaxiDkXREIo65VncbNijGbKHThO6cx3VAlLvHKBwvgcndWWr1lwJ110JSimnjf1JKbGtG18T4uFxLTjW9HFsuzh1VY/CwCi9T717w/rSekxl2ryD99HY03XkRfrPvIuqGai6iWYE0c0AmhHE8EeI1i1DMwILLeYEts3ID1+meLGf0D2bqf3tz1DsHyb15nuk3trnrus6z1x3JehYeZKDZwlWNI3PoKWU5JIDZOI3rgm0h8d8MDp0DruYQ9X947Nq2yoQ7z0+Rdk5BYv00e4ZXTRCV90mCHnrlleUquajetEmVFXHtgoMdR3Ayt/g3rKXIB2bRP9porXtCMVtBiClJJ+Jkxq6upqyW418aoh8arr+qgLdDLLi7q8SqryJlCCumzX5i12k3tqP2d5C6K4NVHz2g6ixMPEfzX8zihtgCTpcPP4GquEnVteBUDRSw510HX6xbJUID49bgczIRc4f+Bn17R/ADFRQzCXpO/suw92Hpuzrb6uh5pENdH3tVVfZ5YrIUqmLXhWm6Ut3Y9REGHn7OIMvHUAWb54+qFdKqLKF1jWPomomhVyS5OC5BVeCIOk/8y6GP0Jl0xpUzSAdv0j3kV+QzyxsI/2FZ2wV+Jtw8lUKK8h8gdzh0+RPXkCtiBDYtIL4j18pnzA6EqREGDpuHdiV388NyQ4tZOOc2f0jNN0HQmBbeZz3iTvC45cLKW36z+xkqOsAiqIhHRurmJt28WdFV4nd2UFwZRN6NMDovnN0/fOrWIkM1R9ch39xLYndp6l7YivZC4M3pHD+ehGqakXVTYRQbqq4k1VIc37/s3QdecnNDrUK81ge4XFFqAqKabgKSxFux5hMzm2rVlJsalWU0Ac2uK0Th0dBSvTGavTaCvKnu7lUydmpLHYiRWDDcnJHzmAnUgjToHhxYM6u0xvWMUY6FsX8wi8s6uFx7UjsQnZO61qrAZPBlw6QuzBI9cPrqLp/NX1P78K/qIb49hP0/nAHimkQ3bzkllWCiqoTrl40JWHoZkE69k1glb6/CT+0jcjDbvN9NRJCGDp1/+FXkUULa2CEgb/7Pk46i1AUAhs6iDy0bdywk7ZN4UIP8ademWLoOck0iZ++SeyTD1Lzbz7rdqvK5Oj/2++UlWjMhtc2zcPjOpI9P0DPd95GFm2cfJHY7R2A2zptrHdornOQ8NrWBZb06tGMIMGK5oUWw2MBSb6+h+z+EzOuKJN97ziFC73TbpOW5XaXAayBEfr++ttosTDCNACJk8ljDcZntOzS7x4id7ITLRZy69CzeYoDc2/J5ilBD4/rhFO0QYJi6thFGzVgoleGMGojqH7DXRex5DkUys1pRc2FUFUrmu5faDE8FhB7KIE9lJhxuzUUxxqaWxzWGU1TGL0Cy11K7KE49hzPfymeEvTwuE4UBpPYmTyL/+AjFPrihNe1URhMsuQPP45RGyHfl0CvCBFYUkdhaJq1Fm8JBJHqxePZlx4etxqeEvTwuE7YySxd//QqtR/ZhF4dYeD5fQy/dpjAsgacgkX1g2tZ9t8+hxowOPvnzyy0uFeFqvsIVjZP1wHr/Y2Ut3zZy/uF+VOCVxwUv54viSjvSydnTwVWVB1fuBozUIlm+BGlrD+7mKWQGyWXGi4F1q9W3iuTR9XMkjwVqLqvJI+FVXDlyaeGsArZa5BnBilLnTTMQAWaGURRtfHC4mIuSS41SCGXnDYTct4RCoYvjBmoQPeFxp8D0sGxizh2kWI+RSGboJhN4jg28/Y8hIBp1zOfhRmeSfZcPxf+14ugKsiCDVIyuucMALkLg0Q2LiLflyB9/MpqZoWiYvhjmIEYuhlC0Q2EUJGOjXSK2FaBQi5JIZPAyqeRcr7LLwRCCPzhGgLRBsqel3Bbll3ZmHAN48FNNfaUrjDt+yDQjAD+SC2GP4KqmYBbZ1rIJsgmB65xnLl1EYqKGazEDFaim0EUVXdXkLHd9zifGqKQTVyXXtPzogQrGlfTuu4xxBUMHJlELyd3fOu63NSijR8jVtcx/vdI7zEuHPjZlGa2vlAN1a3rqWhcgxmIomgmiqK6PyopkdLGtgrYxRzZ0T7ivccZ6TlCPn0F62AJhfZtnycUaxr/qP/cLi4ef+2SH6IgEGugunUjsfoVGP4wijomj3AHf8fGsYtYhSyZxEXiPccY6TlKMXcNrjShEIw2UNG0hoqGFRj+KKpmIlStlO0nkY6reOxijkyil6GuA8R7j13bdWfAF66hon4FscaV+EM1qLqJouiuu0246WJSytJgb2FbBaxChtTwBUYHzpIe6SKbHLhqRa0ZQZZu+Qz+8NxXNHcci7N7nyY5eGba7dJyELiL7E7GSmYZfu3InK8jhEKwoomKhlVE6zsw/DFUzUBR9VJMsfR8HAc5NlmwChRyoyQHz5EcPEdqpItC5srXcVN1P2YgiuGP4gvXEIw24I82YAai44P5GLoRpOMDX0bac290nUsPceKdb1zxyjLh6sUs3vTEeC/XuZDPxDnxzpPXtVTCcaxSHZ77vYWq2qhZtJlITTu6GUBRjfE4sPv7ct/jxMAZBs/tITl47jpMXG42BP5ILRWNq6lsXIUZrETVDISqT2qsYuPYVmkM7me4+xAjPUcozGOd57woQVU3p+2pOBu2lS8N7vMhQTmGP4I/MrG0ST4bR1E07JISVFSDmkW30bj8Hszg1L6mgLteFQqKqqObQXyhKqJ1HRTz6StSggIwA7EyeQKReoRQxl9yVfdR334n9e0fQPdFZpBHRVVUVM1AN4P4w9VE6zrIpYauWhmZwUoaOu6mqnk9ui88Q32XQKgKiqqhGX7MYAXR+g4yiV56TrzOcNfBeVkSS/eFqVt6BzWLbsMMxGZ5l4SrCxUVMNCMAGYgRjDWSO3irRSyo6SGO+k99Taj/aeuWA6hKJjBirLv63I4dhFVM6bdFlzVRN3HtmDWRifWayzR8913iG8/Madr+CO1NC6/n4qGlWhmcJZaPPf7AlyZzCBmsIJQ5f/P3ntHyXXdd56f+0Llqq6qzgmN7kbOYCZIShSDRFGkJFJZtmRLY43lndmxvTMerz27e8Ye+cyMw9oe79ie2Rklry3JlClSVCIlkqKYA3JqAI3YOVXOL9z941U3ulANoKsTAALfc3AAVNV797777r2/3/2F728VLWvvophNkJroZ7jv5xQylw8h190BurY/gj/cjub2obl8CEW7ZC6gUFQ8/nkW6S3DERi121QVzYU32OicHOaLmk+ptUNaTu07ze2nfcN9NK6+xRm7uSonzKwvH25/PfXtW5g8t4+ho89Tyl882ORahssbomXNXTR03YzLW3fR+SSEhqJoaLoHty9MXdMaWtbexWj/q0ye2YNlFhbdl+vCJ6i7AwhVA7OIqnvo3Px+mnvvrG3h4AjuTGzxuVy6N+QIQSy08ibTuGpnzcEFZjGzYALyUGMvXTsexR9urzm5WVFUApF2em/5BMH61Qwcfg5zETmgnmATPTc9Rqipd1G5ZkIouH1hXJ4QybETCxKCSwk94mfVP38Au2SRePskdqnyZFQcnZ82W9e0ltU3fRRvsGnBiehCCITQ8AYb0Fw+Jk6/My8hqOhugo09NQu16xkOL7KByxui++bHibRsnHf0rxCOybS59068oWZO7f4uhfTEMvd4ZeGPdLB6x4cJNnTXPJ+FouALNbN6x0cI1Xdz9uAPF30qXBIhWMolSI6dQHP70Vw+h7FcKAhFQQh1xrR2pZgkdHcARdFQVJ2OTQ/SsubuCh+dlBIp7bK51NFIp82i5/ssycTOLYkJ0OUJIoSCqrlZvf0RGrpuYrYWLMumWGnbgESgzJgDZ/cnNXHKYSupEeGW9fTc8okqDUyWfZWWWULaJlLaCARCUVE0x+c0+/dC0WjuvQNN93B679OYpdoTknVPkN5bPkawoadqfkyTr9uW4ZjNpXR+IxzNWVF1xwR/wXWlfIL4yPzNjLNhW4ZT8NksOXNZc5fnsVJm4XesA/OZy3o0gBb0cvz3v0VxbGEavT/cTs8tH6uyWEy/K9s0HNPbzPiU152io6iaM6su6Gs2PkAmPk9lToJtmZc47Yuq6gTT5upazDyO6bR2s5BRSJMYO4HLE3TeV9mUdv6dXYnqCRKhKKze8SiR1o0VnMlV81lRHffDBX0UQhBq7KH3lk9w4o2/f9ecbIQuWgAAIABJREFUCIP1XfTe+qmK8lZw4Xw2kFI6LgRFLZuOLxwfhfpVO1B1N6d2f5dSfu78xPlgSYRgauI06cmvzjjEFc2FpntRdS+ay0tT963Ud+5YiqYWBM3ldeqItW+hZc2umU3BtgxyqTFSE6fIp8YwChmkbTkmUE8Ab7CJQH0XvlATQtWdzXEJCnHq7gCK5qKldxf1nTvKplFH+OQSw6QmT1NIT2AUs05/NBcuj2PiDTZ04Qk0IIRCcry/Zt+XP9JJ902PVwhAZ9MyycQHiQ0dIhsfopRPYhnFspnGjy/cQqRlI3Ut65xFOyOQBfWrdlAqpDl38Ec1FxFt7rmjSiOU0qaQniQ2dJD01DmKuThmKeeMhaKhlk0jnkAD/kg7gfrVuLyhmZN9fPgoxgIXhWUUOLvv+zPBMYrqtDc9nz2Berpveuyi5s/ZkLbEKhgYqfyC+iIUlfZND1QIwGmFLRsfKr+rwfK7KiCljaK6ymbrKN5gI4FoJ4Fo54wZU0qbybN75/2ejGKas/ufQVWd573QaOkNNdGx+cGKTy2jwNDR52tyG5hmoRzcVBtyiWGOv/oNp3khUMvPr+peNN1LtH0LzWt21XzfxUHQOMvMJ6WklEswNXiQ1MRJCpnJcmAb6G4//nAb0Y5t1DWvrVCwhBAEG1bTsfn9nNn71JK4Ha4kPIFGum/+WIUAnFb4c8lRYoMHycQGKObiWGYRRahOIFFdM+Hm9UTaNs4Q109fH27dSOeWD3J695PY9sLGZ4nMoc6DIEFSDt6YRVMUrO9ammYWCCFUIu2baVlzV7kauE0mNsDwsZdIjp1w/JNzCROhoKo6nmAT4db1JEb6lqQ/iqrR2HVzuT/aDNv98LGXyEydxbKKc0avTSsYvrpW6prWkJ48U1O7mstP1/YP4fZHKyahUUgxcPg5pgb2Y81xsixmY2TjA0ye3Ue4eQ1dOz5cFsTTG5+gqfs2UuMnazqB6Z4Q0Y5tXHgKjg8f4cy+71PMxS8axTdtBnY0aQ/+SBuRts0E67uYHNi3qIArR0t3/m3ZjlO+hGNyKWan5q14lCZSFIfjhO9cS/wXR5HWBc9ymQhFX7iNUGPPBQqCxejxVxjqe/GiJ+9iFrLxQcCJfFZ1L6GG1UTaNuHyhUmOn5hX/wFss0Ri5OhFvw82dNOx6YEKyWhbBonRYytWK3R67wEwLbOCmN/tj1AtupcXQgg8gfpy3yTx4cOcO/Aj8pnJqrljFFLkkiNMDR2kvmM7XdseRnMHZglCp3J9bOjQJd/D1Q5Fc7Fq2wfx1bVW7D1mKcdw34uMn367/N4q10QxFyebGGJq4ADB+i5W7/xIxT2EENR3bic13s/E2d0L6tt14RNECDo2PYCiupC2xcS5vZw78COMwmVOC9LGMotk4wNk52s+mgcUzU3H5gdRVBe2ZTJ68jWGjvzsslU1pLSxjALpydNlAVib+aip+1YnsXnWpmoWM5x8+wmnFNBl7idtg/jIUUyjwLo7PzcTTOP4Mby0rn8PyYmT2POMunP7wlVFT0v5JAOHfkIxO7+q69K2MEtZkmMnSI6fRHN5Z7TsKw2hKpjpPKu+9AAND2yjNJGaqSIBMPXCoUvyhQbCHVVMLJmpcwz2PY81z2ecNr9NDR4gNnwYVXPfqN6yQpBSkhw7zqndT152r7HNEhNn30FKm56bHkfVz0fcKqpO65q7SE+cumbJv+s7thFu3VCx1i2jwJm9TzE1sP+ySqu0TVITJ+l/81us2/X5CiVc1Vy0rn8P8dG+BXHEXrtcTTVACDETxh0fOcrZfU9fXgCuUH8mzu5m4NCzC9iYahOALl+Epu7bKqLipG0zfOzn8xKAs5GeOsNo/ytV1wSinYQae+Z9H90TrAoGKmanKMxTAFZB2s4iWIk8xnlA9bvRQl7ShwewcsWZ/0//UfRL6aACly9UFVCRiQ2UC/jWDkdhuCEAVwqWkWfg8HPz32ukZGpgH1ODB5i9toQQBBt78IXblqejywzN5ad5zS4U5XwgopQ2YydfZ2rgQE1Wm1xylOG+F5F2ZZCZN9hMpG3Twvq3oKuuQUgpnVPG4eeuipOClJJ8epyho8/P++S0GERa1lfZ4nOpUSbO7qXmgAQpmTy3j5Y1d+Hy1s18rGpuom2bSYz2LTgZ2Qm+eXfoZsXhOCe/8uTCbzDHEE4Hu1x/6dTXFqSUJMZO1Fw4XNoWY6feINK2Cd3tn/lcUXWi7VtIT55e6q4uO0JNPVVR6MVsnLHTby4gF1ISGzpM6/r34gs1z3yqqBrRts1MnduLXUN+KlwnJ8FpxIYOkUusjJ/i8pBMnt09b7PfYiAUjWjH1qrPE6PHFnwiNgppMrHBqs9DjT3zTj0xipmqReANNb+7KhII0MI+gjtWE3nvJlSfG1QFNeC5zIWSUiFdpSXXNa1Fn6V43MDVCSltkqPHFhTMkksMk0uOlCMmzyNY34WqX27eXF0QQiXavrVKsU1NnLpIxfvLwzLypCeqlYFAdBXqAojcrxshKG2L2AVmhisJyywRGzq8Im1NJ39XholLkqPzS9SeC7ZlkE+PVy1Uze2bd05ZKZegmJmquIeqe+ja/giB6Kp3xYkwuHUVa//wU/T+3kfp/q2H0esDuFvCrP6th9Ebgpe8NpccrgpU8gQb6N75YdyBelYy2OMGaoO0TdJTZxd0rW0ZVZu8EAK3P4rLG1qK7q0YdE+AQKSjKh0iMXZswfeU0iaXHK3ae1TdUxPT0zSu/V1mnijm4gv3NS0D8qnxReW21AKPP4rLU7l4LKPgRF8uAo7/rXIiKopeYSK9FEr5FPELIm6FEPjD7ay/+wus2vpwORLs2pymWp2Ptl+6h+zxYfq/8k+YGcfsbWUKaCEfvp7mS16fjQ+RiQ9WLHYhFCJtm9l4z6/Rtv69uHxhbgjDqw9GMbuonOK58jh1dwDdfWnF6WqD7gnivkApts0Sxczi9mKzVM2H67A91U7qcN34BEv55FXhC5xGITu1Ynk/3lBTVQCKbVuEWzYsKlDCG6rexIXi5PbMD5LR/tcIt2yoOKkKIXB5grSufw+Nq28mMXaCqYF9pCZOYxlXzzu8HPT6AHrEz6n/9BRWrjgTGWrlS9glAy10adONbRkMHX0Bf7gNzeWvCJv3BBpYtfWDNPfeSWzo0EzO4LWeS/ZuQSmfrDlndjaKuQS2ZaJq510L05R+XEMEMt5gY9XeI6VFqLEHT6BhwfcNRKtdJgIFzeWf49eXxnUjBM1SHttcfKL7UsEsZKoinJYLbl+1duTyBOm+6aNL35gQNdHRFbNTnNn7PSeJtiL30NnsdU+QhlU7ibZvoZCeID5yhPjwUXLJkWtgw3e4cS/MD1Q0FaGqVTRqcyE1cYpzB3/Mqq0PV3BPOsw5Kp5APa3r3kNT960zCceJseMU0hPLQk5/A/ODU7lj4eNvmyUsI4eqVVpVLrToXO2Y62SmuXx0bX9k6RsTVCgN88V1IQSn6YquFn+glHLB7AYLge5ZORPKXDRdl0NyvJ/jr32Djk0PEm7dUFWVwEkpceGPtOMLt9Gy5i4ysQEmz+0lMXZ8wewwyw0jnsFI5Wh8eAexl/sQikANePD1NqOFvOROjl3+JtJm/PTbFLNxOje/H3+006H0m4VpvslQYw/Bhm7aCilSE6eYOLub9OTZa+r0/G6BZRarfFa1wLYtLLN6j1BdtQd+XEms5N4DLIgY/boQgsCy1w+rGSvYn/lQfF1p5JKj9L/1bcItG2hZexeBaKfDGXiBQJ3e8Oua11HXvJZ8cozxM+8weW7vFc39nAtmPMvod9+g89fuJ3rvZrQ6H13/8iEUt8bY996iODRPv4i0SY4dJxsfon7VDpp77ihXTqhevkIIXN466jt3EG3fSnrqLGOn3iAx0ndDGK4gauVOneMGc6YP1Er6f6VxLew9148QvI4xV3UK27aWJT9R2vaCzby2ZRAbOkhi7Bjh5nXl+mu9TkHdCzS8ad5Sb10LXds/REPXToaOvkB8+PCifDFLjeRb/RSH44R2rEavD2JlC6QPniN7vPZUHbOUZaz/VaYG9hFt20Lj6pvxR9ovqiwIVSufDrtIjZ9k8MhPnYjFq00hvIF541oLgRKieu+Rtl1mvlnaeXje4lcbbgjB6wBzJY9m44OcfOs7y0DDJDFLi6vxZZulcvHMPnyhZqIdW4m0bXJKCc3Btg9OROmaWz/J6MnXGTzy06vH/yuhMDBFYWDKqSco5aLXvlnMMn76TSYH9hGMriLauY1IywYnfP6Cai3TJZTqmtfhq2tl4PCzjJ9++6ph1Xm3YtERzULMeY+FkIxfSdhzKMT5zAQn3vj7BVGcXQ5zcR9fDjeE4HWAucxgQgiMYnZB5Y9WCtI2ySaGyCaGGTn+MsGG1TSs2kFd87qZCNTZgSKq7qF17d1I22LwyE+vqhMhAHZZ+imCwOZOjInUvGsKznk7s0hy/ASpyVMMuQOEW9ZT37GNYGN31enQMZOG6Nr6ISyjwNTA/sU+zQ1cAk7pMadm6IKuV9Q5TZ8L2eSvJObqrxACs5S7aspD3RCC1wHmmmyay4+i6XCVHJguDYlZyhIfPkxi9BjeUBONXTdT37m9qiaiouq0rL2b1MRJkmPzr5awkhCaSusn7yT+St+ihOA0pG1RyicZP/0WkwP78Ufaae65g0jrxrIp+fz4aG4fnZvfT6ZcouoGlgeay19zgNhsKKqrijwdHJalawlzFbxVNU9V8NuVxLWZhXwDNWEuZheXN1RDPt/VA2mb5BLDnN3/A/pe+SqxoUNVJz5Vc9PUcwdijsCRqwVCVRa1SV4MtlkkPXGKk2//I8df/zuyFyTbg1PXrb5zO9eeh+nagctbN6cvfr7QPUGUC4JKpJQrQrO4lMinq5MaNbf/qmK+uSEErwPkUxNVvj+haASjV7bO4+IgySWGOfnOPxK/gLBbCEEwugrXNcausZSQtkly7Dj9b32bfHq8cnwUhbqWdVUVKm5g6eDyBNHdgQVf75+jYoRZyq8Yy9RSoZCZqiLkEEK54jVmZ+PGKrgOUMhMUchMVp0Iwm0bEMrVe1qaD6xSnpFjv8C6IBBG94auGNmw4tZRPBf/o3p1J0hmBZBPjTF+6s2qxG1voGHOyL2lw/V9yhSqSiDSucCLFYKN3RUfOdXp45QWQcV2JWAUUuQSwxdQ/wnCLRtQ1KsjfeLa3gFvYF4wS1lSY/34w+0znwkhCDV0E4h2XpPlWWajlE9glLIVhUiFUKrMSSsBoSl0/eYH0esuQd+kCHyrm4hxZEX6lE+NORRcs8xzQtEcc7G1OKfwXMFHQlFR1OUUsFc/hFCpa1nH1OD+mgO0vIGGirU6jWxieFkiKpcTllkkMXqMUFMvsxUjX10rdc1riA+vzBq4FG4IwesEkwP7aOq5DVX3nq/IrHtp23Av/W+OXHNRZ7MhFB3lghOtlPYVoVWTEopDMczkpTlZ82fGyZ9dGRJIVfdUmT5t21wS2j7TyCOljeC80FN198ozhVxlEEIQbl6HJ9BIPjVa07XRjq1VAV9IWRYY116OZ2zoEK3r7kH3hGaeSdFctK2/l8zUuSse7HNDCF4nyCVHmBrYT1PPHTOfTZslOjY9yMDhZxeXWycEquapiZVEc3mXhNQ81NiD7qn0v5TySawrQZhu2Qz//SuLv49QUDXXopUToaiEm9dVmb0L6cklSSGxjCJGIeMQO5ehKBrhlvXEh49yLW7aSwXdE6R9w72c2vPkvNfWdGTvhQIwlxwhPXVmeTq6zChkY4yffpv2DffNBIMJIQjWr2bVtg9xdv8ziyLyByc9aqGMSDeE4HUCaVsMH3uJYGOPk3Q+rZEpKi1r7kL3BBnue5FcanTejCJCqLh8dfjDbUQ7tlHKxTl38Mfzu1ZRWb3zMcxSjtjgQXKp0dpNPUKhrmkNbevfW5FYLKUkGxu85vwns6G5fKy745dJTZ4kPnyUQro6uOlyUFSdxtW3Eu3cfkE9N4eGTdqLT5i3jAK55AguX7iijWjHNmJDh67aNJWVgBCC+s4dlAoZho4+f9lN2lfXQvfOx8rlsc7Dljbjp99aVGmmKwppM9r/GnXNa8t1QsuCUFFo7LoZze1n6MjPypHM85yTQsHlCeELt1LfvhXbNjmz96kFkZYvuRB0GD2Uir/nygkRioruCWFbJYdqS1ozf9+gdVoeFDKTnNv/Q3pv/WTFyUlRNRpW7STU2ENi9Bjx4SPk0+PYZmlmUgkhEIqGqrlw++vxh9vwRzvwhZpx+yIgFCbOvFNTfzz+KIFVO2nqvo18apxM7BypiVPkkiNO1Q+rhLStmc1aKMr5PgTqaejcTn3H9qpToG2VGD/z1uLNfUKpmsuOma866ENz+9DdAaS0q+fzQpoWCr5wK6GmXlrX3kMuOUp68gypydNlgVjANg2HQUTaMwwjiqqjam584TYaV99MuGVDFX9jMRsjNnSQpTil2VaJ+GgfdS3rKwJtXJ4APTd/nKGjz5MYPY5p5JCWMxZCEQihIhQNRdVQVB0p7UWH/zv3VGb9rcxZaVwoCi5PEENRnfkl7Zm/l4JJZ5q+S1F1FFWjdd09+CNtjPW/RiY2gGUUZphUFEVDc/sJt2ygdd09VZVUpJSkJ04zeW7v4jolRNX46J7gnGkcqu5B9wRnjc35+bzQOWMUUpzd/wxrbv9sRdFtoShEWjcSjHaSGDtOfOgIueRImYB81t4jVBTNhdsfxV/XWt57WnAH6hFCITFydEH9gkUKwUB9F6HGHif5UXejam4UzYWq6iiaC0V1oWquOf0D3mAjm97zJSyr5Cxmq+SUDyn/3zILjqmlmGFqYP8ij8s3MI346FHO7H+G1dsfRXPPrlHnEC83dd9GU/dtWGYRs5gp+9WEw2Che9Bc3orN7kLOyloxu0KEP9JOc++d2FaJUi5JqZDGMvIO7ZsQzobh8uLy1pVPHnPTSk2cfofkWH9N/dBcPuo7t6O7A6i6M5+n56+iulA0HVV1oeoeh2Rg9jMoGl3bPoSx/n2z5rGBbZawrRKWWcIynPmcnjpTUyDSNGF4sKHbqRDBvVhGgVI+iVHIYBoFpG0hyiWsNLcfty98UWFtGUWG+l6kkJmqaXwuhdjgQZp7bsdX1zZrPjiV0Htu+TjFbIx8etI5yUqJomqomtsZZ5cX3eUnNXGKY69+bd5t+sJthJvXzbwrVZv7fWmeABeOg8tbx/q7v4BVfj/OezJm/m8ZxfL8zzI1dLDmKiWWWWTw8HM0996JJ9CAoqjUNa2lrrGXYi5BIRsrm+olqsuLN9hUzpsTVQKwmItz7uCPatr/hFBo6LoZl7cOVfegledyxd6suVA1t6PAXoDmnjuItm2u2I+deWxgW0VnfIwiudQoiZGj8z59pSfPcGbPU3Tf/Bgub7hi79HcARpW3UTDqpuwzRLGzN4jy4qv2yklpihMv8/F7j3TWJQQjLRupH3jfcyeZPPtmKLqeENNc353PpxWYhSzpCfP3BCCSwUpmTy3B8so0LX9EUfzhLKmOItZRPegrWCKwey2Vc2NN9R00flxMdiWydTAfgYOP1vzKVD3hOjY9ECF8JjvXJ5WIFzeuqrvZs9lJIyc+MWConFnCxfN5XOIDqqbuyiklNhmiaG+F2s+sV8ORiHNwKFn6b31U9U1DxF4Ag2XLaBa64YWauxh1daHp2t31XQPRVHxBhvn/G72+5o29dYqBI1CivHTb5NLjtBzyydw+yIV9R89gfrL3kNKiVFIcWbvU2Ri1VXmLwWhanRsvB93IMpC9mbd7Ud3zxHdLOWsc6AkMdJHcvwEsoZYgvjIEay3Sqze8WF8dS0IRNXeo+ruikjv5cZiWV4dM1H5IZZKMp+/n7J4ItobqIaUxIcPc+zVrzN5djeWVUJKuaD6Z851NsVsnEy8hsUqZbn6tr2oumvT/S7lkgwcfpbTe55ckMLk7NdKxXxeCsyey+VG5nWdtE2MQsoxRy16fGzyqTFO7f4uw8d+viycqvGRPs7sfcp5pyvizpjePJfzfdW+90gpSU+ec3hdx/rpf/NbZGMD815f07/LJYbpf/PbCw8uumAuL8n4zLrX+flcO1Lj/Rx79euMn3zTsWQscu8p5VOkp84iF2iqXbRPcGUmfO242vpV1Z+roHvOxvhPjJ9+i8auWwg2djv2+nksfmlbFLMxxyQyeozk2HFKufkT4kppc3rP94gNHSLavgVfuA23L1xT8r5tGeTTEyRH+5g4u8ehaFq0T0dWuaSn17qU1et+9m+nDyVSVhxQqn43H5ilHH2vfJVI62bCrRvw1TXj8tTVtPFYRp5sYoT40CGmBg8sL2GxtJk8t49caozWtXcTblmPPs8q6GYpR2GB/sCrZZ3P9ENK0pOnZ0yE6cnTHHvtGzR130r9qp14A42XeIeSQmaKqYH9jJ16Y07ezRp6dPGxEVzx/aeYjXF639OMn3H2nlBTL55g4/wOPdKmmIuTS42RHDtBYuQoxVxiwbEk4lKTSAhxybtezAR0IbRIGC0apTg4CLaN3tSE0HWMyUnsTBYEaNEoal0IO1/AnJhAmo62KqVFPjV+PudLVdHCdSiBAEJVsIslrHgCO3de+/cEGmZ4MRWPG72tjVJykvSJw04of10INRRCaBrSKGEmktjpy+eqCLcbLRJG8XoBiZ3LY8biSKM6H01rbECtCyFzBdScRFUq/UilfNLZlBQFV3sbwu3CnIxhJRZPqLwgCAWXN4Qn0IA/3IYn2Ihrmr9QOpyUplmklEuQT09QyExSyiUxCukFB3+cb9oJOHF5QniCjXj8UVzeOjSXD1V3lxeGwLbNGfb5QnqcbHKUYjaOWc4z+tyXQ7S0qfzlH8UpFeHmO9z8L78b4fvfyfD0dzIoAj71hSCtnRr/5StxQmGFXe/zcutdHto6vWjeJvr7LH70ZIYTR4zpYeFzvx4iUq/y9HcyfORTAdZvcVEqSt5+tcAPnsiQjDsb3k13ePjcr4f48/8Q4473eLnzvV48XsGJoyWe+naG08cNivkUpVyCaIPCXfd5uWWXh5Y2DcOQnDhiOG0frZxPiqrj8oZwecN4go24/RFcniCa7kXR3I6fpByMYZSyFLMJ8qlR8qlxirn4iueACkV1AhjCbfjD7bj9kbKZVMW2TSyjgFFIU8jGKaSdPpbyqZpC3HVPcE5/1lJCSnteUbmeQL1DmI0jDAuZyTmexani4Qu3Eox24QlO71EC08hTSE+SiZ0lmxguK5OLkVICX7i1KncWwNNZT8evvJfRp94mc6g2M+uFmO63qyVE40M78K9rRWgqxmSaof/vZYrD8yVoF7i8QSfgLtKON9iIyxNC0dwgnNJqllGklE/O2nsSlArpebs9pJQX1R4XdRKc2cgvg7qbevE//AGyX/0m7u7V+HfdjnC5KJ45x9R3voveUE/oow+iNzZgZ3OkX3uDxLM/A2vW5ioErvY2Qve+B3fPatRQEKGq2IUixsQEmTfeJrt7L7JYpJCZnLnM3dVJ+JMPog8MkZsYInD7Lfhv3okWCSN0HVksYUxOkn71DTJvvQPmHIOqqvi2bCJ49y701hZUnxNxZmWylM4NkPr5yxROnqrQRDxreqn/5OOY8QSjf/U3WLG5i6jqTY00felXUXw+Jr/5LXIrJATdXoHHq5CMlcdY2s7EyiVIjdcWVHIp6G5BIKgQn7y4oJS2NdN2JnZuwW0V85I77/Xyt3+WpFS0ueUuD91rde66z8sz/5hBUeH293gZPGtgmtC91sXnf6OOk30ldr+eJhzNcff9XjZutPmdL00wMmgiFAj5otyxy8vqLoup8RSv/dRgzXqdz/6Kh4A3w1/8YRzLAmH5WLu2nt/4rSJuT5b9rxcJ1im890Efa9da/PYXxknnnHHoXuvic1922t77VoFIvcr7P+zj1rs9/M6XxhkZPD9etmWUqe+mSE2cXPD4rBSkbVFIT1BITyxbySajkL5qUgacIKPLBRrJmf0yMdK3zD1yzKlzQe3VER1gBXKLWmvTEC6NVf/8AQIb24m93IeZyqMFvdjFWogqJKV8yjFrXgH2qpXLExSC0HvuRg34KZw4ibu7C09vN5GHP4De3ISdyZCPxfGs7SX03rvJHTlK6cz5l+RZu4aGz3wCNRLGSiYpHDuOXSiiNTbgammh/uMfxdXWSvwHP0YWqjVfNRoh+tijeDdtwJyMkTt0xAmV7mzH1d5G9LEPg2WRefPtCy5UCd17D+GHHgQhMMbGKRzvR6jOCc67eSOu1auIPfE9cgcOzihw+SNHMadiaA31eDdtIPPqG3MOi2f9WtS6OoyRUQonlk74XA63vS/A2s0evvony8tasukmL/c+GuIvf7821oyFoO9wiUBQoblVJZu22XqTmxd/nGPbzW58AQESuno0fvqMk4944J0Cv/HpMWIT1oz+8tBjfv7df4qyZaeLkcHzClFjk8pT38rwjf+axDTB7RH8+z+vZ9d7vfxDa2pGaKmqQNcFv/cbE0xN2AgFjh0q8Zv/R4Sdt7n56TOOxeLA7uq2P/iYn9//T1E2bXczMngjEOwGlh7pQwOc+tMfkO0bWpL7uZtC+Na0MPn8IQb/5wtX3My6EKyoEHS1tTL+P75O8dwA/pt20PBLn8K3Yxu5/QeZ+vYTSFvS+PnP4t20AU/36hkhqIbriHz0EdRImNyBQ8S//0PMmHPUFqqKd8smoo9/hOCuOzAmJki/VM3YoUUjqH4/yWd/RvqV17FLTkSTWhei4dOfwLthHcF7dpHds6/CvOlZ20vdg/chLYv40z8of+9sjmrAT/iRDxK47RYij36Q4sAgVrlfVjpDdv8B6u5/H/6d251TauGCSg66hn/7NgBy+w9i55ef4UTVwONV2HmXHyHAH3Rs8KYpKeZnk9yCyyNQVYFtSYpFOeNuU1TnHsWCzXTReiHA41MwDYlRkqgquNzwSpF7AAAgAElEQVQKO3b5qYtoM+1YlqSQW56VMjJgks3YrF6jk0zYROpVfvjdLJu2uejq0TEMcHsU+svmRsOA2ISF7hKoKigKjAyaGCXwByp9E7FJm5eezc0YCopFyaG9JXbe5ik/myMEbVvy4k9yTE04gyVtOLiniG1BY/P55WaU5t/2DdzAYiF0FUV35l/m0MDcJzVFoLj1me8Ul4ZQBNKS2CWjQsAJXUVoKnrEj+LSMBNZVJ/bCYK27Or7qwqKrs66n3nV5IOvKGNMaXSU0vAI2DaF/lPYhQKKx0t23wHsvHN6Kw0O4d28EbXuvK/Rt3UzrvY2zFic+NM/xJw6b3qQtk1u/wH0hnrCD3+A4J23k9t3ACtZHdac7ztG6qVXKoScFU+QevlVPGt70cJhtGgEY2zc+VJVCd51J4rXS/oXr5B5a3eFidZKpUn+5Gd4N6xHa2jAt2kj6Vdec760bbJ79hHcdQfuri5c7W0UT1Ye9V0dHeitLdiZLLnDC0/2nC8UBR76ZJj7H6tj9Xo30obtdzi+jN0vZ/lvXxnFNEDT4d5H63joU2Ea2zTikxY/+6cEzz2RpFSUNLfr/Js/bePlH6f4/jfj2Bas2+7hX/xBC9/56yle+2maux4K8ugvR+ne4EbVBH/1tMOKf/xAnr/8d6Pks4tPSr4QuazN0DmT7jU6iZiNUZL095VIJGx61uoUCpJEzCI25bzDcFThoY/6uflODy1tKr6Agter4AuIqtiFdNommZjVZwmlonPSm10QwrapOEEClIpO3NpsTulwVOGhx/zcfMestn1zt30DN7BYND1yEy2P3+7UsVQEZ//qJ8RfPVbxG29nAz2/+2EGv/4SgfWthO9Yhxb0UJxIMfHjfcR+fhhp2ihujfZfvZfQ9i70iB8t6KXtM3fR8vhtACT3nObsX/0Eu+isA1dTiKYP30LdztVoQS+lqTSxl44y8dx+7NyVr+q9okLQSmeQ5ROYNE3sXB7F7cEYH5/5jV02ZSou3dm1pcS7YT0AhRP9mIk5nK22JHfgEKH770VvbkJvbppTCOb2HZwziMWKJ5x2NRXFd55hQouEcXW0IUsl8n0nKn2U09dmsxijY6h1IdzdXeeFIGCMTVA4dgLfzu34b95J8dSZ89qPEHg3b0Txeckf6aM0NLcNfylh2/Dyj9LseSXLv/qjFqZGTb755445tFiQmOWhufuhEJ/9Xxv4p/8xxeF38qzb6uGX/lUjpaLkuSeSjA4YPP2NGL/yr5s4ebjIuf4iv/q/NXF0T563XswgbUeo9u0r8Gv/exMer+D/+b/GAEmpJCnkll4AAuRzknOnTDq6NDJpm0Tc5uxJg6GzJms3ucikbcaGTdJJG19A8G/+IMr2W908/e0M3/lqgalJm/ZVGn/4l9U5bdKWzJdlbI5pUgFfQPA7fxhl281O29/+aoFYue3/MEfbN3ADi8XkcwdIvnOK0PYuOr74PoRezRQjVIEe9tPxhXspjacYf2Y3CKi/fyur/vn9lMaTpA+cwzYspn52kMQbx/G0Ren44vuYev4g8ddPABIzmccuOYtAj/jp/u0P4WoMMfnTgxTHEvh6m2n5+O24W8MM/I8XkMbSp+zUghUVgpW+OukIBCkrzYTTO810wq3HjRquQwDG6BhYc+9EZiLhCFWvF72picLxC/xrloUxMbf/S9q2065StkuVoYaCqP4AQteJfvQR7A8+WH2xoqDXRx3GDp/PUfend0HTJLt7L97NG53TYn0Uc9I5xSpeL75tW0BKsnv2XX7nXCKkEhbZjEWxIMllbcaHK08tqgYPfybM7l9k+dG3EkgbBk4W2Vj27f3ihykKOclrz2XoXu/hC7/TyNCZEraUfPuvJzFKjpDPpmyyKZt8zgYUxoeXv6KDbUN/X4n3f9iPqgv6DpYoleDwviIffNzPxKjFmX6DQl7Su17n9vd4ePrbGb76V0mmU+fqG1Xc7uU9irV1aNx+j4fv/UO57fKUbmhScS1z29cK7rjVxZe/WFslin0HSvzF31wdwTJXG6xsEStbRI/4kRfZQ6chDYszf/FDjLjjO8+dHGPtH3yC0M5u0gfOOYeOk2POfTNFpGlRGIqR3n+26l7R927Et6aFU3/8fZJvO0FdsV8cxZjK0PbZu0i8foLUvjNL+7A1YmWF4ByDL8uC8GIQmoZQVSQgS5fYSG05c8pUvNVMJ9K0kHNFfl4CQtcRmqMxKT4fwjM3i4FdKjl/CoWqHKD88X6MkTFcqzrwblg/c1L0rO1Fi0YwJyYp9F89EX/+oEpjq060SeMrX3OKggqgtctFLmPj9SkUchamIfne12LsvNvPnQ8E+YMvDxKfuLIaHcDxoyU+889CNLWq/PhJJ3Xi6IESX/43Ybw+hV/8zPG7mqbEKEFDo0qkXqWQt2lq0fj0F4Isd8F103R8gg1NTtvFgk1ji8anVqDtawWhoMLG9bPSiiTU1Sk0N6pMTFqkMhJpS3w+57PBEZPde6+8ae3dgNT+MzMCEKA4ksDKldDrfLXdSBGEdnZjTKVJH5glIG1JcvcpWj+9i8CWTlL7z15R/+AKV5FYwIOa1nniXZd+8d8pAuFyiILtwlx5PQtgJDBNpGUjSwZT3/0exsilIxztYqkqxUIWi2Te3k10VQf+m3eSeesdpG3h27YFoevk+45jxa9QbuAcsCyJZUmO7Mmz5+XKqg7ZlEU2fV6R6d3kIRRRSScttt7m48g7uZU60F4Ug6dNXG6ByyU4d8p5FxNjFqmkTccqjRNHnI1yZNDk2aezfOhjflav1cmmbYJ1Cm+/WqDz9CXm2RJgeMDg2aezPPwxP91rdbKZctuvFOjsXt62rxU8/1KBV14fm/l/JKzwl/85yrePZvnWd7OMjllYNtRHFN5/v5dHPuDl+z+5AqWz3oUwYpU509K2HSGl1GalUHQN1e/GTOWRZuUByC4azsk07JsJlrlSuOpLKdmFAlYiCR3t6I0NjrlyDueMGgo5J0ApMScn57jTAtrOZLFzORS/zzGnjo5d/qI5kD9yFPN978HV1oJ79SrMqRie9WuRhkF27/LkUV0ORkniD6ooKtiWc4CVEnJpm+MHCni8Cm//POMEsAjQdFEOBnEma+sqnV/9nUZeeCrJ0T15fv3/bObU0QJv/CxT1U59s4KqQZkHe1mVvkza5r/+5ziqKpgYcyRyKmHxt3+SIBRWGB5wBGOpCH/7JwlefylPV7eOYUj6DpU4dazE4X0lzp1yrA7Shud/lOPAnmKFAgCw/+0i/+3PEjPtnOwr8dd/nOD0iUqLRXzK5q//OMHhfcWZtv/mT522V13Y9v4SZ0+ufDHgqw2WBblZ0cqPf9hDNKLwV3+bZip+/j1kMhbf+IcM993j4bMf9/Mf/+9lZMW5iqE1N+Ddtg4AM5Yiv+/IRV1Hl4O0l2aBStPCLppoIW+VhUxoKopbw8qXrjjrz1UvBJGS/NFjeLdswrNuDWoo6AjF2RAC76YNKG4P5lQMY2xpct/MWAxjdBTPhvX4tm8lf6SvZpMqgDkVI3+0j+Ddu/Bu3ogxPoHq81E8fRZjZO4k+uWEbcHhd3J87Nfq+eK/bSKVsBg9a/DKT1LYNjzx36f4rf/Yyu/9l3b6DxdwuQWtXS72vZrl6W/E8QUUPv/bjcTGTZ76Woxsxua5JxJ87jcbGThZYuj0ebPUkXdy7HqwiS/9fjOxcZP4hMnPn0nN+A6XGqYJz32/MsfOMBxBdiGKRclbLxd46+XKvNIXLvjt26/Ozbhy/IjB8SPnBdbgWZMnvlHtk0olbb77zcrPiwXJmy8XePMybd+AgzU9GsmUTXGOeWNbMBmz2bju6t/Olgv+O3dS98i9ABSOnaZw6DjSurLmYWnZZI8N0/jBHXg6ouTPnN+XvV0NqD6341tcIqG7UFwTHojcoSOUhobR6p00CDU0y2GuKHjWrSF07z2gCLK792LG50vXc2lIwyT9yuvIYhHftq3UPXhfZdsAmobe0oxv5/aL+gyBcp5gAd8mJ0gGRSF34OBMashKQkr4yXcS/N1fTOAPKjS16ZjmefrZ031F/uhfDvLOSxnqoiqaLtjzcpZXn3U28rYuF+mkxdf+eJxMykba8KNvJXjj+TTrtnoqlL5XfpLmv//ROLpL0NKpY1sLI8u9gesbk1M2G9bprOmpFnStLSq33uRifHJ5oo6veqgqnvXdCFV1/szlWBYC4dZQvS6nfJnXheLWazZx1oqpFw5hZYt0fPF9BLeuwtMepe62NbT/0j1k+0dJ7Vl5hpgLcU2oTlYiQfypH9DwmU8QuPVm3F2rKJ49hywU0BoacK/uQvF5yR04ROoXry6pvS13pI/kcy9Q9/77qHvwfvy33IQxPoGdz6O43aiRMGowAJbNyJmzWHP6I6E0METx9NkZhhgrnSZ3aPlzAy+GQs5Jd3juibnNR+NDJk99fW5lov9wgf7DlcI7l7H5u7+oNkOXipKfP5Pi58/UVo7mBm5gNn70XJ4v/nKAr/9NA9//cZ6jxwwsW9LdpfHoQz4a61WeeCp7+Ru9C6E1RdEaoxf93tVSR++//Qiqz4Xic6N4ddp++R6aP3IrdrHE4DdfJrX71LL0rTAU4/Sf/4j2z91D979+xPlQOtGlg998CTN15f2414QQBCdHcPxrf0fdg/fhXt1F4JabQFGQJQMzkSD9i1dJvfKqQ8i9lLAski++hDE+TvCeu9Bbm/GuW+OkQtg2drGIlc6QP3rskowv0jDIvL0bz/q1CLeL/L79mFeKLPsGbuAaw+mzJl/+7Sl+97fq+MzH/YTKpALZnKT/lMG//J0k71yn0aGu9mbUuounk5jxHINfe3Hu6hUS8gNO2lZhOE7/V56kOF6pGFv5Eqf+9BmsTLXVqjAcp/8/PElx7CJ7mYTM4QH6v/Ik7pYwikfHyhYpDsdr5BddPiyqisR8oTc3obe2YE5OURosc9ZpGt61vQiXi/zRvpn0B62xwWGHmYpRGhisvpmqojc3oYXDCE3FzucxxifmTI4HUHxePGt6ASddYS5eUeHx4FnTgxCCwqkz2Nm5BanQdbSmBrS6Ood82zSxM1mMqRh25vJVKPS2Vlp/81+AojDxtW+SP7LcRLo3cAPvLrjdsKZHp7FBQRGCWNym/5RBJnv9mtgjv/RhgvffOVMzsNB3ivG/+DqyeH0qBXNh2apIOBDo6ARFhKCI4MLJ0TMokpYJknISY2z8PBXZNEyT4tF+upXN2KxlgBOYmLgmCkSncnhpAKWBAlni9gQZypqGZWEMj2AMOwElCioBUUdYWY8bLxKbnEwTk+MUyGHn8uQOHLrkE8hCgfyhI5d9UmkYGEMjGEMLC2bxrluL8LgpnRukcOrMgu5xAzdwPaNYhMNHr44TxNUAoet41nYtWVHh6xGLFoLtopdV6jq8+BEXxNlIbFIyxjFrD2mq/UsKCu1KDzYWo/Y5mkQnveoWXHgQOFW4JZK4GGev9RKSSse3Gx+96laaRAcqKuermEqK5DljH2XIPlV13ZWA4vfhu2m7wym6dx+yeOkaZTdwAzdwA5eD3tqIGrl8TdcbuDgWLQQVoeDGQ0JOkJIxctIxCwZFmGali7BoZK26g/3Wy1jMnV6g46JVWU2Hsoa8zDIqz1GiiAsXQREhJseqBJmOm03qrdSLFrKkGbcHycs0ilCJiCYaRTtrlR0IBAN2P1eyxofQdYJ378Ld2YExOUV2z/6VZUhQVbRICDVahxLwIVwuh0jXspGGiV0oYqUyWMk0djq7PH0TAiXoR2uMooYCKB4XCKVsUs5hxhKYE/HloY+7VDn4OaAE/WhN9ah1QSeCTgikYWJlcljxJOZUAowaU2Uq+iAvPh1V1Ql0qI+g+L0ITXXeUSaLOR7DjCXnzJNdcqgqWn0YtT6MGvAhdB2QyGIJK5XFGJ/CTmWWba6cxyXGCkBRnH42RM73U9rYhRJWOoM1lXRcJUsYhi/cLrRoGDUcRPH7HBIPAVg2drGEnc1hJVLOuzKXkT1CU3GvW43i91Z/J8QlKtjPgaV+j4qCFq0rvxc/wuWIGlkysNJZzIk4ViK1MnP5Mli0T1BDx42PAtkLhJygRaxis3o7NjbvmC+QJlZ17S7tQ7jwYFJiwO7nnN2HwXlbtlqW0xcK0G5lM73KFlIyziHrdXKcz8NSUOlSNtCrbKVAjt3mC+S5vM9uKeHdshnvhrVOxfbWZlyrOsG2mfrHJ8m+s2fZ2xduF67VHXi3rMWzsRc1HEJ43CguzQnqmc5at2ykZSGLJexiCSuWoHhygOKJM5TODWPFFxfVqdQF8W5ei++WLejtzSg+ryNYpvtgO4w8dqGIOZUgv+8o+T1HMEYnlmRhak31NHzpkwhvOX1FQuybT1E8cabid0LXcK/pwr9rJ+41q1ECvvP9pNxPw+mnlcxQPHmWwv5jFPpOzpTWuhg8W9YR+fhDoJXLSSUzxL7+JObE+fWghAL4btqM//btaM0NKF43QtdmyCFkycDOFTCGx8i+sY/8/j7s7BJH1gmB3t6Md/sGvNs3otWHy/0ok9njzBe7ZGDn8hgDI2TfPkDhUP+SBaT5776Z0PvvngndNyfiTP6/30HmKn35StCPd8dG/LdtR29tRPF5nPFS1fK8trCLBrJQxJyIkT/ST35/H8bAAlwZioLWXI+7pxPPxjW4utrK88N1vk1w2jUtbMNEFp15Uug7RX7fUYqnB2pXnGbaFygBP2o4hBatQ29rxtXZgt7aNKOkzIZdLDmKmpy/gMm+eYDUD15c3JoTznrzbtuA76bNaI1RFK/HEYBKeYxs6/xcHp0gt/sw+YPHsKaWN0hwWX2CJgYmc4XZS+Jlv5wHH17hIy1jc/zOQVomOGsfxaTS3j/X6VHHTYvShQQG7OMVAhAom1fP0KH04sZLvWhlUJ5YyOMtGHpjPYE7bpsJoLFicVIvvbzsDDFC1/FsWUPw/l24ezoRHvfF/QVCgKI4C9njRgX0pno8G3od/+fIBFPf/B6lkwO198PjwnfLVkIP3IXe0YxQq1nrAVBVhFdF8XrQInW4e1cRfN/tZF7dQ/qFN7CTiyNEFrqG1taEWq4OIqVEa66vEIJaQ4S6D9+H7+YtFVVEKqA49dOm++nqasOzrpuxP/sq8jJ9VHwe9LYmZ5wBu6GE1hBxhKAQeDavJfzRB3Ctbp97nGaPUX0Yz8ZeiifPkfjeTykeP7MkyoLaECH0wC58t21zFKY554wzX1RdQ/V70RujeLdtoHRumNRzr5Dfe+TS/L7zgBLwobc1O5YKQPG4UQM+zGkhKASejb3OeHV3znD7VnZzup86BHxoDRHcG3pQAz7i3/5hTX1xr+vGf+cO3D2dzrhcitxVCHApqC4d/F60aBjX6naC77mV/KFjJJ95EWOoNtYpV+8qwh+5HzUaRg36HfYqRVzSB6i4Xbjammpqp1C3uHw9JRQg+N7b8N9zC1p9BHGx/ENFQ2gais+L1hDBs2kNxugkmRdeJ/PaXuQVyJteshQJgUBFR0VFoCAQuISH6RRshYtsgmVMyZEqAXgx+EUIN14sDCxMfFSHB+vChUEJNz78IrTi1tDsnn2UhkccIWgYGOOTWEuUxH8xqNE66h65D/+unQiXvihnudB10DTM8YsrLhftR6SO8Mc+gP+2baCpNfVDCIEaDVP3yPvwbOgh/q0fUDqzNFWwp6HN8qHoq1qp/5XHcXV3UOtolQZGHfNxjRAuHa2pHo6dxr9rJ5FPfBAl6J/3OAlNw72um4Yvf4b4t39I7q0DCxeEQuBet5rIpz+Eq6u95jkjNBVXdwf1X/wY2VdWk3j6ecdMukQQLpez8RMDVcF/xw4in3y4pvECwLarTv+XbNfjJvrLH8F38+aZk95C1pMQAuH34rttO3pnK7GvP0nxRHW1hYtBa4jg2dA7o0BdjdA7Woh8+kN4NvZeWkmYA0JR0FsbiXzmEdxruog/8ROs2Mqmji3eJ4hKVDTTpHTgJ4RLeNHQUMrCUEGZV2BKXs5/M3HjmRG229S7LvFLR2PShIYoB9msFKxk6qJpG8sBrbme+s8/hnujkw4y14KVUjq+Ecty/i2EM2lVper3Ukry+47UvMmr9WHqv/hxPBt6qhaELJuLpGGcryiiCKdSyCyhLcr+DPfa1TT8+meY+tp3nRPPEkGNhADQWhtp+GefQO9srXh+WTanybK/QgilWpjbksKR/gX5NIQQuDpb8O7Y4GzoAR9CiHK7dnl8LEdxEwKha1VKjRACtS5I9LOPIvNF8gcWkG4jwLN5LfW/+jhqtG7OOSBLhkMVWPapCVUpK0hq5fvSdQL33oESDBD7++8v+gQ/00WX7vi8hMB3y1Yin3nUMX2W256pQmNZDuelEOXCsbPmtJRYsSSlgUsT4Fc8e9k8jzq3Eidtx5+OZVXOZVUt+whF1fvSW5uI/vJHGP8v35y/+c+2L03VqCgzp+bzfZO1+9YX6JtzrWqj/tc+gd7RUr2GpBNRL03Lub9whJ5w6RXjKoRAKgq+27ej+DxMff3JRbthasGihKCGi3XqDlpEFyDIkSIpJynKPBYmohz9qeO67L3sGiI4p0+aNhZJGcO+SMAN4CRrync3qa4aCRH9lcdxb+iZeyMzTIzhcUonz2KMTmKlMkjDRKgqit+DVh9Ba23E3dWOGg6CpmFn8+T2XD5tZDYUv5foZx91NFelckHIXIH8wWPkD5/AGBrDSmXAlo6JrymKe20Xvp2b0ZrrZ4SnEMIR7r/6OBN//Q8Yg/PfxC4FNRxCCQWIfuaRGQE43cfiqXMUTw1iTsawcwUnRtnrQWsI4+poxdXTiRryYxeKNZ0sLoR32wY8W9ahlP05drFE4Wg/+f3HMAZHsJIZpGki3G70lga8W9bhu3VrxQlIlIONwp/8IMboBOb4VE19cK3uIPr5j1YJQGnbmJNx8nuOUDh+GnN8CjtXAEWgBv3obc14Nq/Bu22DE7wzveEL8N28GVksEfv7p5GFxeepCV1HCfhxr1lF5FMPzwhAaVlY8RSF46cxBkaxEinsYsmZ00EfenMDrq529FWtKG43peFxzMnaLDHZ1/cSuOsm1FDAUZ8tGyudoXRygOKZQYzhcaxYAiuTB9t2TLcNYdy9q/DdshW9tbFCERRCoHe0ELz3dhLf++m8BE/x1ABT3/jeRajQwLdzM75btlR8bI5OkHz25Zp8kAvxwav1YaKf/2iFAJxWtI3RCXJ7DlM6NYA5EcMuFBGqguL3obe34N28Bu+29Qiv5/z8wfGfhz/2ELFvfm/RpvX5YlFCsEXpolV0Y2Fy3NrDhBzCwpwRaG68NCkd8xKCtcAst2FQ4oS1l+ycPsnzsJEregpcSQhNo+6jDzrcgRdqYrZN4fAJUj97jdLJAafE1FwLT5TrNnrcuDpb8W7fiHBpGOdqqHYvBMEHdjnXXiAAiyfPkXjiJ5ROD1QFkVjxJMbwGPkDfaSff53QB+4h8N7bUNyu8m0FWksjkU8+zOR/+9aig0GEcDbyug/di2fzWsARQLm3DpB+/nWM0YmLJhkLXUPx+3Bv6EGrj2At4rSjNUQA57BnTsZI/OOPyR88Pmfb5ugE+YPHyLy2h+hnH8XV01khCPXWJkIfuIfYPzwz7xOAEvAT+eTDaA2RynljmGRe3U3qRz/HnKqORLWmEpTODJF96wDu1W2EP/YQ7llzTygK/tu3YwyNkvrJywsZmgoIReDuasd13x2oYecEb04lSP/sNbJv7XeUqbkiMIVAuF2okRC+HRsds36Npx1jZIL8/j68N22m2HeK3J7DFPpOYWdySKN6g7YAY2ScwqETZF56m7qP3E/grpsQ2vltVigKvtu2kX7+dSc68jKwphLkLnZqFAKtMVolBK1Uhtyb+5c3WV7XCD/2fseNMEsAynyR1E9fIf3imxeJHo5ROj1I9s19eHpXEfn0I+irWivmj++WLRSPnSbzytsr4sZalBBsEK0oQmHcHmVEnqkSNArqkgtAgLxMY2Kg48YtvKQuEXDzbod3xwb8t22r0BSldELZkz/8OZkX3sDOXUZwSGfzk4ZJ4Ug/hb6TZV/m/DVJV1c7wXvvqCDklVJSPHGGqf/53cufUv5/9t47TI7rPPP9nQqd03RPDsAg50SCYAKDRFLBVM6rtWxLu9balsNer++uvPbm3eu7vmvLXgdZtiSvg0xKlGhRjGLOJAgQOQODwWAGk0PnVOHcP2owmJ7uyQEAPe/zAHjQXX3q1Kmq8+X3sx2X1cgPn8FKZ4l89P1jcRAhBJ7Na/DvvZnUT1+f8ZymmqtrZRMIgZ3KMPLDZ5xNYxrNUxomVjxJ9u3D8+8JNWp92sk0w3/9KPnTbVO/8JZN8UIng995hJpf/mKJ9i0Uge+WbaRf2z+z+KkQBO7ejXtCkbU0TZLPvk7i8Ren30BNk8L5Swx++wfEfuFTeLasuzofXSP4wJ3kT7ZRnI0iNQkC9+4Z6xVavNjF8N895lznVOsvpePS7Bkg2TMwu3KBK7As4o+/SPL5NzB7hyoKvsnObY0kGPn+U6gBH96btpSssxaLoK9omJEQvF7h27kJ381bSvedQpGRHzxF+vV3p1fGDJP86QsM/tX3qfnaz6LVV4+tkeJ2EfzgXrJHTy+YW30qzKuLhDqa7GLKYgVLS1CjNI4xyCwksqSJy0FUodKkrEGbQtBOLOB/L0HxeQjefwfCPeH6TZPE4y+SfObV6QVgJdhydlqkEATedytqOFDystvJNCM/eHp2bjrDJPXc62QPnSztNqEoBO/egxqLzHysyaarawhdQxaKxB99lswb787e9bIQdVW2JPXyPvJn2mes8Zo9AyR+8kLZ/VH8Pvy37WQmrenVaJjA3beUudjyJ9tIPPnyrO69NRRn5OEnsSa4GtWqMIH7bgN1/u+f4nEjBJh9Qwz99aMU27tmv/5zvF/W4AhGZ+/MBeD4U96DgyEAACAASURBVObyJJ99vdx7IRTcrU1zms/1AOH1ODRt4/YdaUvSr79L+o0ZCMBxMLr7SDzxUpnCrdfF8O3avGBzngrzekJTMo5EElXqCItqNHTU0brBZmUtK5VNs4r1zRQSmw77NDmZISYa2KreRlTU48XvlGPgJySirFA2sEG5aazW8L0G15oVThnEBDdo7ugZUi+8tbiFuuOg1Vfj3bquRNuWUpJ567CzYc0SMl8sE+COW7Qa345NCzJnKSXZgydIv3HwmvUzs1NpMm8cnLWbLnf0DMULpWUrQhFOjLFS4fQEeLdvdLJTxyss2TzJn742pxR1o7uf1Mv7nGSeK/MRAt/Ozeh11bMerxJsw1HsjK6l7785HxTbuzD7J3RXEaBVT9714XqHe+1K3KtK9x0rniT90tuz33Mk5A6fwuwrXSOhafh2bSpX8BcB85IO3fYFqpVGfATZqd5NRiaQSDzChwsP3XY7buGlRjQu1HzHkJCDnLEOsk7dSbVoIKY2YFDAxkJFQ8OFQJBiBGG/B3n1FAXfTVtAK72FdjZP8vk3l5Q817Nh9Vi85gpkvkDmrUNzzjozuvoonL5Q4kpy4inbSL3yzryZZexMjvTL+5wuvNcIhbZLDqvILCGLBpkDx51EqHHWnF4bQ2+ooTBFRq/Qnc2lZDwpKV7qpnB+5qn7EwYgu/8YoQfuLHkOFL8P746NGN39U/x4Zihe6iZ76MS1JH6aE6RhYlzux716Rcnnwuuev0v9WuDKvjOhZKNwvgOjZ2732c7nyZ9px9XSUPK5a1ULit+Htch72bwswTQJjllv0SPbMSkSEBF8IkhOZjhjHeScfZi4HMDAqFgmIXGK7S3MOfF7DsguDpuvcME+QUIOIpFo6NjYJOUQnfZZzlmHMafKHr1BoQR8jjY24XOjq4fC+UtLN5HR4uWJrrViZw/GwNxjtdIwyJ0oL0HQ6qrR6+dvXRjd/QtefzgbSCmd+zRHYV5s78LOTOhCryplm+1EqKOMIxOziHMnzs0rG88cTlCYaPUrAveG1U5K/DwgbdspxJ+kV+f1DmtC3aQQTinFYje0XQwofm9l79Pxs3NXUGyJ0dlT1mxb8bjRG2rmMduZYd5+wpQc5qS1f6xuz3FW2mNML132OXrsi1gVCuEtDPabzwFixoXyE5ElRbt9gk71DFgCpBibg431ns0KVQM+tLpYmQsyd/zcklo3wqXjWlGqwTmWRc+8N61i2yWkZZewqKgBH3pDzayZNyqOPVcaqwWANEyMiW6yWcAcGMJOZ52GzlcgBHpjzZQWhsN5WWq1IyXFtnkqTrZF4XwH3p2bSjJXXSsaEB7XvASsLBRH44Dzm+K1wuTXfuMJQTUUQKuPlXwmi8asy3MmwspkHYVwvGdLEVM2C14oLEiwTGJjTmLJOcJo8s1wPE/oXOGLqHz1T7fw3Lc7Of7y/G7GjQKtNjaWMTcG26bYsbTWjRoNo3gnJD/ZNmbv4LxdPWY8iZ3OokTHseSrKlp9jbN/zGP44uWFqTmcK2TRwErMnVnFzuawkhn0cfqHEMKh9nLpk7rDtYaaMgvETmfnn6kocTZC0ypxlSl+H1o0QjE5d25Ru1As4VldxrWDXl9dUvIBgGXhXtVcqpDNEq6VjeXvsxCoIf+cx5wpbqiMkVC1C9uWpIdLNSuhCKJNHtz+qanZ3kvQYpEyRdLO5OZVuzYXqOFgGaWTtGzMkfkTFEjTKUnQxglBIQRarGre8ZSlZKSoCNMsI4WeFaRTYzkRit87lvlaCVqsquwzO5PFXoDCdiuZRhaNkudBKMLJ6J2P69m0sOZAT7coEAIl4EMNB1GDftRo2Ok24veiuN0Os49LH81A1hEubUmsmaWCWh0tKzdRfF6qPv/gIpxNlCv6i4AbRggKAfd+qYn+jhxvP3pttfjrAUoFrcvOF5C5pY2bKD7PVRb9K5CyPF41F1h2xRIPNeQffRHnJgSlLeeU8r6QkLaNPc85VFob4XZPTlYOqMFyzdrOFxdkPexcoZziS4h5WQiA0+VkiTKdK0II1KoQ7nWteLdtQG+uQ6sKO22UFqAE5EaCGp7fvZwVBEsSN523EHT5FDx+DUUVWIZNNmliGaOk2aogGNVJDRt4Aiouj4ptSTIJY+yYK/AGVdw+FWlDNmVi5K+6Vz0BlXCtmzW7w+QzFlUNTlucfNoklxqXlj02joZtS7IJA7NYeh6XV8Eb1EBAIW2Rz4xP64ZAlU4+43Br+sI6igKFrFVynusBwl0h2cC0puYZXIx5aFo5pdMoVdu8Mck4TqLFPF4OOU2PuqWAZN691CquzTQJF5WSVKRlLUiZiDTNsuQGR5ufX2LMtew5p1aFCdxzi9PiarSs5J9yF/elKFlYasyPNm2Nj4/+xioa1vlx+1TyGYvjLw3y9Dc7yKctYs0efvkvtvHy33Wx64M1VLd4MYs2B58e4Jm/6MAo2AgFtt4b430/10x1ixfLtOk4muLpb3bQdyGL5hJ86JdXsunOKHWrfdS1+rj9U/UAvPZwNy/+HycjTQAtW4Ls+Xgd9Wv8SEty/JUhHv/jdvJpR4A1bwzwoV9eScvmAEIR9LZleOabHVw45LjGvEGNn/v9TRx8up+mDQE23xXF5VE4+sIgj/yP89dVNnMlbX+MTHhJJzLJhrBQ06h0Pe+ZTWie11FpjcU041b6SrIwCWRysjndgPdLCDybVlP1+QfRm+srCr+r5N22Q7Z+haBeSsd6LRrIYtHpBRhaQgtqESGUCvuObTvu9wXfehZImZ4G8xKC+bTFkRcGeeJ/t5NNmmy9J8Ynfms1Z9+Jc+KVYYRw4nh7PlbHY99op789y84P1PDBf7WCs+/EOfPWCCu2BPn019fy5g97eOcnp/H4VT70yyv53H9Yx3d+4wTZpMnTf9bBm4/08It/spU3H+nm7X90MgON4lUNUdUEW+6K8uM/uEDniRQb7qjik7+1hvbDSQ482U8wpvO5/7iOgY4c3/ylY1im5IF/0cJnf2cd3/raceJ9BRDg9qm87+eaefvRXv7yV4+jexQURVxXAhAmswKUGTGGLChMx2qeuM1V7PM2WwhRcRzn2q+zGzJbXOl2MB9UWmPLZqpmqpM9N0Io815RoakV+sjJuTeTvYbwbF5L7MufLicXH+XkNYcTFNsdEm2zf2g0iSvjuIQLRUcwSkcrqPrUBwnef8e1u5gFRCVPkzkwzOC3Hi4rBVmQ8y1BeGdeQjDeV+Cdx66mqh94qp/3f7mF6uarrBW2Jdn3WB/n9jkksIefHWDv5xuJNXlQVNhxfzWJ/gKvP9xNNuks8NN/3sG//tudtO4McfLV4VF3pIltSYp5m1yq/EZICYeeHeDEK0NICcdfHuKuLzRS0+oFAat3hQnXuHj4P5+lt82JV73895f5te/uYMXWoCMEcWRI58k0rz50ucyVej2hUvmBE4hfWneFnc+P1rqNc3kpCsqVTu7zgSIQnnLaPTubu/FloKrM202o+CqsTXFcm6oKqERALlz6gigtV1rklEBWjl1ez1BjEYdcfDxFn3Rs5WJHN6kX3iJ/4uwokfb0Av6axjMXGBUZhUY5eBe7O/xiYV5C0F+ls+uBalbfHMYb0FB1QbjGhRj3HpiGzWBnbtz/JWbRRtUFiiqINXlIDhRLBFtyoEghY1HTMj0F1BXYlmSgIzdmsdmmxMjbqJpACIg2eQjXuvmF/7VpLB6p6gJfSCNSN54DD/o7spMKwJVrdH7rv0YJhRXSKZtv/Jdhzp9e+iQLs0JKu+LzzIg2ayFhJTPORjDutEIRKOHyRsezhVDVim4kK5688Zg2JmK0S/18oFZYYzuXn3JjrsRQo/i8CxLrUQP+8uav0q74rF63EILAXbvRm+tKPpZA7uAJhivwpE4/5sJN71rDrJBVrXjcKD4vsLhNwxcLcxaCiir41L9bQ22rlxe+28lITwEh4Bf+1wTSUwn2JIqQlJDPWIRrXai6GBM8qi7Q3IJ8ZuZuFAlY1uQbYzFrkRwo8Mw3O8jES4VWf/tVIS0BOYXi5vEK1m50URVTSYxYeHzXJjvM7B92FnB8406PG70mOv/C59nMYyiOnS+UCitVRa+NTf6jGULxecs2emlLzL6hG14IKro+rziRcLvK10ZK7ERqysJ0o7vfecjHbcxq0O/0NZxnwbMai5RZt9IwMQdunM1RuF34dm8rS/YyewcY+f5TsxeAOB6a9wrMK30Hx+07it/rEDAsQMeQa4E57+Buv8rqnSGOPj/I4ecG6TyZckh8Z1GrZ5mSc/vj1K3y0bQhgFAcAbjt3hjShotHr9a8Sds5PhhzoajOPRAzmr0ACZdOpDBHLcALh5Kc25+g/XCS7rMZEgNLx7O5ULCG42Vd34UQuDeuXtJ5yHy+Imeg3lxfOYN1FnCtaETopc+TzOcxeufOtHLdQNecbMM5QotFUPy+0g+lxOgfmjKb0hocwU5NiN1oKq4V8+T3VRRcrU3lNH59QzeUO1Svr66geNlkDhyfdVPeK1CrQtMfdIPAGBgu23dQFNxrp6bru54xZ0swnzZpO5jg1o/XE6p2oeoKkToX8f5ZBDIlHHtxkHV7Inzhv6yn42gSl0eldUeI57/byUDH1ZcnlzZpOxDntk/WE21wYxqSE68OceKV6ZgkHMHXdTrNaw9185FfW8WuD9ZQyFr4Qhq6R+V7v3uGeO+NxUtopTIUu3rxhErbF3k2OmTWS9arzJbkT57Hu31jCV2We1UzSjCAVZijFaAoeDatKYsxWfEUxjVme1kICCFwrW5xrm8O/KF6cz1qcIIQtO1pu3aYw3GM7n6Uic/NlnWkX90/53IExevGs761jMav0HZpScnc5ws1EionfzCt6fsXTgLF713cYvkldojYqQzFjm4829aXvO+eresRT72MXADShaXGnIWgtOHR/9nGtntjVK/wkhrK8fx3O2na4B9zN2YTJq/+w2WGL18Nphp5i30/7qXrlKONFrI2P/q982y4vYqWzUGMQoFXH+qm80QKe5x70zIkT/7ZRTpPpalt9WLk7bFklmLe4rWHuultu6qhmIbNu0/1M9ydZzRJi1e+d5m2gwnW7YngDah0nUpz4VCS5IAzjlGw2f9431jizPUMmS+QP9PuCIqShp1V+HZvJfXCm0v2ghROt2Mn0yUatBL049ux0WnpNAeoVSE8W9aWWBZSSqez9zy7y18vcK9pQQ0FKjK/TAlFwbtjY7mCkM5O28RWForkjp0p8xi4V7fMi5PVs3kt6nh6OxxquPyJc9e0zm+2ELpWXtIx2qB3LnC1Nldk6VkYyJL2VVcg3PqilaXIQpHcibN4tqwD9eo59MZaPFvWkXv3xKKcdzExr8SYbMJk32OlL814gZceMXjmL0rjU8Wczct/d7nss2MvDnHsxaljEvmUVZKNOvb7rM2zf1l6HrMoeeOR0t5jtiW5dDzFpeOVqcWMvM1rD904fu3cu8ed9jXjWECEpjpdvU+1zZtkeqYoXu6lcO4i3pu3XrUuhCBw1y1kD56YPUWZEPhv3+lo0OMti3yBzP6jCzjzaws1FHQUluffnJWVoTfV4R3XyR1Gra7TF8pdVRWQPXSS4AN7USNBp6PBKCNK4K7djHz/qVlbPIrPS+DeW0tiX1JKjJ4Bp2HwDQRZNMuvX4g5JTEJXSNwzy0IzyJlbI9m3kopS54FNRJ2+GMXqetG9uBJQh/YixqNXLUGXTrhD91N4fylJekGv5D4p8X58x6D0T9E9p2jTqHuOGg1UaI/9wmHaHopYNkkX3y7JH1aCIHeUk/oZ+6ddSmAe8MqgvfdUeZay59sW9Kkn8WG0FSC992O3lg789943IR/5h6UCUk10jDI7D82o5R9s3/Y6fU4brMXQuC/82a8s+3mraoE778Dz/rW0mJy0yL98r45Nem9lrDiybI1FJqKq7VpdtaVohC4Z09JmGAxYI0kyxKh1IAPz7rWxTvncJz0K6Wu8yvu/arPfhilAjXfrCAql/8sFpaF4I0M0yL5/BtlDPtCCNzrWqn5lS/iu3kLwjOLmj1FoIaDeDavJfjAnTNO7y6cu0j6zUMltFlCUQjcfQvhj99fnsRR8dwKni3riH7pE2iR0NjmIaXETqZJPP3KNW1/tBjQamNEv/QJh5VkGig+D+GPvA/feIubUSvwXAf5k+dndlLbJvXiWxhdvSX3Sw34iP6zj+C7ZVvlQvwJEF4PoQ/dRejDd5d0Frjits68c+NZ7Ub/EPbE/n+Kgu+WbegzVCqFx03wvjsIf+J+lEWmGTO6+8sTjzSV4AfudIjLFwO2JPXKOxQudJa97/7bdhL7ymdwrWqG2ZBBKAI1EsSzeQ3Rf/5xIp/98IyewYXAghBoaxrUNzlDDfZb5HOS5laND33CT0urTl+PxUtPZzhzvIhtOwXp6za5uP+jfmrqVPp7TF59LsepYwWsafa4QFBQ26CxfouLNetdVFUreLyCQl4y1G/RdsbgxOECvd0m5izL9wIhhU3bXOy6zUNDk4aiQH+vxZH9eY4dLJAYsbEtOafsfH9QsGaDi523eGhu1fD6BPmc5HKHyeF38pw/UySdnP3AZt8g8UefI/qlj6MGrgoaIQR6cz2xr36BwrmL5A6fIn/uInYq4xTvjqY5O0XbLrRoGL253okNNdU58aHeQcdVN5PgommRfOoVXCsaca9bObZJKy6d0Af34l7dQuqltymc78DO5h3mCSkRiopw62jVVfhv34X/9l1Oun7J2CaJp16h2N456/W5XiFNC4RTC+le30rNr36J9Etvkz18EjuVwS6aIG2EoiDcbvTmOkIP7MW7Y0MZK5CdzpJ86pVZub+soTgjjzxD9S9+rqRUQ41FiH3503i3bSD92gGM3gFkoTgae3IYfBSPG9eqJoL33jYaG5pQTjAwTPzRZ284KxAcl3v2yClCDTUlioZWGyP65U8R/8HTFDt7ypN9FAXF78W1opHgfbfj3boeNNVR4DI5FJ+nnGN3AWAOjlC80Il2U2jMUr2iBMe+8hkSj7/oKDuFokOiIITD6qOqDsOPpiF0DTtXwE7PvFOHnUwz8v2nqPlXXyhJ/BGqE692r24hd+wMuUOnKHb1XD2/M0GEOvrexyLozQ24W5vQm+udhtmqOqrQLU2B5YIIwfpmjb95ohFVhf/xbwe5cNbgP32jhrUb9TGy/wc/HeD3f3eIV5/Lcuf7vfzb/x6julYd6wv3sS8E+eP/NszTP85U3HOrYgoPfNTP3R/wsXGbG69PODzBJZkTDnVffMjip49l+PtvJRgenD4oLwRs3uHiq/+mil23etAmxMa/8C9CnD5a4Nt/HKf3soVlzlxYqSrsutXDl34pzM49bnSXKBlbSjCKkqMHCvztNxMcfDs/u2RBCdkDR1EjQSIfv7/EjSCEQ17s2bzWSaCxbKx0xkkssWznJXC7UAI+J54jmBdBsDWSYOR7PyH2Lz/rlEhceSlVFfeGVbjXrcQaSWL0DmKnM0jbRnG7UGNVTp8yt2uU+nKclWOYpF7aR/rlfQtC8ny9oNDehTWSwLd7K0JR0GqjRD73YUI/cw9GTz9WIoU0rFEFIYreWOu055nIX2mYpJ5/g/yptlnPIX/yHPEf/pTI5z48pkAJIRAeN/69N+O/dQdG3yDm4Ah2Lu90U/B70Wqr0aqrQFVK7tcViz3+g6cpXpw6S/W6hZRk3jiI/5btzjWOQgiBe+1Kav+vX6BwroPi5T6sVBpsieL1OJt5SwOuplrQnPskpcS43EfiJy9Q9c8+glYVnuLEc4Rtk3r5HTwb15QQZQgh8Gxag3vNCozuPszBuOM2VZw94UqBuxrwofi9xP/xuVGFd+Yotl1i+Hs/IfqlT5TQywkhUIJ+/HfchP/2XchC0WmzdUVxUB2iCCXgRaijm63gmhGTL4gQFDibvcst2LTDzX0P+lm9TscoSlRVoGoQrVH4yq+HGRm2+JV/V0W0WqVYlCiKQNMhElX58q9HOLy/QE9XuTl41wM+fvW3o+ij3gUpnQbqti3Hajc1TaCoEKvV+PxXQtQ2aPzebw+SSU29eW7f7eY//kE1jS3a2NhG0RlbCIeXdOtNbv7TH9bwnT+Oz7hxu6rBRz4b4Jd+q4pI1NECLcupd5TSYVbRRtdt950e1m128ef/c4Qnf5Se1iIugS1JvfAmslAk8on7UcLBkgdKCOEskKI4L+JivIyjKF7qZug7jxD92Y/jWt0ypv0K4WifWnVVyeYyGaSUyEKR1ItvkXj8pXl1Jr8uIW1Gvv+UU1qwZZ2zTkKghgIzKqKXo8TNqVf3k/zp63MjD7Al6TfeRUqbqs986GrZhBCOcHPpuFoacLU0TDcSUkqsoTjDDz9J7uCNlyE4HsblPhJPvETV5x9EeFwlm7vwefFu34Bn+4arSlkF5fGKABz6P49iXO4jeN+wU36xCBt9/lQb6df2E3xgryPkxs/X7cK9qgX3qpZJfy8ta87ZpLmjpxn8ziNE/9lH0JvqnWHGr4UQCK9n3uxIi4kF7ScoBHzgY35MA/7ovw2z/408O3a7+ZWvVxGpUlm7ycXXvl5FdZ3GX30jzivPZmlo1vjN/xyleaVOY4vG9t3uikJw36s5ertNYjUqJ48UOHKgwOmjBbo7TfI5STCssH23m499PsjajTqqKrjrfi9vveLnyR+mJ/XoVcUUfvW3q8YEYCYteeYf07z0TJa+yyZev2DTNjcf+mSAHbe4+epvRvD6p3drCAH3PODja1+vIhhSsEx49608z/4kw5kTBbJpSSjizPkjnwmwbrOLcJXC175exWC/xZsvzbIMwLJJv7af4uVewh95P97Na0FT5/TSXfHzz7UtU7Gjm4FvPkT4I/fiv33XGCXXTOZy5dxmTz+JJ14ie+D4ey4OCE7HdSuRYui7PyLyqQfw37oTKlh6lSClRObyJJ97g+RPX5tfHZ5tk3njIObACJFPPoB73cpZeQMcYWyRP3Ge+GPPz7me7npD+vV3EZpG+OP3OZ6S8etxRUlQy9foCsF2/uR5Rn7wNEaXU9NavNSNe33r4kzWskg8/hKoKoF79sz4OVoQSCicaqP/T/6O8IfvwX/rdoTXU+bRmdFQo8+Nnco49a5TEMEvJBZYCApiNSp/82cJHv1eCtuCyx0G6ze7+PSXgqiqYMduD4/8TZK//1YC04SL5w0e/0GaX/qtCJom2LDFxbM/yZRd/0CvxTf+yzCJEYuONoNMesKL1glnjhc58Eae//YnNazd6MLlVrj7AR/PP56hkC9/MYWAn/l0gE3b3QghyGZs/vf/GObpR9MY4/aVcycNXn8xy2/8bpT7P+JHrfDwT0R9o8Yv/maEYEjBtuGRv0ny13+SIJm4emHdnXD6WJG3XsrxO79fzY5b3IQiCr/wtTAnDxeIj8zyIbAlxfOXGPzWw3jWt+K/fRfu1S0OndUM4hFSSuxUBrN/iNzJ82T3H53zhmYNxxl56Amy+48RuPdWPOtbyyzUCSdHGibF7n6y7x4n89ahG5aQdyYQuobi1rHiSYa/9zi5Y2cJvu82x3p26RXX6Up8qXC2ndTzb5I/e3FOhfYVBqZw5gIDf/b3+G7egv/2m3CtaHCSOqa4X1Y6S7G9i/TrB8gdP3dDxgAnhWWReultip09hD5wJ+4Nq5wmupOsh5ROt4xiVw/pNw6S3XekpJ61cKZ9UTtJ2Nkc8R8+Q+HcRYLvuxVXawuKZ4r7d2Xelo0VT2LNoLRmKlgDwwz/w+Ok3zhA4I6b8Gxcg1YbmxExu7RtrOEERu8A+ZNt5I6ccpL9piCCX0gseGd504RXn8uO8YWaJhzcl+ejnw/g9jiC5qWns2MuRSnh7IkixYLE7RE0NGsIUW642Ta89fL01lH7OYMnHknzr/9DFCFg3SYdl1tUFII+v+CDH/ejac6D8voL2TIBeAXDAzbf+oM4229209A8dcq/EPChT/lZudo57sRhJ943XgCOR+dFk7/+0zi//5e1uD0Km3e62bHHwys/nVvRvszlyR05Te74ObRoGK0uhmtFI1ptDDUcdDY3RUEaxpi/3hwYduI/fUNYIwns7Pw3NGmY5E+1kT93Eb025sRMVjah10Sd+IUQ2IUi1kgCo7uf4sXLGL0DM6p1mynMgWH6//C7pUqAhGLXtWWdEYqTkEQ2jywUye4/Ru74OVxNdbjXrURvqkMNhxC6iiwamEMJipcuU2i7hNk7uCjuYTuVIf3yO2TeOYpeX4OrtQlXcwNqNOxsqLbEzuYxB4cpXuqmeKkHs39oQeeSffsIxfOXSmL9smgujLCfLWybwtl2Btu70Oqrca9uwbWyES0WQbjdICV2voAVT2L0DFC86LRWstPl723uxFn6fu8vrg6dzi34NcmiMfocnUVvqMHd2oLeXIcWjYxliEvDwM7knDn3DWH2DmCOJLAqEKvPGqZJsa2T4QtdqJGQs++sbESvr0GNhFA8bofFMl+8um69g5h9g5hDcayRxDXx+iy4EEwMWwwPlt7cvtFMTbcHRoYs+rpLLzSdtCnkJW4PBMPKvMkOTh8rYJoSTRMEQwraJFe5YaubxhZHUOXzNs/+JFNRAI5dx2WTt1/J88l/PrUQDEUU9t7nQ1EFtiV54YksI0NTazVnjhe5dMFg3WY3qgq33+OdsxAcg2VhDgxjDgyTP35ufmPNB6aF0d2P0d1Pdt+RJT21LBoU267DrFIhyjrAy1yewvkOCuc7pv15eMvNBDdsJ9/XxeAbzy3o1GQ2T/FCJ8ULk69b7b0foWrPzpLPikP9DLz5HHKqlwio2nk7/tUbSz6zclkGXnsaM55cOsq/GUIaBkZnD0Znz/QHTzZGvkjh3PT3dSEgcwWKF7ooXrhGyUlSYo0ksEYSFE5fuDZzmAUWXAgmEzbFQqnVlcvKsRhyJi1Jp0oFgmHIMaVI1+fvyy7kJUbREYKKIiYds3WtnYlebAAAIABJREFUjtfnfDc8YHPhzNQarWXBiSMFPvaFwJQu0epaldXr9bHfHHpneqsql5X0dFmsG61Vblml4fZUtmCX8U8cQuCurie0fhuKuuCv8IyghyJ461sQqobiciE0nZzvIkJVkdMYhqrXj6euCUXTEZqO4nJjphMM7XtxaSa/jGWMw4K/QfmcLLPybZsx/6ZRlGVCcnzYaSadITQNPD6FhmaN2nqVUETB41VwuQUuFzS2aGjTCFNFhaYVGsqoyzqdshnsn949MdBrUshLfP7Jx29p1fF4rqSNw4OfCXDPB6a2BFUVmlY6t0MIgT+g4PUuC8FlXJ/ofvJhhKKiuN3E9txLdPfdM/7t4L4XGTrwKoruIrB6Iw0f/OwiznQZy5gaixATlFPy5dpWuZCcKbw+wa13e7nnAz6273YTiqhoGqiqYDTDHHHl32l8qooCwZAydlwmVW7BVkIm7ViZTMEMVFOvjsU0XG7B5788+1YqqirGBPQylnG9QVom0jKxjQJWbpYxXNtG2kUso4iZSrwnskmXceNiwYXgdM/zXB/3Vet0vvb1Km6+3YPHe1XA5bKSwX6TbEY6blBD4nYLNmx1oSiTC0IhKBmnkJ9ZJpJpTC3kgZISCjnXF1yM/bWMGcClBwgHmkllesgXrwb5w4FmTKtIJlfe83AZy1jGMhY+oLAISl19k8q//39jbL3JKWXIZW0OvJnnpacznD9tkE46VpxhSExTsmGLmz/62zpcU9H2ORnNY1C1mQmcK3XnU2F8C6ihAYv/+TvDJIZnZ/7mcpJk/BpkxN2gCAea2LHuC1weOMjpi08ipbN2rQ17yeaHONe5sMkjy1jGMt4buDZR9VniY18IlgjAP/29EZ54JD1pvKxYlNMLKgnZ9FWTzudXUJTpW595vGJagTm+vk/XBedOFui9vCzQFhuWVSAWXk3QV0cyc+O0xJoPJBKhqnjqmgms2YSrqgaEoBgfJN12inxPJ3IK+iEtFCHQuh5v40pUrx+7kCPX00W6/TRGYvi6dFUqugtP40oCrevQwzEQAiM+RLr9DLmeS9Nmpy5jGeNx3QtBf1Bw290OJ56UkrdfyU0pAMGJHU5WFnEFtuUU4F/pxRUIKoSrlGlLGcJVKq5pmjJ0tRvYtpPs4g8o1Ddpy0JwCVA0M8RTnTTV3EQq2zdmDY5H0NdALLIWryuCZRvE05cYjJ/Dtg18nhjVkfVkcgPEwmvpHz4JAmqjm0mkOukbPonj6hCE/A3URDag634yuQH6hk9QNNJl51t02DaxPe8jduu9CE1HWpZDTqxqRG/ay9A7LzP09ovlglAoBNdtofbuD+OK1joxPttGKCrhrbdgJEcYeO0ZEqcOXVdNcfVQFbX3Pkhw3VaEqmKPFhwrmkZ0910kTx+m/5WnMdMLUPe2jH8SuO6FYDjiZH8KIbAsyYnDhWkzJtdudE2bGCMldFwwKOQlHq8gElNoWqkzMjQ5E78QsHKNjss19dg9l036Lps0rtBRVLjlTi+H9xeWrNP7P10ILg8cZPPqj+P31pDOlhbEK0KjtXEvtm2SK4zgdoXY2PogF7tf41LvPtx6kBX1tzKUaMOl+9m0+mOks32YVoE1ze8nnesnkxugNrqJdS33E09dIl9MUhfdTE3VBo63PbrkgtDbuBJf8yoyHedJnTmKkUmh+QKEt95MYPVGYnvuJdfdQab9TMnvAqs20PChz6LobhIn3iXddhIrl0H1BQiu20Zw/Tbq7/8EVj5Huu3kkl7TZFA9Pho++Bn8qzdQHBkifuRt8v2Oxe+pbSSy/VbCW3ajuL10P/kQduE9xGCzjEXDdS8EpeSq8JBOTeFU8PkFe++bQe864Mwxpz2Sx6sQCivs2evhxKHCpB4gt8fJTp1OwA4NWBzcV6ChxWG/ed+HfTzxSLoiJ+oyFg5CKGRy/SRGrcGzl54p+d6WJicvPIaUFra0UBQNRahEQ6vp6n939Bibrr79SGmze/NXOD/0PPFUJ7s3/TxuPUjRSNPasJe+4VNcuPwStm3i88TYuf6L1Ee3cqnv7SW9ZtUXIH50H30vPIZdvKrAZbvaafn0V/DUNRFcs5lMx7kxi071+qm+4wFUj4+hfS+VFbin204hbZvwlpuI3XIP2c62krGvDQThrTfjb12HmUrQ/cQ/kOu+xJXNIdN+lkzHeZo/8fMEVm8ivPkmRg7NrivCvGeoaHiDtXiDNWguZw8yi1nyqQGyqX6kPfv3XwgFly+CN1CN7gmiqi4kYBl5Crk4uWQfZjHHXDTssbGDtejuAKqqO0ToZoFiPkk+PYSRTyKn4vAUylj6nizZrGc0gwmk49fG43DdC8F00iadspHSqe1bs8GFplOxV6Cqwad+1okfzgT9fRb7X8/x4Gcd9vyPfi7Aq89lOX+qfHBFcYTZpm3TN8k0DXjs4RR33e8lFFFoXavz1X8T4Y/+6zCJafhANd0pth/ota4JU9SNDts2uTzwLptWfQy/p7rse4mN31uDSw+gKjqqqqMqOqOUyFhWgaKZdSixbJNcfgTbLiKlRFFU3K4Qfk+M4eQFaiIbAFAUDVtaBP31CKFWdMMuFqxsmpGDb5YJKTMVJ9fVjqeuCT0SRaga0nYEnae+BW9Di/Pbw2+WxdDsYoHk6UOE1m/FU9eEq6qGfN+1bY2k6DqhjbtAUUmePkKu56oAdCDJ93aSPHWI2G3vJ7z1FuLH9iNn21R0DhCKSlXDJurX3Ik/3ICquRHKaEca28QyC2QSvfS2vcFIz0mkPf3z4fHHqGrYTFXDZnyhWmdMVUeMFlJL28K2DIxCmuHu4/S2vUEhO0OuXSEI16yhfvUdBGMrUTWPQ7ogFEAibRvbMrDMArlUP8M9JxnqPIJRSE24bo31e76IP9IIQF/7Pi6feZmZCsKa1t00b3g/QggKuQRn930PI7/0bEHXvRBMJW1OHC6wYavj4rznAz4OvJHnteez5LLOYus6NK7Q+ejnAnzii0EU4RTl69O4LW0LHv1eilvv8VJTp1HfpPG7v1/NX/5hnBOHC6RTNprmkILf8X4vv/C1MKrquGWnI9E+daTAw99N8uVfi+ByCR74qJ/6Ro0f/m2S86eKpFMSy5LousAXUIhWK2za7mbPXg+FvOS//99DpJLXTyzmRoEEEunLpDO9NNbsKtEuve4o61d8EFV1USgmMa0CHlcYy7oqBKSUDpk3jlY7UTtVFQ2hONZjyN849nnRSJPND7PUPm8znaQw1Ff5u5zjmlV05925MjNfcysoCmYug7u6AT0SK/utHggjpY3i8aIFQlD5FEsG1RfAHasFcKzaSdw1mY5zxG57P66qGHowTHFkcHHnpXtp2fQAdav2oOrl7YKEqqOoOpHatQSqmulv30fnqeewjMldtR5/jPW3/Sz+SNOY0CsfV0NRNTSXl8b19xKMruTcgYcpZIannrBQaFh7J80b70N3V2rZ5TTavjK22xchEG0hNXihTAhK2yQ13EG0aStCKNSs3E1f+z7M4vR0j4rqorp5B56Ao6gmBi9gFhaOM3g2uO6FIMBjD6fZe5+P2gaVUETl6/9PjE/+8yDdnY57obpOZeUandp6lXxW8td/Emfv/T42bZ/eajt1tMhD307y1d+M4PY49YX//U9r6Gw3SCUdIVhdq1LfrGGZkoe+neB9H/azYvXU/KGmCd//bpJgWOGTXwzi9SnsutXD9pvdDA5YpBJOl3rNJQgEFMJRFZfLKfI/eiC/XCI4D0hp0dV/gA2tHx6L/wkUGqq343WHOXruB2QLw4BgY+vPVLQYJ0OhmKZoZOjofZO+oeOLdxEzhJXLTm7tjMmJ0odJD0YA8NQ0sOKz/3LacyjumXlWFhOqz49QVUBipCZPerFyGaRRRKgamj+4qEJQUV2s2PJB6lffjlBULLNAcrCdeN9ZCtlhBAreYA1VDZvxVzWh6R7q1+5FSptLJ346qXu0kIuTTw/jjzRjFNLk04Okhi+RTfRhFjMIRcUXqifauAVfuB4hFILVq2je8H7aDj06ZQuicM1aWjY9gObyYdsWmXgXIz2nyaUHsM0iqubG44/ir2rGF27A7Q2TGrxILj1Ucbzh7pM0rN2L21eFxx8lUreBwc5D066dN1BNMLoCIQS2ZTDYebjEg6IqOk1VO7g8fARrEh4+XfUS9jWiKW7ShUEy+UEkszccbggheO5kkW/812F+7d9X0dCs4Qso7NzjZueeqy+nlNDfY/HdP4nz9I/SRGLqjISglPDDv02hKPDFXwxTFVPw+RU2bB0/tiQ+bPPI/0ny0HeSNLfq0wpBgGxG8hf/X5yL5w2++IthmlZoaBrUNWjUVehTalmSdNKi/ZyBOU3scxlTI57uJJsfoiaygZHkRSTSiQOqLtyuIBJJVXAlVcHWWSWzFIw0vUPHaG24E4EgkxtE1zz4vbWMpC6SzpabTIW2Swx+55GS+IddNCp2G5gtpip/mBSjHTWKiWGyF89dtXadxNer/47CGKm8AS4lrrirHQN9hu/GIvfUizVvp27VrSAUjEKajqNPMtB5qEy49ba9SfPmB2hYcydCKNSvvoNE/3nifWcqjitti+5zr5JN9jLSe4psordszCGO0Nf+Nqt3fYqqhs0IIYjUb8Tjj5JPVxb8QqhUt+xA1Z1s+5HuE7QdfGQ0plh2NLonSDC6ArOYwTYrx4Tz6UHifeeoW7UHoWjEmncw3H0c25raDV3VsNmZh5Rk4t2kh0vJ2lXFxaraO0nlB3BrDj1XPHuZvOEoQJriZkvzg8QCq7ClhZQ2F/rfoHPowKgXZ+ZYECEYH7b5898fQVUFg30WhVypNB4etPjWH4ygu5zvTbN0kv09Jn/1jThuj/P9RJe5lPDKT7N0tBk88DE/N93moaFJw+V26ga7O02OHijw/BMZ2s8bSBt+8nCKni4T0ygn7J6IYkHyD99OcmhfgQc+6mf7bjd1DSoIwWC/ybF3Czz7kwwnDhWwLKc34PFDBYoFSe80yS7FguTx76d56+Ucd77Py023e1m1TqcqpqCqgnxOMjxo0XXR4NSxIkcP5Dl3qjhlN4slgaIQ3nkrrljNjA6PH3wLY2hgkScF7rpG/Os2I3SdfOdFsu3nHPou28Y0r7qYpLTo6jtAONCMZZuApGfwCD53lE2rPoaUNiPJdjp636Q6vA4AW1pYVmHsJTKsPFcco6ZVwJY2Ulq0d7+GYWRoqbsVjyuIaRVJZXsYSpyvOGdrKE72OuqNaGUdoW8khul98bHpk16ug1pBM5dxSjiEQPMHKEzyqCluj1MqUshj5eavZEwG3R2gYe3e0difpPvsq/RfOlBxrUwjR9epFwhGVxCIrkTRXNStvo3k4IVJhUV6uIP08MS4ZymKuQRdp18gGGtFd/vRXD68wdrJhaCi4A3UjCljg5ePTiIAASRGPslw93TeDslg50GqW3aiai7CNWvwBmvIxCev01V1D1UNm5xfS5vh7uNYRvk8NMXFrpWfGXMJ540kxzp/QjLXQ8TfQpW/hbO9L5LIdtNYtZ1VNbczmLpAtjg7pW1BhGAqafODv05N+n0ybvPDv538+5Ehm0f/fvLvwXm22s8ZfPsbcVwegaYKp++glJimI2zGlzOdP21w/vTMg+K25fT9O32sgMvtFMQLHOusWJCMb7J++J0Ch9+ZebaclE5N4o8fSvPkj9LoukBRnfGldLhUTVNiGFw3ZRRCUQhu3o5/9YZpj5VSkmk7vehC0BWrofHTP4cerXZ6ERYL9D/1I5LH3mU42caBU5ew7av3fDjZzltH/xxbOjevUExy6uITqKNJC5ZtgpT0DZ3Asosk05c5fO5hLMu5t/tPfBvTKgKSQ2e/Nza2ZRXo6H2brv53EUJxBKVtjZ3nekeupxOkxB2rGy2Qv/5LCaxsmmJ8EE9dM96mVjIXK7cG8zasACEwMymM5MiizScQXYE/3IAQgnxmxHEBTqEsmMUMwz0nCURXIIRCILoClydEPjPVhj39ZpBN9GAU0uhuP4qqj2WlTgZ7nMvR44tOO/5MkB7pIjPSRbB6FaruJta0g0y8h8nm74804QvVI4TAKOSmFLTxbBcdg+8AsLJ6Dyur93C883E8ehDDyjOQPE/eSFAwUkQDK6nyt8xaCM6gZ8P1BduGfNax7lJJm3RKks9Nz+c5U1iWw0eaTjrjZzOlAnC+MIqOm/TK+OmUJJeVjuV3nQjAMUhHwF35c60tAk9zK3qsxmlIKwSKy01gw1anQbC0Ma2JionEtPLY41xJUlqYVgHTKiClhcTGGs2alNhjAhAYHc+5ZssqTEiSkVh2EdPKY1mFG0YAglM+URwZQPX6ie6+C6FP7toX2vRu/6WAbRRJnTkK0ia0cWfFZB4tFCG8+SZAkjx7FHsR3SmR+g0gFEcBjF+mmJ9aiQfIxLvHniGXJ4jLVzXvediWiTlqRQkhUNTJ75dtW2RGusb4jOvX3knNyptRtOnDRlPBMvIMdh0ZnYNCtHELunvyDgPRhi0omssJMw2cIz9JMo8tLdr6X2co3c5Qup0L/W8S9jYghOIon9IeW0/DKpAtDON1RWY9/xsiJriMpYe0LBJH9lPo70b1+lG9fhS3G6G7UL0+9KrYtPWSC40rWY7jIVStJPNxGdPDTCUYfOsF6h/4FFU7b0fzB0mcOOh0dEAidBeucAz/yrUYqTgDrz1TeSDFySK8IiiFoqC6PEjDmD5WKcRoL0Kn2zhCoLhGXZmWWa5wSUn86DsEVm/C29RK44c/z9A7L48mvkhckRjRPffirmkg39dN/PDbi6a0KaoLb7B23KUIwjWrp30GvYFqrgZbBW5veMbnVDU3msuHqrlRVH1UEVQRioqmXc1KFVNl1EmbgUsHiTVtxxOI4faGWb3r09S13kr/pXdJ9J8fzS6d/bqN9J6iMXsPHn8UT7CGUM0ahrrKG2jr7gCR+vUO+YlZZKjraMWSESkllm1MaEBgoyg6ilBRxMQWOzaWbaAosxdpy0JwGZUhJanjB0kdP3j1M1VF0XS8K1bR9IXpswoXGoX+HqxCHtXtBgTYNrmui8jlgspZQpI4eQihqlTffj+hjTsJrd+OVcyDlI6yoekgbYbffb30p0JQfdt9+JpXIXQXQtXQQ5GxRr/Nn/oy0igiLRMzm2HwrecoDFxl7glvvonQ5l0ouhuhaaheH0LT0JQAjQ9+AbuQd2K8xSIjR94iff4qW42ZSdHz00eov/9T+FpW4W36+bH4puoNIFSFXM8lep//8aK6QlXdje7yjylk0catRBu3znocZRorW/cECVWvpqp+I95gLZrb7whBRRsVgo4gnE0CUDbRQ/vhf2TFtgfxhepQNRehmtUEq1spZEZIDl5goPMQ6eHOinG6yVDIxhnpOUX9mjsQQqG6ZRcjPSfLYp7B6lV4/I4Vn88MkeyvHEe3pUm2MExr9R4uDu4DJCuq96AIjfUN7yfgqUFTPeiqh4KZQhEabj1IpjD7bOBlIbiMmcOysC1rURMOpkL+8iUGX3iC0PZbEJpG5uwJ4u8uLSvIdQHplAIURwYxM5MXF1v5LMbIIEY6Ud7Sy7aIH9lHtrOd4Lot+FasQQ9GEIpKMT9AYaiPbGe7U5NXAoGrqhpX9GrClDQNjLgTh1HdHnA7lonqC6C4SmvntGAYd6yu5DMj7rjDFE0vEQyaL1h2TYXBProe+xuC67cRWLMZ16hbNNd3mcyF06TOHsPMTO+anA+Eok7pdpzFSJU/FQrRpm00bXjfaCH6GCcLtmU48WezOBam0D1+hJj5Vj7Se5psso+61bdR3bILtzeMUFTc/ig1/ijVLTtJDXXQe+Ft4r2nsCbJDC2BtBm8dJDa1t1jgtUbrCUTvzzuwhRiTdsQioaUkqGuo2Ou3Imw7CIXB/exuelD3BJah0RimDnO9DxPXXgjquJiJHOJtfX30Bs/SdBbS8BdTVvfazNeh7FpTdXvTgix7GVaRhk8zStZ8eVfH9OEpZR0fe9bZC+cXZoJjKb4Y8+Wpuk9hCs9vSST14XN5JixY5XSPXmqGPDEY6fCxGD9THqRzWQOE8dawpi1yxdhy13/Cm+wBiklI72nSPRVTtSZCon+82STPWWf17buoXX7R8eK781ilsRAG6nBdvKZIcxiBssoYFsGtm2x6c6vjLG2XDj4KL0XZqoYCjz+KNHGLcSad+AL1aNoo8QK0kn2Gu4+TsexJylkp7esFc3Nhtu+RKTOSaa7fPoFLp246kp3+6JsvfdXcPsiGPkUJ1//Dpn4VExEgoCnhoivGZDEM12kCwMIoaCgomtetjQ/SNjbiI3N5eHDtPW9WpL8cwVSykkfumVLcBk3Hq6jrgbXDDPZ9GcjGKQ9c31iNsfOZ05LOdZsTjtKWXYF2UQPPednb4FUgidQTfOm+9BcXqSUpEe6aD/8YzLxy5MU14t50PRJ8pkhus+9Rm/7PoLRFVS37CTasAXN7UdRVWLN21E1N+fe+YdJrbYrsM0CQ11HCNesHaWS20zP+dcxCo7LOlK3Ht3jWPfpkS6yiXIFYOL80vl+0vnShthS2ljYWIbBkY5H8bmrMO0iuWJ8TvyjN1x26DKWsYxlXEtYRh6jkBlzMbt9kUnpzWaLcO063KNZo5aR49KxJ0kPd0zKLuNkhM4vuxMktlkg0X+OC4ce5cTrf8nw5WOjbeYUwnXridRvnNFIIz2nKWRHEELgDdYSjLUCoKg60cYtTlanbZcxxMwVpl0gmeslWxieMwH3siW4jGUsYxmzgG0ZZJM9hGvXIoTAF25Ec/nGLJ65Q4zWzzkCtZCNkxy6OOUvdG8IVVs4WjtpW2Tj3Vw49Cia20+oejVCKDOmQzOKaYYuH6Vpw/tRVI1Yyw6Gu0/g8ccIxFYigFxmkMRg27RjKUKjOriGWGAVuuarmPl6rvdFssX5JUEtW4LLWMYyljFLjHSfHLPOvMFawrXr5j+oAEW5mvpvmRNrU8sRql49ZU3eXGEU0iVUZprLO7MfXkl4KTpk2KFo6xinqKa5kUC87yzF7HRNjwUtsZvZ1vIxakLrCLhj+NxVZX/mUhIxEQtmCQpNRwuF8TQ0465tQAuGUVwubMPAymYo9HdT6O3Gnqa1iV3IY6UrZHcpCnq46mpSBGBl0tj5mafxAghdRwuVFlSa8ZFJ65qEqqKFq8YC8HYui5Udx3auKGiBEO76JjwNzejhKhSXC2lZmJk0xcF+8pc7KI4MIReiJ5uioPmDuKprcdc2oFfFrlJFmQZ2sYCZTGCMDFIY7MdKJ7HyuRsqjqYGQjMjbZZgZVLzYjxRvD5Ur3+UM1NiJuIlz4LQdfRIDG9zK66aOjR/AKGo2EYRIxmn0HuZfE+XU2M33zUWAi0QxFVT7zxLkSiKx+FXtHJZjJEhcl0dWLlp2PYtCyOZoIx/cCqMPlfuugbc9c65VbfHib9kMxSHBsh3X6I4PIg9j+xgZ5+IjHGUWtkK77Cqooeq8DavxF3XgBoIOa2gjAJmMklhsJdC72XMVPKasd2kR7pIDLQRqduAEArNm+4jm+wjm5icLuwKlNG+fWUuTgnFfGrUDelwd2q6Z9KuDG5fFY1r7xpr2zQdhFAQqoZtTk8ioKg6Hv9VRpniLFocZRM9JAfbiTVtQ/eGCNWsIVS7BoQyRpY9XVBZU900Ve1gOHOJsz3PkzdSFeO/1gKQVMxfCAoFT9MKqm7Zi2/1elSvr3L219gFTH3xqeOH6HnsobIL1gIhWn7+V9ECV9t/9D/zY+IH3pjVdL3NrTR/8avjs47p+M4fUei9XPF4PVbLip/7FRSPByTED+2j/+kfgZRowRCR3XsJbt3lCOiKWW8SWSyS7WhjZN+rZDva5rZZCgVXdS2Rm2/Hv2YjelV0tP/XJJASaZoUh/rJdXWQPnOM3KX2JemvNl/UPvBRglt2TnuctCV9T/2Q5OF35nyuqlv2ErvrfhACadt0/f23yF264DzXzSuI3noPvtXrR4u6Kz/XZjJO6uQRRg68jhmfm2tGC4UJ77qd4JYduKLVk9/bGTQuNeIjdH3vWzMjvhYCvSpG5KbbCWzc6jCxTPYcmxaF3i4SRw6QOnl41googKexhZYv/dJo1qpk8JWfMvz6C2Pfu2K1VN16N4GNW1F9gUnX3C4WyHW00ffUj0aL/JcWlpnn8ukX8Uea0N0BvME61u/5Il2nXyDedxbbKo7FDIVQEIqKyxMiVN1KuG49/Rf3E+89PfHCSMe7sG0TVdVxe8PUtt5Cb9tb2OPafQlFwxeqZ+W2B/FXNTmJSmXF4+XwBKpZsfXDJPrPk+g/RzGXxLbN0WJ1hzVdKAqa7qVm5c1E6jc4XR5sk3jvzDO/5WhRfqR+I4qiUVW/gWBVCwDp4Utkk73TjAAKCi7Nx4WBN8gUFpfAfX5CUFGJ3Hw7sbs/4LQ6KekSfPVFFbNJi57iOKE4D9NMjp1y/FHarbF5TnVOcI4fPa8rWo3icqFHYtQ9+Bk8jQ5X4eTsKQLh9uBftxlvcyuDrz7rCO5ZCEKhaoRvuo3onfehBUMzY2oRgv+fvfcOk+O8znx/FTuHmenJgwEwGOQMIlEECWaQYpBoUsGyZFlXlmWv71q21kHWeu1rry1L9t679mN5r1a6tmxlSjIlMYg5AiQBgsh5EAaDyaF7OoeK94/q6ZnGDCYCBCDhfR6Qz3RX1fdVdVWd75zznvcIqoqrrhFXXSPB1Rvo/en3yLQdm/a4Vw2C4HSsnvI8rckVMmYylig6C43qWnJdHYTWbSJyx/1IXv/k8xAElHAlFVu34124mP5nfky+58KMpuBunE/t/Y/gqmty5lHEuGdoZL5TnZIoMq0aBlEksGItke33OQpA4mTZEQFBEXE3LcDd0Ix/6UoGX3wKbWgWjQbFEdkre0xURsC7aCm1930QpbJ6ymsuuT0o4UrMwswN8eVCMtpOx5GnWbDmYUe8OlhL68aPoOW5MasDAAAgAElEQVTiZFMDmFrOUdFRPLi8FaiekKP2gkCs+8jExxw85+hwVi1AEGXmrdiBv3I+6eh5TENDVj34wo2EalqRVR/ZRC+51ABVTWunfF4EUSZUs5jK+hWYpkYuNUQ+NYBWSGOZGqIoo7iD+CuacPsjJWmy4d4TJAZnVgKSHDxLPjXgzLV2aTFvaRPtOTppL8URWFhoZhbxPcjYzckIhtZtInLn+xFV12htiWlQ6OtBiw5gFfIIsoISqsDdMA/R7RlvKG0bS9cw00mMVJJ898xeIO81lHAlrpp6qu/5AO7G5tHzti1sw8TWCyBKiKpaWs0LRSMpeX1Etu/AzKRIHTs4vQEFkdCGrVTf9QCiopZegnbRI7BNE1vTsCkqfUhy2Yty5HqbmRSF3qvbHXy60OPD6LEhRLfbubek4m1aPJcrKdfmqm0gsGIN1Xc/hFgs+h57n9qGUVRUkcsWP4Io4qprpPaBR+n+4b9hJKbnEarVddQ9/BHUSG3pXsK20YejFPp7MDJpEHBClXUNJc3MskUcYJsGZiaNkUpSGOidujOEKFGx6Raqtu9wwumCE5+0i62KbMu5r5AkREUpu5eRJHyty5H9QXp/9j20galX9peC7AuAIOBduJi6hz6MHAiVnVcpIiSU/lO6TtmOs9jaVYxs2DZDFw5gaHmaV+5wevuJEm5/pNQstnxz51wsQ8O8REjS1HN0HHmG1o0fxu2vRpQUqhpXU9W4qhQmHYklp2MXOHfgCRR3wGFeTlXAP6K1KYjIiptA5TwClfMucWrFOsHeY3QcfmpahmssDC1LtOuwQxoq1jtquSTDvcen2NOBaWoMJtuoC68gmm6nYMyVdHRpzNoIqpFaqrbd7ShE4Fw0baifwZefIdfZ7oRKij+6qLpQqqqJ3LYD3+LlpRWnrWv0P/cTchfOYWkatq45orfXQOuWS0EJhanZ8Qiu+kZH/y6fI3PmJOmTRygM9mLrunOT+QP4Fi0lsHoDSnhUZ1Nye6jadg/ZjrMT5z4vghqpoWrbXQhFA2jbNmYmTerkYbJnTqLFhka1FiXJeVnW1ONubMZV24BaVYMgy6Tbjl+VsNFsEHvzZeJ7d4EkIUgSoupC8vhw1TZQdds9pXvuSsC/ZCX+1uUlA6jHhkidOEzm7MnRvJ8k44rUEFixFv/SVY58WNEguuoaqdi0jcFXnpnS2xcUlcj2HSUDCE5OPPbWKyQP78PMpEqScIIkI/kDhNZtoXLrbQhjrkFi/26G39mJVcg7hrporC89sEBw1Tqqtu9AcjuEB9u2MdNJ0qeOkTl7Ei06gG2Yjtfl8+Gd30pgxTpctfUlAXNXXSO19z9Kz4+/hTkLlRZBEJA8XtwNzdTs+GDJAOrxGLnOdvJd59HjMSxdR3S5UCoiuOubSrn3bPtprrZYguMpHSMT76KqcQ0VDSvw+CPIqg9RcpRRLFPHKGQo5OKkYxeI9RwrV1K5CKnoeU6+/e/Ut24jGFmEyxMqKekYWpZ8Jka89wT95/eg5ZKo7iCDF/ajqD7y2Ut3ls+lBzm95ztOYXyoHtUdQFI8jv6rIGHbjhKNlk+RTfQx1HWQRH/b9BRjJkCs5xh1i25B9QSLogKn0HLTfAcJAun8ELWhFdzU8jHimU7HEF70c3fFDlIw5qYQNDsjKAgE12x0CCNFmJkU/U//kFzn+XGbW1qBQm8Xfc/8kIbHPom3ucU5jKLiaZpP6sj+2TUHvQoQJBl3g7N60qIDDLzwM7LnTo+bvz48RK6zndSJw9Q++CE8xbApgoAaqca/eAWJA3umHM+/bLVDDBjx6NIpen/2fbLtbRMuFvTooJPT2vcWktePq7oWX+tykmM1QK9xWIX8hIQHI52g8n23l2S5LjcEQXB0MAHbskifOMzgq8+ix8a3iNKjA2TOnCS45gzV9zzsGGZBQBCcEOPw3l1TeoPuhnn4WpeNenWmSeytV4i99eo4A2qbBkZimNiuF0GAqlvuQpAkpx6ruYXozhcwktN7waiRWiK331/m6eY62xl88UnyPV3j1GX04SHyXR0kD79L1fZ7Ca3dPDr2vAVUbLmNodeenVWuWw5VUH3Pw6iRWqxCgcSBt4nve9vJZ060GB4hD1XVUBiYqtj6vYOWS9B7Zif95/eguhzj4qRQbCzTcGoLtfS0SCkAuWQ/5w78BNUdQHH5EERHy9U0NfR8qowso+WTnN33oymPaVsm8f424v1tyKoXWfEgyS5nnoIItoVlGRhaFj2fnnMdn6HnMPQcqieIZepEuycWy54IsqiysOZ9JbHsSKB1wu0Gkm1XxwgKsuJ4dGNCMqkTh8lNEco00ynie9/E07SgtJr0L11FbNcrJe3B6wVGOkX/s09MKRVW6O9h6JWf0/DYJx3SEIAo4V24mOThfZMbf0HAXd9U+nOkb9+lDGAZbBszkyKbSZE9P7FI7Q1MDNu2yba30f/sEyWB5gm3Mw0Sh97FVVNPePOtpSycHKrAXddIegoj6Fu83PHwi2NqsSGSh/ZOakxs0ySx721CazaihB32nloZwbdo2bQWVYgSFVu3I4cqSmFFbaif/qd/NGV+z0glGHzpaZRgBb5FSx2jL0qE1m4keXgv2tDApPtPBCVciRKuxNY1hl79uaMFO5kxtW2MlJM6uRZhGRp54zK9y2wLLZeYvvc0Axha9pKs08uFQNWCUlg4m+wjNUXN41joZp6D5388ZQo8p839PphV1lEJhpADwdLftmmQ6zg3rZVgob8bIz06ccnjxVXbMJtpXDXYtk3y8LvFcMzUyF04R35MPk4QBNRIHYI6udKDIIqIslKWAzOzmWs6XPyLACuXJbrzpUkN4OjGJsnD+8bl4Nz1E+daRiAoKq5IXdlnhYGeab3cjUyq7H5CFHE3zi8rH7oUXNW1+JesHP3Athje/fq0CS5WPsfwu7vKevVJvgD+pauntf/FGLm3k4f3kdj/9nVVynMlISgSctiL6L7y/RxFj4pS6S//V+FDkGZPShFEmZoFmxBFCdsyGeo8iKnPJKxqk9ViZAqT/7scfTxn5QlKvkAZS9PW9TLDNhnMfB4rl4MxrDAlPPfmku8lrFzWCS9O0xjZpkm+uwNfy5LSZ3IggCjLTPbI26aJmc+NSYiDu74J0e2ZFT39BqaHXHcHua7z095eT8Yx4jGkMYu5qe5pQZaRvN6yBc5IN4UpYdvoY7xMQRBQQmEE0ZGkmgzeRcvKmNz6cIzMmYup+pMj39WBkUqiVlWPTADP/BaEd3Ziz7CRrZOLTBHf99aNllhjUPPAeho+cjPxvWc5/0/PY+tX7trUP7qZ+kc3lxHqjESWU3/+I3IdM29NBFBRt4xgUTKtkIsT6z7KbPK3giAiCnKRBT7eLTSs0cbXs8XswqHSRT2sbHv6N/CYbsDOwZi0s/W1Btu2KQz2ocdmdnPoiXjZ307d2dS1PfnuDgIr1wJCMf+zkKrb7iW26+XpeSo3MCPYtk3mzIkZeSSWrmFkM4wt7xfdkytsCMVQYtnYk5FZLp7nRc+b09h2itiRKOJbOKpsYts2+Z4LGDO8j8xsBj0xXDKCgiDgqq5DVFXMWXRzz/d2lvUcvAHwL6lDCXvxL6lHkMQragRTx7qQvCqy342rNkRg1TxElwzibFjYAr5wA80r70OUXdiWxcD5vdPqQnExPGqY1trbqPDNRxTkksBC6f/Y7Gv/Aan83O6dWRlBq5Ave0kIkuQUk08DoqI65QMjsLnuvBptaGBqCvpFGLe9ICJM4yZLtx0jvPlWlHCls3oXJUeYYMEiEgf2OKzPadLxb2Bq2LpOYaaUf8ssEyEQirWok45jmmUhRXD6700XsrdcKsshEk2hwuEPjt5HAMU8JLPwwC5egEleH6LqxszMfGGW6zp/Iwx6EfqfPoClmcTfPYdVuLKkweSB8yQPnAdRILhqHkv+8rFp71vZuBpF9WIaBURJxRduoLJhFarHYfqmYh30n9vNTL01UZBoqbmF6uASoul2bNuiyr+Q3sQxvGqYgKeWc/27yGpzz7/Oygjq8RhWIV8iegiqC1dNA9mzp6YeMFyJ7A+NfmBbFAZnUXB7FTEtJY5xmJ3LrsdjRN94gZodH0QaqbOUJNx1Tbh2NFCxZTuZc6dInzhMvq97TpJWNwBWIVcuizcdTERinGocXUOPDWE3t4yGumsbEBTFKbOZBKKi4rqIMFUYGpgyGiP7A6PkLABBILR2E75F0+sQMBZKRVX5B4Iw7YXwxdCi49m3v+xIH+8mffzSZRRXBJaNZZgz4BwIVM9bT1XTmnHf2LZNLtnP+UNPzkpYXBIVKnzNdEX3c7r/dYKeOvyuCKd6XkQUJBbW3ELAU489fHjGx74YszKCZj5HtuMswSLDTBAEgqs3kDq6f9JaNEGWCa3dWOYJatFBtOvMCL6nYUjbJnlkH7ZlEbntXpTKqpLKvCCKqJURlIoqQms3UejvJd12jPTJI2jDs1vh/7LD0vVxHtqVGcgic+4UwdU3gSw7ZKnaenwtS0mfOjrprr4lKxxptSJsTSN3/syULy/R7SkjYwlFtZsRluncIDhpklngxsLt+oVp5DENR23GkR50SiySQ+10nXx5Gj0DJ4aAiCy6iGe7nfpF28TGMY6GVaA/cZzVzY/gUcNkCrPLW45gdnWClkXi0F78i1cgFcMyrpp6au57hMGXn3YS/GPDG4KA5PYS3ryNwIpRTUjbNEgc3Du1IPA1BmsGuZvLM6BF6sg+8t0dhDfeQnDluiI5aYwijaLibmx2dFw3byPddpzE/rfJ9/fcMIYzgWW9Z9crc66NfF8X7sb5Tl841UX13Q9i6Rq5zvZxHqGgqHgXLiZyx/vLSityne2OJu0UEGRlVH3nimB2Sj6XFNUXHOai5FKcvJhtY5sWVl7HKujjPXBRQAk7nq4ec94poltB8qggCtiGhZkpOCIAU0EUkLwuJzeGgG2YmNnC9HNzooDkURFdskNYsm1s3XTmrk3w/hBACfsQ5PIwuqUZGInppYsESUT0qIiqjCAK2JaNpRmYuQKYV4JRbtN14mWGOg85dZGCgGkUyGei5FODs+7v5xzZxrR1FMmJLpiWDth41BCp/ACmpSOLKpI4dz7JrJ+I3IVzJA69Q8WW20rai/5lq3HXN5E5e8qRbirkEWQZpaIK74LFuOsaS6tF27JInzlJ4uCe65Dyf3Xmq8eGGHzxSRIH9hBcvYHAirXIwYpS4fJIWE32Bwmt30Jg2WoSh/YSe/s1zGmyd2/gvYOVyxJ94wXqP/hriB6HKapUVtPw2CfJnj9NvruzxLqW/UE8TfPxLmhFGCNTaCRiDL323LRYmRfLzdm2jZFKXJZODLZpYk+zEHw6kPxuIneupOLmxXjmVSH5XNimhZHMke8eJnWsk8EXj6BHR6MyaqWfpf/9wwiSyIk//h6ehRFqH7oJ3+I6JI+KHs+QPNBB35P7yHdeOqWh1gapuW8doZsW4qoNIUgiejxD+ng3A88eIn2y59LvLEHAsyBC5M6VBFbNw1UbQvKoWIaJHsuQuzBE9JVjDO8u99xFt8riv/gVfAtrykiHif3tnP6bn05qfAVFIrRuPuGti/EtrsNVE0R0KVgFncJAkuThCww8c4BCb/ySx5gt8pko+czlr/G2LINMfoiQt5Ge4SNoRgbDLNBUuZ6Oob3UhJYgCOLVK5EozpLozpcQXW5CazZhF1/ESriS0IatYFlFaj8gSuVah5ZJuu04A8//5D0nxVzMyLvuYNtog30Mvfocw3t24lu0FP+yVXjnt5ZpszpapV4qttyGu2Ee/U//8Ebu5RpE5lwbAy/+jOq7HnS8+6I4tH/pavxLVo1GVESxpFXqaNXaaNFBBp57YtqC3bZlOS/ekZesZRJ97TlSJ+aeV4EJyF+zhOhRWfC791K5bSlYFvm+OIX+BKJbQY0ECG1YgK+1lsS+9jIjCCAqElLATe3DG6i+f13xeUliKDLuhgpqHlhPYFUTZ//+abLnxhf3+xbXsfBz9+FdWIOZ1Sj0xbFNC7U2ROTuVYRuWsiFb7xC9LUT4ycuCFTdvpx5n7odNeIHG4xUDj2WRnQpuBvCuBsqSB3pHGdEbd1k6MUjpJuqkH0u/MsacM+rQpAvYuJPAG9LDQt//36UsK9k+MxsATnowbuwBl9rLYGVTZz50k/RBuemrvJewbR1euPH8aghBEHEMPP0xo+ytP5u6sOrEEWJgeRpctrcDfucYiNWPsfgC0+S7+0isv0+ZH9gVPhWFMuCI47xsygM9JA4+I5TYHwVeoGJ6uXrwnxVYVuYmRTJw++SOnEIpSJCcOV6AsvXoIyhrguiI6tVfc8H6P3Jd65a/7UbuAQsi+Th/ejRISJ3PYh3fsvod0US1FjYxdZN6bZjDL+zE30GCxtLczRFR/OCAoKsXHP3RHD1PCq2tmIksrT/43OkT/diGxaCKCB5VXyL63E3hMmen/jc5YCHug9uZPCFw/T9bB9mOg+igK+1jvm/fTeeBdU0fWIbZ/7uaazcqPeqVPiY/zt3422pIbbzJN0/eBs9mna6XQQ81D+6mep719D0G9vJdgyRay8fP7CqiebfuhMl5CN9spu+J/aSbut1vDhRxFUdILCqidib4wmEtmEy8Iwjqi9IIk2fvI36eVXjtpsIufZBhl45hjaYJHGwAyORwzYtREUivGkR837zDvxL6qm8dTl9P3nnasutTht9ieNO+V2xmrpn+Ai6mafCN4+cFqcvfrwYJp0bLkuCQA6ESrV+tlZw8hO27TDdTBMzk0aLDpLr7kAb7JsVjfpyQfYHp97oOoOt62gDvQwN9hHf70hqhTdtQ/L5S2FSb8sS/EtXkTz87tWe7g2Mg+009/U5+XXbssh3X8BIJ5xFmw1mIYc+HHNq6vq6nWL5GZYVmLkMViE3SkwTRacv5TUGNRJAVGWy5wZIHr7g5P+KMJI5Cn2Ty4gJokDm7ABd396FmRn1ThP72un61hss+uOHCW5YiHdh9SgDU4Cq7cvxLaknc3aAC994FW1o1GsyU3k6v/k6viX1+Fpridyxks7zr5UMiiBL1D2yCSXsI9PWy5kvP4U2UD5PPZpyQqlTwDYtbHMGdaqaQee/vQEX7WMCgy8fJbi2mao7V+JbXIsgS1e05vBy4mLtUss26E+coD8xgRc+B8zJCAqyTNXt91Gx6RYEScbMZRh88SmHzXglyAUXhRAEeebTVyM1l2s21x5sGyMxTHTXS+T7u6l76KPIPqf2TJRl/MtWkzyyf5xA8g1cXfgXr6D2gQ8hB4KONujBPQy9+txlZyEbyThGJl3q1iAIAq6aekTVddlCmZcD+a4YZk7Du7Caul/ZxNBLR9GiKbCmqdBk2yT2ncPMjj+n5OFOCv0J3A0VBFY3l4yg6FYJbWxBEEUS+9rLDOAIzHSe9PEu/IvrCK5tdorJi4QTV30Y3+I6bMOi/5kD4wzgFceljKZpkSvmP2Wf2yHMvIfTuhxQZR+q5EUUZSzbRDdzaHoa+zKdyZyMoLdlKeENWxEkGduyiO99k8Thd69M4attYZnlru9MiovBCYW6ausv56yuTdg2mTMnSR0/RHjj+0p5Qld1rfMQXB8LwV8KSL4AkTvfj+QPAJDv6WTwlZ9fkbIBK58j39OJq7ZhtDaxoRk5XDGnnoCXG6kTDgGl9oF1NH3iViJ3rSKxv53hN0+ROTvghDcng2lR6E9MGPazCjqF3mHcjRV4Gkel7SS3gmdeJQgQWr8AV83EESNvi7OIloMeRFUphVPVSAAl5MXM5MmenbmQ+FwhSCJKhQ/Pwmo8TVXIYa/DTlVlfItqnY1mpQBz9SCJCk2V62moWIPXVYkkyFi2RU6LM5A8xfnBPejm3J+TORnB4Mp1Jaq2pRVInz5+xZQfLMPEymYhPBond9XUO3mTabJL1Zq6st5tv9CwLLRo+cMoiBKI0o2SiWsI3oWtKJWR0j2ZOXviitbNpU8eIbjmpqLMmlM7GFq3mcGXnr5mVFts3aTrW2+QPt7lEFlWNFL7wHpq7ltLpq2PwecPEd11qiyfV7Z/sTTgUt+Zxf1E95iaSUlE8jp8AW9LDZ4F45vijsAs6Ni6iSiLJe1fya0gKBKWbmJMZaQvM+Swl/pHNlF1x0qUCi+2UQynFovfRfVKlsVcKQjMq9rIopptxLPd9CdOoJt5JFEl5G2guWojiuThZM8Lc2aIzuHqCCgVow8vglB6sK4EbL2AHo/hqm8a9Wxq61EjtWjT0B0UJInwhpun1HT8hYEgooTKRZzNQu6GSPE1BqVY4jICQVYZI4542ZHrOk++64IjeF18jkJrNpE9d9rRTL1GYOsmw2+fJnHgPN6WGipvWUp48yL8yxvwLakjsLaZjq+9hJmeIIwrCg6rcgIIAqXvxtYLjtQgYtv0P/kuyYMdk87Pqb8bNcIjRkcQBMQ5dF+YKQRVZv5n7qTq9hUYyRx9P3mX5KEOCv0JjHQeWzep/9AWGj5y83s2p8sBWXLREF5FX+IEp3peLAplOxAFmfmRzTRXbaRD3UOmMLcSjTkYQRszP7piFVUXkdt2EN31IoX+Hsx87rKuLG3DINd1Hv+yVSXhaTkQovLm2+l/7glsbZIaJUkivHEbgRVrrysvUA6G8C1aTrbjDHosykxejO76RgLL15SVpuS7LtzIB15jMAs5J9dVfGeH1mxEiw2SbT/tdJa/zMIMVj7H8O7XnD6Vquq8tD1eau5/hMEXpBlFcwRVxdM4H8nnJ3X80BXxJK287kiIneyh9z/eIXLXSho+ejNV21eQPHyBoReOjJ+XJJaK5sd9J0solU4aRY+N5lxt3USLppGDHoxknvjeczOap5HOY+Y1RI+KWh0k3/Pe6Pn6WmsJb16EbVhc+P9eZejVY+Nyp9fTO28EAiKK7GUodabMAIJDkBlKnaU5sglJnLwd3XQwJz85c/oEvoVLRrtML1hEQ30jZiaNbeij5RIl2FiahpnNoA32kevuIN99Ydps0XTbcSpuvh1lTGI/uGoDiCLxPTspDPWXFQ2Lbg+u6lpC67cSWLHWIQDoGoIkl9RWrmVIvgDV9z5cyuVkzrVR6O3CSCeca3xxJwFFRQmG8bYspmLLbchjPEGrkCd14tDshQlEEUGWEWUFeYJcrOT1IXl92IbhKOpMs4P0tMaVZARZRvb6S5Jxpa89HiSvH9vUnXGvM083d6EdMztKVpGDIWrf/yhmOoWlFSZsjWQbBlYhhxaLku/tJNfZ7nQ1meZvmzl7iuG9u6i8+XYoPrtqRYS6hz9K5vRxkscOoEUHMXNZp6Si+BuILheSx4ccrsDb3IJn3kKUiiqyHWdJnzhSorJfEVg2eixN3xN78S2uo+q25fha6xh68ci4taEgCASWNzLwzAFso3xOanXQyf1ZNum2UUkvM1sgfbIHb0sNwQ0L6H9m/8Re5iVQ6I2jDSTxLKimYmsrqaOdM2J4zhZqdRDRraIPZ0if6hlvAFW5lMe8nmDbJnktgSJNHLlTJDe6mcMw5x56npMRTB7dj2feQgLLVzsvqmKhrzSdkOOy1ViGgTbYx/A7O0kdO1imxD8R9HiUxL63qLr13lIISZBlgqtvwrdoGXpsCCOdxDZ0RJcHOeCo5o+EQM1CntibrxBavwX1YgHgaxSC6IQ1lVAF/mWrsfI5zEwaI5vGKuRLNV6i6kLy+pEDQadkZYyRt02T5KG9ZC9Mb3XrW7IS74JFSC4PosuN6HI7kluyjCDJSBMIJVff9SDmLTlsw8A2DWxDx9IKWHlnjtpwlPjeXZO+qD3NLfiXriqNKbncCMrouKLLVR7OFgQqt95OaM1GLNPANkzHGBYKpWujx2PE9789eaTgKkIb6ie66yWqtu8oLS5EWUGchp6nbxHYtoWRiJM6cYjh3W9Mqt07Ats0iL35MpLbTWjdlhLLWvJ4Ca7ZiH/FOsx00ullaRoIgugsdFUXksfr3A9XyrsQBDzNVVgFA20oNU7iTPK7UMK+olrOpXOnoQ0LCW1sIf7O2ZJhkAJu6h/djORxke8ZJjVGoNo2LIZeOkrl+5YQWNFE/WNb6PvJu+VjCI6SjasmiDaULvtOH84Q3XmSpuYIkXtWk+uKEn39RLkhlQSUkBdsZ/vLASvryMBJXhW1KkC+a7QnpaBIVN/lKNdcD96gJKoIjEhBQk/8KHXhlWQKQ2QKMWzbRBAk3EqA5sgm+hMnyWlzZ+HOrUQCgfSpo3ia5iMXxbRnAlGWcdU1UvvAY7gitQy9/vzkhtCyGH5nF2pVDYEV60YNoSAg+/wlHdOyOY4obOgasV0vkdi/G+/CVtSKSmardfieYYIVruTxInm8KHb1uM0FZ6PR3W0b29BJHn6Xodefn7aX5GtdTvim8hzCxce+eF4jhnpk4hfbOm2wn/i7b05uBOctpGLr9gmPf6lxHaM/yuS7OPqgxwZJHT2AcY0aQUGWyXddIN99AV/r8hk/Q4IgIocqqNh6O+6GZvqe/MG0upxYhTwDLz2FkUoS3rwNyesvjS3KMmK4kulk+B0RjMuXvxQkgboPbCS4fgHZcwPkOgbRYmmwQa3yE1w7H/+yBrSBBMN7zkzMANUM9HiWhb93H/F3zpI9N4CoygTXLyC0thmroNP3xDvj1GbSp3ro/v5bNH3iVhoe20Jow0Iyp3rRE1lERUKNBHA1VOCqCXLmK0+ROlyu1DPwzEH8SxsIb2xh/mfvJnLHSjJnBzBSOSS3gqs2hKuxkuFdp+j+3ptjTtrRDRXdCqJLQfKquOpGIgMeAiubMJI5zJyGrZvow5nS4iB9uo98zzDelhqaP3MHg88fpjCYRAl5nfNdN59MWy+BNc0TXm/J60IOuBFUGcmj4ltchyCJCLKEf2kDoiIXxzUwkvkJy04uF5Y33odXHX2HAPjdNWxY8FFyWhzT0hFFGY8SQhRlDLOAKvsoGHNTwZldU11Zxmx9OlgAACAASURBVL9sDRVbbsVd1+gwDnHaw9jmSCuOi+/O4sMtFleVRc/RaQ0kE968jcJAL8kj+yYd28rn6H/2CfR4jND6rY7hKx5nIm1E2zTRhoeI7nyJ9PGD2KaJHotiL1g8lRrRVYeRSpA6cQTfoqWO9zVGfm6yl+WI8dOiAwzv2Unq+MEp2/NcjLmtHIVZX9u5rljH73+N/siCgLuuicr33YG3dVlJycgyDCcPaFtc8hkqNuQVZCesP3LOnuYWIrffT9/TP5yWlqitaUR3vUS24ywVW7fjXdBabPY8/lkq26/4XFmFHLmOc8T378a+TOFv27LJ98Wp8KqEN7VQsaW1dNojItSZ0310fWcn2TMTd5+xDZOex98muG4+lduWErlrVUlQWotn6fuPPQy+eGT8gsyy6X96P/pwmvrHtuBpjuBrqXFKC2xnbrZukG0fnNCTMxJZ2v/hWeof21IqvPcvbywx2EfEvy9mj0oelYV/cD+htfMRJLHI3nFO2re4jmV//WFnfNtGi6Y4/dc/KZ27Ec9y4RuvOEo4zRHm//bdzjUwLYx0nr6f7CWx/zzLvvyrE16r6vvX0vhrtzii2yO/uSggqTIL//OOkQuPpZt0f3cXvT9+Z8rfcLYwzDyaUe7dx9LjCUoj3p8suS6LgLYwPm835ktBGPelICtEtu8gvOmWUnmEmU6SPHrAIXDEY9iaNm5FLiA4OSWPpySoHVyxtiQGDJDtbKfrO/97Wg+wI0NUh3/pKtxN81ErIkheH4IkYRsGRiaFNthP9vwZ0qeOYqSSjLxUPPNb8bUsLt6cEH93V/H78ZB8AcI3bR1lvto2qeOHKPRPrfwwFq7aegIr15f+ti2L4d2vT6mdKkgySrgSz/wWXLWNqJVVqKEQnqAPAwVLkJyb1NCxcln0xDCFgT5yHWfJdpydlTarf+kq3I0TrxxnCzOTYvidKcKh8xfhW7T08o6bzZDYv/uSxeDehYvxLmgtvXTMTIbEwT0zkxITRYKrNpQJMWjRQZKH9l5yF/+SldTc/yvIwTDg5PkyZ06QPnWUwmA/Vj43oWERRAnR5UIOhHDXzyO4ekNZiYWlFej8ztfId03Obhx3XFnGVduAb9Ey3PVNKBVVSL4AoqJgWxa2rmHmcujJYfRYlHzPBXJd5zEScWxz+uQdJVxJaP2W0aiCDfF9b2EkRzUgBVlyis8X1aBGgkgeBdsGI5Und2GITFtvmRLMCNRIgOVf/lXU6gBnvvwk8X3n8C+ux7ekHsmrosfSJA93ku8dntx7FUDyufEtrsO7oBrJ78I2LPThDLnzg+QuDDnM0EtpaEsianUA39IGXLVBJLdaCu9mzvaT746VqbYIikTk7lW4akNlx5FCAXxbbyJ//DRapxO6tQoGQy8eKS/mF0CpdLxkd30YBAE9liZ1rItcZxTJrVB931qMVJ50Zwa5oZ7s7v1Y2RzBtc0E1y2Y1loxeaCD5KGZ3VczgSBIM16yWtMserZt+5KHnrERDG3YSs2ODyIWDaA21E/vkz8g39M5I3aYUPT+qu98f6m9i5FJ0fXdb1Do65r2ccAhhIiKUvKUbNtyVO11fco843WFIjnlju1+/udXqvj9P42z8y0NJ/zoaLNahuEsIq67zhy/PFAjNTR+9DdRKqoQBAFL1xh6+efED+ye3gJwBIKAu6GZhg/9BnIgWAr9D736c2K7Xp71/Jw8rFIivDmBHRvbMp2cb8lTvbZQMoI1Qc585UmG32y72lOaE5TmRmr/5HcYfvwpMm/smfsBBQg+cDeB22+m/+/+F8bA5e/+cK1iMiM4o3Co5PURvul9Ja/Itkyib74y41UnOMn51NH9VN58B3JRLUN0uZEDQQozFK+wdQ3zvWiEerVhWdiahmJnqfZ7kLUURuraEj++gakRXLupZABt2yZz+sTMDSCAbZfYoYEVa0sfq1VzYwPauj7j8PkN3MD1ihnVCSjhStTqulLoxUgmyXW2z3pwq1Aoa6grFPOFN3ADv6gQFAXv/EWjH1gWmbMnZ24Ax+x/ca9I8QqKVtzADfyiYUaeoBwMl1HvzXx2bm1YJKmstdFICPMGyhEKClRWSNi2TTRmTZiL8HoFqqtEenpNBBGqIxJul0ChYDMYNSmMSaGIIkSqRAJ+kbHlkrYNg0MmuZxNY4NEMlUcj/J9G+slLBu6e66vmrzpQpY9LJp3J6oaoLN3D/Hk7Bd6F0NyexDd3jEiBpbTEWIOEF3lJSvmNdYa6XqAa1kr3o1rSDz5Iu7lrbiXtSLIElpHF5m9h7AS5QxEqSKEd8NqlPmNTs/CcxfIHjiKlSxnnAqqinvlEtwrFiN63BgDUbL7DqP39JelLARVwb1yKZ7Vy0ASKbSdwxgYmvBZHzf22Q6yB45hpcrHlkIBvJvXoc5vxMrkyO4/es3yxK4mZsYOvahQ2anfmr3n5mlaUFbWYOXz06pz+mWBIMDWTS6++IdBli1RKGg2h4/ovLl7/Evuli0u/vYvw/zeHw3z8AMe7rnDTUVYJJOx+YM/HealV519XCp86uN+PvYRH1UVIh63QCAgYtnQ22fyF38T583dBb719QinTht89veiZQL1jfUS//Hdana9XeCP/mz4eqtNnxZEQaYyvAiPu4rB2MnLfPRy5qyAgKiOr7ucLhyCTFPZZ9rQxKzJG7g05Kowvi3rkHxe5NoIWmcvos9L6AM7cC9fzNDXv4ddXEkqDbVU/saHEL1etPYLIEkEH7wLz7oVxP79x5hxxzMXXCoVH3oQz7oVFNo7sTJZPOtW4Lt5A7HvPEH+WDFnKUkE791O4N5b0Tp7MQaj+LdtxsoXxpUlKY11VH7yQ4heD9q5DpBlgg/djWfdSmLfGh1bDPip/NRHUJsbKLS1I/oEKj78kFMvexn5Ah6PgNcjIIkTM8LjSYtCoXw8VQGvV0SRnX0Mwyabs8kXJp+XooBv7H6mTaHg7DuXU5qRETSS8bILqIQqcdU2YCRnbrjkcCWVt9xVpjeqxYbQYkMzPtYvKpoaJf6fv63ANG2+8OfD9PZb3LLVxWc+FRhPnhegskLki38YpLPL5L9/JYGm2SxpVWg7Pepdb9/m5k//MMTXv5niO49nCPhF/urPwixplXn044Oc7zAwTXjl9TwfesTL/Pky59pH2X+bN6rU1Uo8+0LuF9IAXmmYhZwTPRnp8C5J+FqWkDl9bMa6roKsEN54S1mKwtY0ctMURfhFg5kt0PfkPiSfi9yFmZM+BEVBDPgY/Oq/YSaSCJJE6MG78d9+M+qCJgqnzoIsEXrobgRVZfCfvokxFAUE3EtbqPr0r+K/dQuJp14EwHvTGjw3rWb4B0+S3XcYbBspGCDyW79G6MG70c53YWWyKHXV+O+6hez+Yww//iS2piF6PFR+8jGEsZ0fRsZWZAb/6V8xhmLO2MsWUfXpj+Lbtpnk0y+BAL4t63Etmk/sO0+QffcwAGpTPZH/9OtTdqqfDiQJ7tnu5TMfD7F+lYtgUESWRg2hZdmYJnzqc/089YKT8nK7BO6708uD9/i4aa2bhjoZUYChmMm+wwW+/u0EO3fnJjRoq5apfPbXQ9y61UN9rYwkwnDCou2sxss7s/zr95PEE7Mja83ICOrxGFpssNSJQVQUIrffj5FOUejrntYKQ5AVvAsXU3XrPbgbm0vkAIqqJr9QbM454q7tbuY1Sfz252I8/ZxT6rDn3QLN8yQefXi8NqLfJzI4ZPFf/usw2azzWzz7Yr7sZ7nlZheFgs2/fSdDd68JmHzn8TT/+OVKGuslzp5zDN5Tz+X49Y/5uONWN+3n09i2c+M/8qCX9g6D3e9eO/3nrifYmkauswNX/TwEihJfq9aT7+siefjd6WmFCgJqpJaKLbcRXH1T6aVm2zaZc6coDP5yeoJmVqP/ycnrjCeDbVpk9hzAjMVLf+eOteG/8xbkyjAFQK6uQl20gMyudzAGo6Wa6MKZ8xjRYdyrl5J45mUQBTzrV2IMRB2PrxhOMRMpckdPEXz/nUiVYaxMFteSFgRJIrtnP3bOidhY6QzZvYfwrFhSmp9SE0FtmU965x6Mwdjo2KfbMaLDeFYvJfnzl0GUcC9vxRgYIn/8dEkkQ+vupXDqLO4xx5wNBAEe3uHna39fQ6Fg88QzaXr6DVYsVfngfX58XoEf/DTD869m2HtwNGoVqZL40hcjNNbLHG/TeO3NLJYFyxarfOghP9s2u/nIb/WxZ395pGv5YpXvfa2OlvkKB44UOHYq66Rl6mS2bHBTVyPxze9PXOI2HczICJrZNImD71B95wPOGxFw1TXS+JFPk2k7SuZcG0YyjlUoODVOQrE2UHUj+wO46hrxLVyCWlOHOKY+ECB16oijbXkVICgqalUNss+PIEpYhoaZSaMNR2dPWLgM2LBOJZGwOHBodA6mCW/vKUxoBAGefi5XMoAwfl2iFWxE0QktjMDjFrEBXR/d+OQpnUNHdD7wgIfv/jBNPg+LW2U2rFf5l3/PkErdKMGYLRKH9xJYsdZhRQsCostNzb0fxL90FemTR9CGBjDz2aIqiO1IxqkqkseHWlWNZ0ErnsZmJH+gpKVq2zZ6PEZ050tX9Z69rmGa48oG7ELxWhY7Q0gBP6LPg/+2rXg3rS3L2UlVYczhBIIig22j1ESQqiqo/ZP/VPYgij4PgiIjeh0JQLmqAiyr6NmNwhiKlTWOFQN+RJ+XwPab8W1eN37sWBxBURyPszKMGU9g5cYYFNPCiMWZKwJ+kT/4bBi/T+C3/6ifnz2XwbIcT6+z2+CPfreCVNrie0+kyqrmevoMvvDXQ3T3Gpzv1ElnnNKuSJXEV/5bhF99JMDHHwuMM4IfuM/HiiUq//D1OF/6hxipjIUggM8jUlcjEw6JDM/SC4SZ5gRtm8SBPbhqGwiuWu+oVggCSjBEeOP7CK3f6mg2jqlTEyTJqeNTXROKVtumQfrUUQZffGpuJJtZwt3QTPUd9+OqbXRUWQQR2zQw8zn6nnqczNnLnROaPkJBEV2HVLr8B76U22/b9pQhgedezvOxD/v4sz8O8YP/yBIMiHz2U3727tc4emLUC09nbH72TJY//0KIVctV3j2gcedtbkRR4IVXZl6AfwOjKPR1M/T6c1Tf/ZCjjyoICKqKf/EKfK3LnWdI00ZFyEfEy1V3qXZvLGzbRhvsZ+C5Jyj0d08w4g1MC0UlnIs+nHDT/PE2Cu2d41aZVi6PbZiO8guOIUu/sWfCGmpjYKg0rsNYmSpM6Xh+uWNtaOenGLt0zGmdzowQCoqsXq7S0WWwe1++dGr5gs2zr2T4z58Os2KJSsAvkkiOnrdlwZPPj1fa6ekz+d4TKT5wn59Vy11l3wkCBIOOKlJs2CKRtEocBU2zGE7MfcE3Y9k0K59j4PmfYqQShNZuRvKNaA4KCJLkdBNgvIbnuOMYBvrwEPF9b5M8tPeqGEDJ66f2/Y/irmvCSAyTPHsSK59DdHuQ/UG02OB7PqexSCYtZAX8fpFkavTh9PsvXdkymfgBwMHDGl/9eorf/90gy5cppNM2L72W51+/nR7n3b3yep7P/59B3r/Dw4lTOg+/38uBgxon20aNZVV4MU31m+kbPEz/0BEUxUckvJhwaAGK7MUw8qQzffRHj1GYUOxWwOupojLcSsBXjyy50I0s8eQFYomzaNqldAEFXGqAgL+BUGAeHlcFoiChm3ky2X5i8bNkcoPYlyzqFvB5IkQql+H3Ojm1TLafgdhJDCM34XWcV7+VynAryVQX57t3Yl9CrUJVfLTOvxdZ9nCh503iyYvqaG2bxMF3sAp5qm69BzVS4zQ8hhmJ0NuWhZlJkT51lNju151OEjdwRWEmUlipDEYiRfq1ty+ZArIF0HsHkCOVZPceGsfcHAtjKAaSiFwTcUKsRciRSoQxhsxMpLDSWcxEcoqxBYzoMHJVBaLXg5UsPkOSiBypmHCfmUBVBCRJQNftcbZd1x3vTpIu3cheFCEcFPH7xOKxoDIsYZg2Hpfz98haxLbh9bdyfPbXTT73mTChoMj3f5Li9DmNTPbyRKNmpR1q5bIMvfJz0iePEFixDl/rMmR/sKhlKDGOd29ZJbUJM5cl391B5uwpMufaMDNzEz+dC7zNLbgitZjZND0/+145oWAGHeuvFPYf0njkYS8b1qr09DrelyQ5jNHZIhAQ+NXHfPzvf03x1a+l0I1Ln2ZPn8lru/Lcc4ebt3YXaG2R+cJfxBlbxeJ2h4lULCWXj5PO9LF00UNUBBeUjI8giNi2RV5LMBAtN4KCINFYexMLmm7DpQaxLAMbG1GQaKzdSCrTy+nzzzOcGE/0iFQuZfH8HXg9VdgU1UxsG1GUEAQJ3chw7sIrdPXtZSINztrIKhYv2IFLDWHbJpZlUl21nMa6zXR0v4lljc/N5fLDVIUXE/Q30R89SjY3sdGpCC2ktnoNmpamoF3i5WdZpI4dJNd5Hv+y1QSWrUGtrkFUVIdxLYqMruQdkWrbMp0yIk2jMNhHtr2NzJmTFAb7rvq9Om0IAnLQU0qnAGBbGMkczLL1kOCSkXxubM3AvMJd3Y3BKPnjbfg2raXQdg7tbAe2ZSFIImIoCLrhGDLDJLv3IBUf/xUCt99M6vXdDjNTEBDdLqRQEO1CN1gW+VPnsAsF/LduQu/uxcrmEf1evJvXl1mS0tib11Foa0c7Nzq2FApi67qTKzQM8kdPEX7sAbwbVpHZvR9sG7W5EdeSljlfg+G4yfkLOs1NCq0LFQYGTWyciPH7Nnpwu0TOtOvjIliiCGtXuvjEhwJsWuemoVbG7RZQZAGXS8DnLeoiXzTeK7uyfPGvo/zh71bwuc+E+fSvBXnrnTw/eirFsy9nGYrNjaE3+y4Stk2++wL5nk6irz+PUhlBCVc5eTXV5bjkVlHTspDHzKTRk3GMeAzL0K9IA86ZQqmMIMgKWl83+d6LpNqugZfKy6/l+a1PGXzhvwTxeAR6+0w236Ryy1bXrC+fLDklEffd5SEUEDFM0DSbM+cMXn4tRzwxet66Dj99OsfD7/fy6x/zEY1Z7N47MSEm4KtjScsDuNUQHd1vkskOYNsWHncFXk+EVHq81mp9zXoWL7gPyzZL9XiGUcDlClJXvZbKUAsrWj/I4ZPfJ5XpLdu3UEgiCCKDsZPEE+fJ5qNYloHbFaahdgOhQDML591BPHmBdLZcgijob2TJwvtRFT/x5Hl6+vdT0JKoqp+aqlW0NN8x4TkmUp2kMj0E/Y3UVK3kfNfr47YRBJGaqpWIgsxwop1cfnKWopGME39nJ4l9byMHQqhV1ciBEKLbXZQTLIpVawXMbAYjlUSPRx1t0euQniuHvCz4sw/jbo4UhcAFR3j6rx4nf35gVscMv285jb+9g9S+s3T83U8u84wvgmWReOolRJ+Xqv/jI07eLq8h+jxIoSDxHz9d8uayB48j11QRuGsb3k1rMRMpp1tDRQjtTAfRbz6ObVkYA0Mkn32N4IN3Uf17n8aIDSOHAmjdfeNyeoknX3TG/vRFY4eDxH/4tGMEgczeQ7iXtRL64A68G9dgazpSwE/hdDuuxQvndAniSYt//maCv/vzCF/92xq++f0kvQMGK5aofObjIQaGDL7+7QRju2AJAjx4j49//nINwYDIG2/nePblBD39JumMRetChS9+buL2YboO3/hOghdez/DYQwEefcDPHds83LPdy/4jef7r30Z5/c3crCO9c2qlBDjizVqBQl+3wxC9XiAIiMXO2teqxmhnt8nn/3SYL3w+xJf+IkxBs9l3QOMr/zPBn38hXGanbZtJvTpwbsTmeTLdPQa1NRLv2+LCBrwegcYGiTfe9PC7n4+RzoweZN8BjTNnde65w823f5Chp3fiF284OJ9kuodDJ79LJjvIWO9LFOVxnpXHXcmCxm2AwJnzL9AzsL8svBiNn2HN0o8QCsxnXv1WTp59skwsN5XpZd/Rf0E3suOOPZxsZ8PKT+F2hQkF511kBAXm1W9FVQKk0t0cbfsRBW2UWTY03MaK1keorlw+LpSqG1kGho4VjeAKuvv2ol+keu92VRAOLsCyDfqj47ueXwq2aaDHo+jxX2w9RzNTYOBHb6LWhpAr/ETuvwlBledG25dEBFVBkGdfs6z3DpDeuadUZ1eabzJN+vXdTnH7yGfDCaLf/CGu1gW4WpoR3G6sTBato5vC6TFRC9Mk+fwb5I624V7eihQOOi2J+gbJnzyDrRfvW8si9drbaJ3duJcvBlEk/fputPZOjP4h9O6+8rH/9XFcrQtxtcwbM3YXhdOjog52Nkf0Wz/Gs2Y56rx6rEyO3OETTr/GvkGs7Ow9ZsuCf388SWO9zB/+Tpi/+pMqDNMmnbZ450Cef/xGfBy5JeAX+W+fr6S2WuKLXxri//23RFl9353bPJOWXFk2tF8w+L//1zBf/3aCm29y89lPhrjvDsewPvyJHs6en907fO5G8DqC6PaghCqcnIvHi6u2AQDJ5yewfE1Z1EyLRylc7B0KAr5Fy8C2yHacwzZ0BEXFVVOH7AuC4HQt0GKDmJlL5QAE5GAItTKC6HJjGwZ6Ytipj7yoa4Btw9vvaHziM0NUhEVsG2LDFgXNZu8+jaExai5v7i5w67199A1c2kWc1yjx1f9RySuv5/nnb8TI5x3umSzBxz7k4w8/F2TpEoV9B0aTzYmkxcc+PYTPKxIdNi/pgdq2RUf3TjLZ8av5iUKLlaFFeNyVpDN99A0eGpdf07QUA9HjhALNVIRaUBRfmbEC+6K/R5EvxEllevG4K3Cr5cr8LjVAOOh0yOgZODDuGIaRo6f/Xaorl0147IHoceY1vA+/t5ZwsHlcMX2kYgmK4iWV7iWZuo4Whe8RbN0guccpEhe9LsK3rkB0q1d5VqCdu4B27sK4z81YnPiPnh73uZ0vkD96ivzRU5Mf2LLQO3vQO6foOmOaFNraKbSVqxMln331EmOfJH90ctKencuT3XOA7J4DZZ/rXb2X2GP6aKiTuWe7l9ffzvHFv4mSTDvvpeG4OWGuriYi0bJAYTBq8rPnMuO2aaqX8XimXghZtvNOeu7VLG/vy/P41+u54xYPG9e6bhjB6SC0+iaqtt2DqCgIilpi2blrG2h89JOl7WzbJnFgN33P/Khsf0GWqb3vVxAEgQvf/RqSx0f17ffhrmsqdTy3tAK5C+fo/o9vjfMu5UCIis23Eli2BtkfQJAVbMvEzGbJXThL9K1XJmzRlEzZZcQYcLzEscjmbNo7Jg+PLVuq0NQg8fMXcvT2lW975pyOIDiKMmNh29A/YAGTx1/zhQTJCUKel0I4OB9BENGMHMFAExPR1iTJhW3buNQgsuy5hNETUBUfiuxFlBREQUQUZCTRORFRLL/FPe5KZNmDYRbGhVhHkMkNoRs5ZGm8kkteSzAUO0Fj3WbqqtcSHT6DZTtGXhJVaqpWICAyGDsxzku8gRv4RcF9d3pZt9LFX/x9lONtGgVt8mCkrttYJiiKgNtVbuzqaiQ++ZEgijyxEayrkRiKmVxcQpvL22SzFsUy81njl8oI5rovEH3zJecPQcDfuhxfy1IK0QHi777F2BdxYeDSrSxEtwdvcwuVN9+B6HKTH+jFzKQQ3V7Uyir0RHxCA1j34IfxLVqKmcmQaT+NHo8heTx4GucTWLEOd10TvU8/PidR8skQG7awgZs3q7Sd0cnlbBTFCYX+xsf99PaZnG2ffm+4sTDNAqY5/RCL2+V4aFXhRVSFF026rW1byFK5dRZFhcrwIuoia/B5a1AVH5KoOGU7CKX6uYshS25EQUY3chiXmK9lGRhGfkIjaNsmfUOHqateS0WoBa+ninTWCZX5fXUE/A3oRpbB2Ikpr8HlglIdRPK4KPQNY2vF308ScdWGERQJPZoqI4yotSEEVaHQEysjowguBSXsQ3ApRWHuPEYiO2GMXakOIvncaIMJrAl6+8lVAeSAB304jZm4TIsBAeSgDyngBkFw5hfPMFI6cAPvHTo6dZJpiz/4bJh7b/dSKDihTU23udCt88wLWd7YnSsZx8GoyTsH89y73ct/+3wVX/2XOImUyfLFLj7z8SCN9TKZ7PiFtijA//i/IrhUkV17crSd1chkLaoqJXbc4ePu27y0ndV499Dsw7u/VEYw33OBfE8x5CEISB4fvpalGIk4w3t3TpsMI6ouItvvJ9/byeCrz6INjbLz5ECIcfwmQaDyfXfiW7QMbWiAvqcfJ9fdMbpPMEzN3Q8RWLGOmnsepusH/4KZvTSlerY4dlznW99L85uf9HPfPR5SaRu3S6CxXiKZsvjiX8YZGJwd48Ye6Wk4TYwYqXSmj0R68v6Rtm2j6aP1RaKosKj5LprqtjjHyPbRP3SEXH4Yw8xj2xZNdVtKYc/ycYXiz2NP8nvbk5RWQDLdQzx1garwYmqqVhaNoEBN1UokUSE6fJpcPnbJ/S83qh/ZSuXdazn/pR+TPugsoNTq/5+9946T7LruO78vVc5d1TlMT06YGWSQAAgwRxCiJFKkSSWHXSXLXu9+1l7Zkv3RR7Ikh6XXa1n2il6JliwSK1EkQVIECFIAQRBpBpiAyaGnc6ycX71w94/q6Zmequruqa4OM9Pfz/Rnuiu8d1/Vfffce+45vxNg+29/Di0aYOrLLzL39dcBkJ0aff/oKRydIS7++peqxlGS8O7rJfr0Q3j29KAGPAjLojKVJv3yGRLPH8fKLc4N7f67HyD42D5G/83XSf/obE2bOj79KG0fvY+pP3uRub96bdXXKDlUIu8/ROTD9+LsaUOSQJ9Ok/rbU9VruOmrVFQnirZ8mslSWKaOZaxvTqwkq2hOL6tRuha2iaG3fvy4htNZDWhKJC062hUO7Lk+QVVkCa/Hwy9+Nshv//sEX/yvaWy76qn6l3+QoC2s8NH3e3j6I14sC8q6zdETOj/7q9P8l3/bUXstgK4LPvykm499wLOgG2pZ57/0cwAAIABJREFUUCzbnDqn889/L8HwWHOTd7jLjGDLkCRsvczM81/HvKkCQD0BcEckhn/PQYRlknj1B5TGhxe/J5tm7qXncPduw9XZi2/3ATInWlBE8yZKZcFv/0GGb36nxIF9Gj6fTLlsMzJm8faJCqn5leJ6cM2oZfOTnL/y7AoM6PXnw4FBejsfRpIkLo18j8npY1i2sfAaCZlYZH/do1hWVc1IllUUpf5elCQpDZ8DsG2D6bmThIODxCL7GJ9+A5CIhnchhGAmfrruPuhaURqaqVZj721bMIJaLIAa8SOEwLWtHUlTEIaFEvCgtfnRx5PY5aq3wn/fdnp//eMoHieF06OUR+aQXQ58hwbo+PwTuAZijP+X5xav+CRqkvYXsZD73YKyBbJE20fvo+vn3ottmGTfvIgxm8HRGSL2E49gzGVqTtM+8CD9Bz+6dBuXYXroNYZPfWuVjb81/JE+9jz8cyha82lQ2fhVzr/2ZWzrpkRyRcZ3aIDCmbHrHoN5nD0RbMPCmF1aB1pR4Jd+Pshv/a8RvvV8gf/2PzJMz1kLUrget8zD97n43f+jjX/whSBf+3Z+wUAdO6nz1BcmefwRN71dKnpFcGXY4I23S+Tygt/5YgKPW16UKSME/MPfmOOPvpxhzw4HkZCCqkK+YHNl2ODYSZ1sbnWZBltGsEkKV87XGMBGODu6UX0BzFyG0kh9cWMzk6I0Pox//xE823aROXWsJlCmFRgGHDte4djxjZXWyubHiUX24fd1IcsalrVyLdJQYABZVheCaix78bVIkrzgbr2Zsp7BtHQcDh9uV4RsvjZ4xaF5cWhLCz5cW+15PTGC/j4A3K42SuUE6ezwiq+lFZSHZ0ACV09b1X9kCzw7u7EKZfTxBO5t7chODcuwUINu1IifzOsXEbaNGvbR9fPvQ/G4mPqT75P8wSmEUZU81MI+en/tYwQf20/p6kx1NbkBXkdHZ5j2n3wEIQQTf/QcmVfPI0wbSZXxP7CT/n/8VG1kqCQhy2pdlaqV0sil7gy14+scJHHhKCzhMWjyrFWZPKX5mpDXhBduRnZp9Pz9DzH0L/8CI35DfrYEwcf2ITs0pv/spSWP3R5V+J9/Nkg2a/Mvfi/B+FTtZO/sRZ2/85N+Du5zEG1TFq3S5hIWf/2d+qvUr/9NrZoMVFeRx07oHDuxNnrFzfeQu5xbKVejhSJIioKllzAalIqqhshXXWhaIIisbXzE3FqSSF2iYuTxuKN0t9+H1LArSjXBLQsVE7DrujQjoZ143dG6RyvpKQrFOSRkOmOHFwJorh9bpiN6EElaOtzeMIvMxE8jSQqxtv1Ew3uQJIm55PlFrtv1wEjmMZN5HF1hZEf1s/Ls6aEykyZ/ahitI4QSqGrNOjrDyJpCeWQWbIH/vu04+6IUz42T/P4phG5WQ/AsGyOeZfavXkVUDCIfOIzibb7k02oI3LcdNeyjcHqU7BuXqkZaCIRhkXvrCrmTw+vansiu++k48v6GxmazIgGSplCzbJ7fUlXDyyt9uV0SsahCOmuTL9SfAHjdMuGQTKkkKJU2/17t1kqwSexbECm+ZtCEZS+5urumuSpp2qpmsLcD+cI041NvMNj3JNv734fHHWUueRbDqAZRKIoTrztKOLidZGaIyZljC+/N5icQwsLraaez/QhzibPYtoGqugkHBtnW+zh2gxm6EDbj028QDPTRFtrJrsGPMDnzFoZZQlEcRMN76Yzdi2VXkKWlb4/ZxBl6Oh6kLbQL09IxLZ3ZxBnWe7lklyqUJxI4uyNITg3FoeLsiVC8MEHpyjSSLOEe7KAymcQ92IFVqlSDYhQZz94eJFUmf3qkxkUGoI/FqcxmcMSCuPpjFM6Oreu1Abh3dgFQvDSJXV5834mKSenyFMF37VmXtkiKirdz9aor64mkqbj6o8heZ/X3wXbU0LzBk0B2OfDfu538mdoUkZvJ5QXDowa7dzh4+iNevvlcgfS8PqjXIzHYr/FLPx9k93YHz79U4Oro5su/vpkVGUFJ1dB89d1LjbBNAzO/VSAXqjXkYF5MXFERVv39ItlRjXoTRqVaheMORiAYnXwVJIm+zofp7XyIno77MefdooriQJaqSfaZ3OKbM5m+zGziLO1tB9g18CH6Oh/Csk1U1YWmephLnKFQnGPHwAfqnjuRusjIxCsMdD9GT8eDdLQdxLTKKIoTSZIZm3yNgL+XSHDpqNVCcY5UZojO2GEcmo9E+hL5OnmSa41dNtDHE3j396EGPMhOFS3ipzQ0Q3l4FmFauHd2knn1HK5t7Vj5MpWpFJIsoUX8AFTm6udcWkUdq1BG6o6ghn3reVkASKqM4neDLTBT9VfY9STXCqlxpq++jub0ojm8KJobWVGrLtL5/2VFRZJV5BWs6BSHG8XpxhXpwhWMIYSNJ9qDbV0f5G2jgp5Znd6wXkozc/V1HO4gmsOL6nAjK9p8O29ss7pQwGDZtvucxH7qXbgGYmhhL72//BGEdUNVe1nCSOZJ//DMsseKJy2++F/T/Nt/FeX/+t12fuUXK8RTFrJUTYjv79GItim8eaLMb/1BguKdshL0dA3Q+/EvrPyoQlAYH2L8O3/WbLvuKIxkHNs0UFxu1GCortCxpGo4Im3V12dSt7TSbD0SsqJyo3bljTf7jZimTqmcQq9kF5V9WQmWXWF47CUSqYvEIvsI+vtwOPxISBSKs+SLM6QywzXaoaalc37oW6Rzo8TCe3E5g0iSRC4/yVziHLPJM3jd7XQUZxZWljdiC4vh8ZfJ5SfpiB3C52kHqrJoU3MnSaYvM9j7JG5nGOvm4IJFCKbjp+iIHgRgNn4a296Ama8QlIdnkQBnTxuypiCpMuXROcx5g+caiKGGfThiQSqzacxsCUmVrw+ijQKTrmUfzEucrZRrVRRWj3RdTaZRGy275qls4irZxPVUI0mSkVVHNWr0hp9g+056975/yRZo3iD9T/wMzmAMzRtccIPu+uSvLXpdYXaES8/+4ar2CfVCkpF3vnPDIxKK6kC+sd2aE7cvyuDhn5iX1lsaM1Vg9N9/E9dAjG3/9KeY/JPvz6eWVBGmTWUmXRMBXA8h4KvfyDE0YvDpT/p44IiLwT6tKuSRtvjeDwu89OMSz79YZC5xe0zkV2QEjWyK5IkfL3rM3d6Dp3uQwsQQldQcwrJQvX68fTsxC1lSp1YfFn2nUJ4ex8yk0EJteLfvIZ1K1NzQjkgUd+8g2BaFoYsbqq3q9kfp2/ch5PkIyUo5w/A738Y2aw3CTPwdZhOnAZZMK2iEQJDNT5DNT1T3BW8YZ4VonP9lmiXGp15nYurNhffc+Ppsfpw3T/5Rw6hTISziqQvEUxev7zHe8P6hsRe5Ov7SslGriqwBEuVKhkT60kovu+WUR2YRtsDV24bscWJmilSm0wjdoHR1Bu+Bfly9URS/i9yJoeqemi0wUtUgBa3BKk92O5DdDoRpYWZvmFBc+1jq2UUJFJ+rwZO3hrAs7KIOslRdEdZro9eJpCx9LiFsLKOMZSzOJ1tJFKZtVEgPnUSSFTRfmNjBx7BNg5m3X1jksTFKuTUIlBHVVA1T58bpVaWUubX7zbLRJxIUL01SujSFkWi+cIFtw+tvlXnzeBlZvnGqXH1uE8hC3xIrMoKVTIK5119Y+FsLthHYcZDJH3yN3NDZakcQgCzh8Ifp+cjnkB3Nh/jeaZjZDOkTbxJ78qO0veu9GJkUxavV6DwARzhK7P2fQPH6KI0Pk7+8fonW9fBFBmjrPbxgHEq5WWRJaaAZc2v5gUtRDXRp3XtWNkg0av/y1yUh0xk9hCTJxJMXGleMWAf0qTRWUcfRFUaL+DDmMhjJHNiC0vAsofccwNnbhuJxUhqaD+qybIqXJom8/xDefb3MPSvXuBUdHSEcsSBWrkR57LoH41p6heKrNUxKwIOjY/UlewAQUB6tuhhdA+1Imnpdc5Oqu9TV27Y67dFlsColEuerKUvuaC/RvQ9jV8rEz72OaOAh2YwI3WTyj7+H2aJKG7ejwatHU4Exvr6dCNuiMH5l8f6WLahkEpRmxgjuvZfcleV9zHcL6bdfwxGJETz0AN0/8Xn02SmMTBLF6cbZ2VOtXzg3zewLz2KXNlZuKxjbsar8qruFUGCAUHDbfKToKTZStUToBvpYHGdXGK0jROZHZ6tRnlCtzmALvPv7ELagMnU9tSf31hX0qRTeewYIPLBzQdcTqqvAto/dj+J3kXrp9CIXWnlsDiEEgQd2knrxHezSvJdAlgg+shvXtljLri331hXaf+pd+O8dxLO7m8INARzO/hj++3e27FxrgaJJhDscKKrEzEhtYv96YrZKvecOoikjKDtcSKoGDfJoZNWB6lo+3PZuwi6XmH3hm+gzEwQOPYizvQt37wDCsjBzGTLH3yD55g+pxNc/sOJGFM2FL9y3oW3YrDgcflTFgYSMxx1le9/70FQPEzNH6+Ybrie2YVIenSPygcNImkLxwvX26GNxrHIF/73bMTNFjPj1IBhjLsv0//ghvb/yUXp/9WOkD2+jdGUa2e0g8OAufIcGKJ6bYO6bbywYVYDc20NEn3oQ373b6f8nnyR79DII8OzrxX/fdvTxJK6B2jQVrT2I4nMhO1QUvxvZoSGrCp7d3cguDVExsXUDfSq1sCotj86RfOEE0aceou9/+SSpvz1FZSqFozNE8LH9WPnS9WjHTcb2wz6e/rVeend5yMQr/Ou/c4Ydh/1sP+zj+/99CmMZzc1WIqky0U8+TOnKFPkb0kokh0rH5x4n+8ZFiufvPtH3poygnphG84cI3/Mw6TNHscpFEDay5sTTPYh/+z7SZ48tf6CNRAgyJ96gePUiVrm0Isk0YZpMfv3PkVUV/RbyBK9h62VSR18he+Y4aiCMrM0LaM/XWtwMvgVPoAuHy7/RzdiU9HY+SFfsPiRJqgp2yyqpzBDD4z9saj+0pdiC8ugcklPDLpSrKRDzWEUdfSyO78ggpeGZhX3Aa1STzy3af/rdRN53CPnjD1Sr1mdLpF8+y+wzr9QoiZRH5pj84xfo+Nzj+I5sJ/DwboRpYcRzzH3jDaxMkb5//NSi90iaQvcvvh/fPQNImoKkqfN5a9DzSx9GGBbCsDDTea785l9gJqvtFIbFzDM/BgHBx/fT/ulHkaTqqib943NkXjvPtt/49Fp8qqsi3OngC785SHxC5+W/muWxn4whAYZu88jHoxx9LkF8fG0SwOshaSrhJw9QmU0vfsIWaCEvgQd2bhnBlVKYGCJz4SSxh95HaN/9GLk0CBvF5cEZaUdPzJB6p/WyX63GSCcXEtRXhBCUJ0ZWfV6rWMAqrm9C9UrxhftWrbl4p1LWM1SMPIrioFROkcxcXijIuxnIvnkZM/M1RMVEn77u8hSmxdSX/xbHc6FqKoR904TPFmRfv0jhnVFcA1EUvwdhWlTmMugTyfoV34Ug8+p5CmfHcPVVc9CEbqJPJqjMZlD8bkZ+/2uUR6/vIwrTJv7tY6RfqdUaXXRow6qpEG/lSkz+yQ9IfO8Ejo4QSGDMZiiPJ5BkibH/81nM3OZy9e26z0+lbPPl3xoi3OHgsU9VXcTJKR2XT8btXedk+/ngosrMYiMoLBsjXUANrX8KzGagKSMoTIOZl5+lNHUV//YDOEJRJFnGLOSIH32R9NljVcO4xW2FJCkEY4Nb+4ENmJx5i6mZtwFpPh1kc+VAmak82dcv1j4hoHR5mtLlxpVRAKxCmcLZpcXMa86ZLpBP107orGyJzGs31doTYtF+3i1jC/SxOPrY4hQjYUH26MZF5jbC5VEo5U3KhTqpAhvRdWyBVdBxxIKULl0vIyYpMlrIWzPxuFtoWjHGNiqkz75F5vwJkGVAAmHPR4pursFhi5WhOtx4Q70b3YxNzWY0flusI2I+G1aSkGRlyejQ2bEybV1Oevd45nMtq0Ey+x4Jks+Y5FLrG1kqKiaFs2NEP/EA5fF4VTBBkfEdGcR3ZJCJ//LcurZns7Bq2TQhbGRJQdh2QyWULW4PvOFeVMeWK3SLLRphGWVsQ0dxuHCF2inONV7ZXjmRY/hMgV/+4i6mh0p4gypf+K1Bdj8Q4Ln/Nklmbp2NoGUTf/ZN+n79E+z4nS9gJHPImoIa8pH+0ZlFwTJ3E00bQdnhJLTvfoJ7jqB6A2TOH2f29RdQXG582/ZSGL2EWdgceyVbrAQJf2RgIUF+iy22qMXIpynOjRHafpi+9/w0yYvHsPQiisOFVSmTvHg9INDQBV/9/WHe/ckY97wnxNj5Ik63wv/3b0Y48WJqQxxm+niC4d//Gv7Dgzj7ogjDpHB+guK5sYXcz7uNpoygpKh0PPZxgnvvxcimUNxeFM/8pqpt0/7IB0h6fCTe+mHLGipJMg5PCLcvhifYhdsXRXP6UdTqoG1bBoaeRy+mKGSmKOVm0YuphnJfa4WsaGguP053EJcvhtsXxeEOoDq8C1JkwjaxzAqGXkAvpSnn5igX4lRK2TUthrkUmtNLILq1H7j5kNBcPtz+dryBLtyBdjSnD0Wt6szapo5ZKVIuJCnl5yjl5qiUM5h6kbVy28qqA4crgNMdwu2P4fK2obkDqFpV5xIBtm1imeVqHy+mKOfnKBeSVEoZzMrmCmC5FYRtMXXsOWTNgbd9gJ5HPgkIbNMgM3JmkREEKGYtvv/n07z41RlUh4Sh22tRIe2WMJN5Ui++s7GN2EQ0ZQTdXf0Edh9m5kffIXP+OL0f/TsLz1mVMuXEDJ6uARIN3t+9+wn8kW2LHquUM4yd/V7NDSIrGv62AWL9DxCIbsPpDq9IHcLQcxTSE8THTpCavoBZWbtoTEVz4/ZFCXXsJhAdxOWL4XAHG9Yjq4dl6pTzcXLJERLjJ8mnJmqLYrYAWVZRNCeK5sLlbcMT6MIT7MTlbcMXrr8f6HAH2fXg57BXcffOXH2d9MyF5V94A4rmYvDw09UB/yb0YpLRM8+1dJLTMfgwoY69dZ8bO/cCxcxkU8eVFY3+/R/G6W1b9HghM8nE+R/UTa+QJBm3v532gQcIduzG7YutqHSPZZQpFxLkk6MkJs+QnbvcgvQNCdXhwRPoINSxG39kAJcvisMVuCWlFssoUcrNkY0PER8/RSk7va7Fh1uFnp5l+IX/jivSieryAQKrUqaSq400lxWIdDnp3u7G5VPIxg2mhkpkE8aGhU4ofjfu7R04YkHyp0eozKRRg17MXK0Q+SIkiZ5dT+CLDNR5UjA99BqZ2bUNUNKcPgbu+QSKWqtIZpk6I6e/g1G+NUm45oxgrAczlyJ7+TS2obNoxikEViGHI9Le8P2+cB+R7gOLVh2Gnmf26puLjKDLF6N37/to67lnwU230pWKwxVA6/ATat9NLjnK2LkXyMxdaZm2nyTJOD1hIj0HiXTtxxvqXSiE2cxqStVceEM9eEM9tA88SHrmAhMXXiSfGmdVM3pJxhPoxBvswhPowOWL4fKGcXoi87qJ19vaqN2K6iTcta/5NgDp2TpRi8sgyyrhzr1oztrQ7Xx64pYmGSvBE+ymredgzeNCVG/wZpEkhUBsR80kw+kJM3nhxRojpTq8dO18jI7Bh9Cc/nk7s7I+pTrc+By9eEM9BGI7eOelP8QylhdGrttuWcHljRLtPUS4ax+eQNeCIW6qjzs8+CL9+CL9dGx/hOTkaSYu/pBSdpbbLdjINisUZ5eOdJUVeN/nOvnQL3Th8ikISyCrEsmpCl/74iinXk6v+2U7+6L0/NKHcW/rQHY7GP3is5jpIr2/9jHmvvkmhXeWSAETAkMvEOneX3PvXZMYzMaH1rQCTqhjN9G+IzWVP4QQZGYv1WjDroTm9gQlCWHfGCUnLXpOdnmw9VtrjOrw4PCEKGQmAYlgbDvbDj+NJ9DZtItOkiSQFPxt29j90OcZPfscs1ffXPXM2OkJ073rPbT1HKoO0JLUEjfitWMoqoNI90F8kX5GT3+XubG3m464dbqD7Hn48zi9bQsdd8vluTnQXL5qFYAbVkMubxuDR36CUPsukORV9f18crSpQQHAE+ika+fjRLoPzAdLtbaPq5qbWP8D+NsGGT75LKnpjdXLXQu2HfTxwZ/v4qVnZnj7+0nKBYtQzMETn2nnU7/ex+i5AunZ9duukVSF9k+/G7tY4epvP0PX362WGhOmhZUr4zu0bWkjSHUyW84ncPmii/qDJEkEooO4fe0Us1NLHKF5ZEUj0n2w7uRXCIvExDtNeYaamkqXE9No/iDu9pvcZ5KEM9yOp2uAwsRQ/Tc3QJJkvMFuAALRQXbc/5lVGcDFx666cwYOfIxY/32rPp7q8BLtPYLDHUCSmx+olkKSJJzuINsOPUW09wirUeS/VjNNapGx3qI1yLK6aJXr8kXZ+cBnCXXsWXGtuEYI2yI9e4lmlxoOd4ho72E0pxdpFcZ4KSRJwuVtY/t9P0WwfXfLj7/R9OxyMzNS4oX/Ps301TLpWYPhMwW+/h/H8QQU/BFtXdsjOVS8e3uZ/atXKV6YQFSqBkPYNmamgLaCyvKVcpbkVH2xA9XhIdJzkFZUD6mH0xNuGLdQKedITZ9v6rhNrQSLE1cpTg7T/cFPk714Ei0YQXY4iT34XoJ77sUqFcheuvWNV2+oG5cvyuCRn8DpCS+62BsV/S2jjFkpYtsmkiShqE5Up2/JlU7VELrp3fchCpkpCunm5YGKmSky8Su09Ryqe66bqw9Ypo5llLAtAyEEsqygaG5Uh2dR++qhOb30H/gIxcwUxezSyc5LsdJKDyu5ni1agyQraE4/5XwczeVn+5GfxN82UPMdrOTzv/k9lXJuVX08Gx8in5kg0FZ/0Knp40YZ0yhV9/iEQFZUVM2Dol3fz210XzrdQQbu+RjnX52hUrpzCnEXsxZG2a75rGxbUMpbGPoGSO3V60uShOxxYhVXIOEmBPGxE3QMPoR6k7KUJMm09dzD9JUfr0nwU6hjD6qj1lALIUhPX6BSbi4boWnFmKkXv07s4Q8Q2H0I1eODSDuuaBfF6VFmX30OI3sLcmTz+MJ9DB75VM0KUAibciFBZuZSdTleSGIZOkJYVAvAamguH8HYDtq6D1bf3yCIwOkJ0bPnvVw6+lVEk5vyQljMXH2DcOe+hejU6uMCYZvoxRTF7Az55BiFzCSVchbbrMwHloiFAp8uT5hQxx5CnXtxedsaGsJr7tcrx//6lttsWwbZ+BCac2k9UEmW8bdtQ1FqZ6e2ZZBLjiyUfmqGSnFLQehmZFnB4fJXA2cOfIRg+46aiZ+h5yhlZ8mnJyjlZjHKWSzLQJZVVIcblzeKJ9iJ29+OyxNGVp1IkkQpN0s5X1u8eaXYVoXZq2/gD/cvKtwqhMC2KtU+npkmlxylmJnC0PNYlrHQPyVJQdGcuLxthDv3EurYMx8sVr+Pe4NddAw+wtjZ73G77Q824sKbWR75RJRHPhHl0ls5TEPg9sk88lSM0fNFhA2Rrur4Ucxa9ZVlWsi1ZPnY0w8x/dVXkDQFxePEd7Af38EBJr70vRUdp5SbITN7pSauA8DtixFs30Vi/GRL266oTiLd++s+Z5k6ycl3mo73aDpP0CzkmHrxGyTefhnVG0SSZaxyET01hzCb83M7PSGcntDC30IITKPEzNBrzFx9E73Y2LCW83Pk4leZGXqdzh2P0rXzMZT5AeFGJEki1L4bf6SfbPzWXLY3kkuOko1fJdSxG2Fb6MUU6dmLpKbOU8xOUSllWe5mLmVnSE2fx3X5x3TvfoL2gfsXgmtubnO4+wDuyz+imLk1f7uh57l87JllX6dqHg69/x+jeGvrwFVKaS6+/meYTQZYbFEfSVJwuIO0DzxIrO/eBU+GEAK9mGR25C2Sk6cp5WaXDTZQnV48vnaCHbuJdO0nNX1u1XvfqenzFLPT+MI9WKZBuZAgPX2e9MxFitnpFaXzFDNTJCdP4wl00rv3/UR67qkJaoDqKiLWdy8zV1+/Y1aDO+7107PTzeEnQ6TnDMyKwO1T8IdV4hM6O/7zngXP4d/88SQ//vrcmrZHmBazX3uN3l/9GIO/+RnUkBctGgBbkP7xeQqnVyZpZ1sG8bHjhDv3IN00XkmyQrT3MKmpsy2N3HYHOvCGeutOokq5WbKJ5jWdV6cYIwSVdIJKulEyxGoOLTDKWa6e/CaJydMrDgwx9Dzj576PWSkycPBji2ax11A0F5HuA+QSw00PFLapM3P1NUAQHztOevbSLYfmXqNciDN86lnMSpHu3e9BlmvbrGouwp17b9kIbrGJkSQi3Qfm0x+q37ltWyQnTzN29nlKuZUPiqZeIKtfJZu4yuSll1sSBW1WisxcfR29uIe50bfJxoeadnMVs9NceftrWJZB+8D9dYMbHO4Ageh24mPHV9v0TcH0UIln//PKXNLDZ9cnP1gfTzD8e1/Dd2gAZ1cY27AoXZ6ieH4cYa68z2RmL1HKzeENdS96XJIkgrGduHzRlo5Vka4DddMihBDVgBiz+WoczSXLqyqSpMynR9R5ft7wrEZGzTJ1rp76FomJ09yqe0QIi5nhN/C3DdTdt5MkiWD7LmTFgWU2LxqbnDxLavp8S0KCbctg4uJL+CJ9BGM768x4rim6aOsuALDF2lCNqNu+8LdtW8xcfZ3R08+tql82mxJRj5nho8yOHGtJH7fMMmNnv4cv3FNNt7j5vpRV/JF+EuMnN740VQuIT+jEJ3UsY3O5d61skcwr56qr0CabZhpl4uMn8QQ7ayY0iuYi2nuI0RYZQdXhIdy5p+bx6kIpd8v5xzfTVHRo5NC76f7gp6uFdevQ/uhHib3rQ003Sgib2eGjJCfP0Oy3ZJsVpi7/uKHBcHrCOOu4/m4N0dKcGGve9VuvzZIk4fK11Z0NbXH7cmPEbmryDKNnnl+VAWw510TxW0SllGZ2+Fhdz44kSdVBtY4n5HbkwY9E+Lu/s4PDT4ZweVub09oSVmWbBckRAvD6AAAgAElEQVTJMxjl+ivYcOd+tBbVJfWF+3H5YnVdobnkKKXsrdd2vZGmvhlJ1ZCdtSoeCwiBu72n2TahF9NMXflx04Er1yhmpyik66t8yLKC2984oX+jyMxdwdDru1U1V2BL2/MOpLoHmGLs3AstXcVtVlLT57AauK+c7jCSvAkNRhNMXi6haBKf+41t/O9f3s+Hf6GLru3uWxHZWT2KjKTOf54SqEEPasjb8EfxuVasAlTKz5GZu1wT/SpJEp5AR40qWLO09RxEqbvgEsTHT6zaa7AmUy7Z4VyRxFM9hBAkJ95BL6aWf/EyWKZOPj1WN+xckhScntWuBFuPZZQpZqZx3SSxBaCqTuQGq+8tbm9mR45RXOWM9nbB0POU8nP4I/01z6kON7KsssHymi1h9HyRL/3Ty7T3uzj0RIj7PxThPZ9uZ/hMgde/HWfoZJ5CZm1l47r/3geRNIWJP/wbZJeDwd/6GWTvEgsY26Z4cZLpP3sJI7FMjIOwmRt9m0h3nf06SSbWfy+p6bOr8iQ43EGCsZ3Uyz0s5ebIJYabPvY1VmwEJUXF3TWArDlwRtpR3T582/YgrMUX6Ai1EdhxkMzF5kJkLbNMcvpca+TNhKCcTyCEjSTdZJQlCc25fHLoeiMQlAsNAo0kqW4Kwxa3N4aeJzF2gjslNWA5hG1RKaWBWiOIJNUNDLtdMQ3B5JUSk1dK/PAvZ9n7UIAnP9vBr/yHXUxcKvHGt+O89u04+dTaGMPSlSkkZT5/WpZQQ17mvv46Zq6+y13xOmn78BGiTz/E1J/8YNkumUsMU8hMEWjbtuhxSZLwt23DE+hcVb5qMLYThztY87gQgvTMhfko/NWx4t4maw7CBx/C178Lxe1FkhX6Pvb5Wte+sNGTc6Teeb2pBhnlPIVU8x9azfH0/LxBXWwEJZhPR1jF7vBaIFhS6qrGmG9xWyOEIJccQS/dPXmUQthYDYLqQGrai7RZkRWJcIfGkfdFuP+DEcKdDl57Nk5iUuexn2pn/6NB/t/fuEIu2XpDmPrBqUV/W4Uy6VfPYyYbR6Oa6QLtn3m06hZdJirfMnXiY8fxR2q9bZrTR6Rrf9NGUJIVIj0H6/YH26rM5yKufuxesRG0ykUmvvcMqsdP7OEP4O7oI37sxcURoAJsQ6c8N4VVbq5qQyEzhdXC6gnXVFpqkKSqQdlkNhBY2se9JXt2hyHIJ0fvrohfIe6I6M/lUDSJ/r0eHvlElENPhNFLNm/+TZxjzyeJT5SxbXjzuwl++Yu7OPhoiNe+1by4wUoQUFWFWapSBGCk8kiOatm3lQyO6ekL6LtSNSpfkiQR6bmHqSYVZFzeKIG2bXUVlHLJUYrZ2Vs+Zj1uze9g25j5DIWxy6huL7mhs00nxjeilJtpWiy6LsvebJvNCm6mtmyx1gjbvmv2Au827v9ghM/+swHGLxR59j+P887LaQpZc9GQFB/XmRnRcfvXfvUrdJPZr722rDyaFvZhJnKsdCwqF5OkZy/Sse3hmudcviiB6I6qosstEu7cWyPNBoCwSU6eaVkUdVPO99L0KJKirEpGqx5CCPRCijvREMiKhqK5URQNWdGqAsmygiyrN/yuIMkqnmDXRjd3i3VCCHu+z9/uSMiqhqq6kFXHfL9Wb+jX1/u4rDhw+WoDv+40ZkfK/D//22WGz+QpFxqPlUe/G2durPlk75UiTIvc0cvLvq5wfpzy6BzYKxyHhSA++jaxvvsWyUgCKLJKtO8Iqakzt7T6r8qkHajrCjX0AqkGIt7N0JQRNLIpMtnW37hC2JjG7Vt1GqrGzumN4PJG8AS6FipvV6tuzxs8SakmmErSvEK/DPL878hbLs+7CSHWtODzWqCozvk+3oYn2IXbF8PlrdanlOX5CZ4kV1Md5itQVPv7Db+vUaWBzcTwmeW/VyHg7e+v/yRIcqgoHifCsrEK5UUGz0zml9wzrEchPUUuOVIr9HGtxJK/45ZKLHmCXTVqNFBdKKWmzzctll2PTRWGJWwLu8Xu1fVAVjTc/nbCXfsIxnbh9kXRnL47Jt9pi7XDFha22PwJAYrqxBPoJNJ9gEB0O05vZKHM0hb12XbQSyjm4MSLi42cwy1z7/vCnHo5TSm3vt+97HYQfv8hQo/uQ/G7wbbRJ5Mk/uYt8qdHV776uwnLLBMfO0Ewuh1uCt7TnF4iPQfmq+Cs4PiSRFv3wbo50bZVITH5TksFHJqUTdPwb9tLfuwytl5N7tUCYSKHH0XxeMleOEl+5OKtpzkIe74yRCtZuxmnJKsE2gbo2P5uQu07Ueb911s1+7ZYMcLe1N5/WdEItu+ic/u7CEQHFwamrT6+PNsP+Ri8x1djBGUZnvrlXsYvFpnIraM4giwTe/phIh86TOb1i+jjCWSHiveeAXr/0VOM/tuvUzzffGR+evo85WIKty+66HEJiWjPYaavvLqiABnN6SNURyYN5nMD41ebbmM9mjKC7o4+uj/w04x++8sUx4eQHU46n3waX99OLL2Ef9s+xr71pxQnh2/puIK1qF23NiOM5vTTs+e9tG97sG61ilth4ZqFQCBucBltscXG4fSE6dv3Qdp6D1f3sZvs49fvaTH/7+7u45IsoTllFHV9JxKyUyX0+H6m/vRF0j86uxCAmHjubXr/4ccJvnvvqoxgRc+TnDxN9673LP5u5yUfV1piyRfuq4rK10SF2iQmTmGZrcsegCaNoLOtA6tcXKge4ekawNu7g6kXv0F+9CL9T/0Cgd2Hb9kI3i44vW3suPcnCbZXlQwaDQ7Vm78aDm5bJkY5h6HnMPQilqljWxVsy7jpxyTSfYBQx51XaXuL2wdvqJsd9/403nD98jXXWOjjto1tG1RKWQw9j1mp9nHLrPZxYRtYpjGfsmTTPvAAvnDv+l3QOjKw30v3TjfbD/mI9bl419PXV0aSBNsO+rCtamHddUWSkDSF0tD0ogh8u2xQHp1DC69S61PY1YK72x5aVDAcql6zaM+hZUssVQvzHoI6EyRDL8xXj2/twqYpI6g4XZjlIsKoVDc+99xLJTVH9tI72IZOcXIEZ7SjpQ1tntbOtjSnnx33/VSDSg9VqnUQi+STY+SSo+STo5Tyc9VCwLaFEBZCzFecvtllPD9r2jKC68/dujK5GZcvxs77P4Mn2L1kVXlDz5NLjpBLjFBIjVMuxLFMY9k+Lisawej2O9YIekMq9zweYtf9fjwBlU/8T4t1lPWSzfN/Oklicu0jQm9EGCb5U8N4D/ajTyYX9v+UgBv39k5SL956GsPNlLIzZONDhLsWF9ytVu7ZicvbNr83WB+HJ0wgOlg3NzDfArHsejRlBG2jgqyooCg4ghG8vdtJnXoN26guU4VlbiIl+NbNGiRJoXfv+wlGdzQcHMxKkbnRt5gdOUY5n8BuYeL/FmuJVO3TdzmK6mTg4EeXNICVcpbZq28SHz+BXkhir1Lo/k7j7KsZLh7L8sRnOth5xMdf/rvrxWoFYFZs8mmzJcqQyxH95INosXnZMSGQFJmOzz5O4MFdVGbSyA4V945OhC0wU6uPUrZtk7mxEwTbd9emS2gu2noPUTzb2AgGo9vrajoLYREfW71Ydj2auuvLs5OoDweJHHo3jlAbkiSRvXyt7p+EFgw3rRizmfG3DRDtv7du1KcQglJ+juET3yQ9d7k12qdbrCt1E3PvMsKd+wh37m1oAPOpMa6e/Cb55BibOqJngzErgqFTeRwumeT0xk2EnT1tOHsXB6roE0lklwPXQLWKjlWsAALvwX6KF1YvWZmZuUg5H68tuItEpOsA01deq1spR5IV2noP1T1mOZ8gGx9addvq0Vyy/MwomQvHaTvyKLZlEn/zB1TSVckfxeXC1dZJ+vzbLW3oRiPJCrH++xoOlIaeZ+j4X5Odu7L6c2255dYfCTSXb6NbsaHIqoPYwP0NvTjlfJwrb/3lku6slZ/szu/jw6fzjJ3f2MXAxB89t+7nNI1S/YK710osRbeRnKh1vXoCnXhDPXVdoemZiy3NDbyRpoygsCxmfvRt0meOYhuVhQCZKhKzrz5HcWq04ftvRxTV1XAfUAib2ZGjZFsSuittFc7dABTViebYfFVF1hOHK4gv3Fe3j9uWydTlH7VG4k2SUO7AupiaU6Kte4X3roD4pI5ZWf/VtOzScG/vxNkTwa6YlC5Po0+nltUUvRVSU2fo3PFunDdXgJBkor1HSE2erUmHC7bvQnPWTkRts0Ji4lTN462i6U0QYVmU52oL1lrl4rxr9M7C6Q03rJRsmRXiYyda4gKVJKlu6ZC7lUYpM61eLTs9YeS7fPLh9sdqovquYeh5kpNnaIULVJbVllUd30z07fHyT760d9FjsiwhKxKWKRCimhqiqBJ6yeLf/PxZJi6tbxFlNeKj5+9/CN+hAeyKiaTICCGIP/sm8W8dRRitiVgtZmfJzl0h1ndvjQKWv20Alz+6KMhFVrSqTFq9gJj0OMVMC7wPDdiKBFghLk+k4cCrF1PoxdaUwlEUJy5frCXHut0RCESDoAtVc9UNo24Wtz9WPeZdjMsbafhcKTdXLUvWAlTNsykLWq+WyaES//FXLyz8ragSH/hCF4W0wYmX0hSzJoE2jUc+HuXKqTwzI60RgF4xikzsU4+g+Jxc/e1nqMykQZHxHxmk42ceozQ0Q/5EqxLRBfGx47T13DNfsq6KJEk4XAGCsZ2LjKAn2I3bX3/cS06ebplYdj3WxDHv7hqg84lPrsWhNwxFczXU9DTKuZbJ+PgivWgNZuNri6DhLH+jtB6FwGxQd07R3KiOFgWySDKB6PZNFNG8MSwVGFQpZxAtCoQJRAfvyOLQ5bzFxaO5hZ/q1obEV/9ghLdfSHL+jSxv/k2Cr/zeMIfeEyIYXd/PQHaoBO7fwcwzr1C8OImZKWIm86ReOk326GX89+9o6flyiREK6ckab44kSbT1HLzhfpMIte9E1WrHPaOcm88NXDvWxAg6AmHcnXWqRm+xJJKsEOm+B1ld//0SQXXfpx6K6gR5/Y2gbVt1o8gAZFnBG6wV2G0Gzekl1LHnrpcCW4/dKVl1Eum5p6Wr+M1KrNeJbQvKhcUT5FzKJBjT8AbXedIlSUgOtbaqvBBYhTKys7VG+VqATD08gU48gWouuayodSOShRCkZy+hF9dWYHzFPVF2upHm86gkWUHWHA1+nCjuOy/KzjLLDescak5vS8SyvcGe6gxpIwZjYWMZ9fcnFNWJ07X++5TCMtELybr7gpKsEmzf1YK9QYlo72GcntAqj3P7YxmNXU6a04fUAm9AMLaDYKx+nu2dRnJKp2ubm4OPhvCGVFxehUCbxrufjmKbYt3Fs4VpUR6ZI/zEARTfvGdLkXF2RfAfGaR0eeVVHlZKauoslVLtVpHq8BKIbgfA7W/H5W+vba9tkhg/2VKx7HqsaCrijHTQ9b5PUZ6bZPrlbxHYdQ+hgw81fL3DH8Ys3Vl5gnoxjaB+4IvTE0Fz+pccRJZDc/roP/Ah1A2KULRtC72UoV64giwrBGI7KGRqA6HWEiEsCpmpasDRTcr0kgSRrv1MXXmFcm6u6XO4A+107XgMSVr7oqabnaVm3G5/DEVzr6rsk9MboW/fBxftEd3JXDyW4+zrGX7xX+8gMamjFy28QZVQzMF3vzRJYmqdFWMqJvFn36T3Vz6Kd38flZk0kkPFPdCOPpUk89qF5Q9yi+jFFKnpC3QMPlyjIBPq2M30lVcJRAdR1dr9+GJullxypOVtupkVT6MlRVlYCWrBNtztvUjK9YKwN/7cia4OvZDEqtQ3corqpK3nIM3um6kODwMHPza/stmYGbKwLUq5mfrRmJJMtO8ImnP9I/pyiZEGgrnVKNqe3U827T52uIMMHvokTm/krliZLEcpP4fdQJxYcwUaKvuvBIcrwOChT9bNA7tTqZRtnvmDEf7kn1/hzCtpxi8UOfZ8kv/06xf5/p9Ps8YLnLrkT40w8u++QfHSFIrXBUKQ+O5bjP3Hb2OtQUULIWziY8fr6oV6Ap04vSH8bdtq4i3EfPV4s7L20bMrWgnqyVlGvv4lhGUtuASLE0NMPP9MnaWqROjAgwT3HGl1WzcUy9TJJUeIdNdxV0oSnYPvIjs3dMszF7e/g4GDH533iW/k5EGQS4xgW5WaPEVJkvCFeund+z5GzjyHba7fDLaUm6GQmawGrtz0uUuSRKzvPgy9wMT5v72FCDIJX6SfgYMfratTeLdilHMUslP4IwM1n4ksq/TseoJCcoxS/lZW3hK+cC8D93z8rvysK2WbUz9Mc+rlNLIE9kYLSQlB6dIUpSvTSKpSrVzTorSIRhTSk3UL7qqaC1+4H3+4v6ZfmJUyyclrKmRrywp3ZgV25frAJ8wKRjaNpde30lapNaHUmwnbMkhMnq4aq5vcOZIk4fCE2PnAZxg9+z1SU+eW1gyVZNz+GG3dB2nf9hBOT3ihEwghMPUCisONLK+viy6XHKGcT+AJdtUaHFmmY/ARNJefqcuvUEhPLKkGjyTPJ6B7UDQX5UKiKXexbRnMjhzDH+mv+dyrp1Ho3vU43mA300M/JpcYbeiyUx0ePIFO2noP0dZzD5rTv+hzN/R8de/rLhuor2FWiqSmzuGP9HOzV0OSJDzBLnY99DlGzzxPZu5Kw/QVqH4vnkAX0b7DxPrvu+mztjH0wiYpyrtO37Voul5tcyx3WbZAVNZH89UyyyTGT85Pgq6bHFl1EOk6gOpcvAUkhCCXuEppFdsct0JT4Unps29V3Z4NqGSTFCdbW/hwM5CaOkc+NUYgOki9QaKqvv9pCulJMnNXKGamMCoFhG0hywqay4/b34E/0o870IHD5V80CAghKGVnGH7nO2y/91NL5m2tBZZRZubqG2w79NSC6/tGZEWlrecQwfZdlPNxSrlZDD2PbZlIsoyiOFAcbjSHB9XhRVEc865KwaWjXyWXGG6qXcmJ08T67iXUvqvGbVKtS6cS7txDIDqIXkxSzscpF5JYZgVJklA1N05PGKc3jMMdqqn/eE0Tc3roNQYPP31X5wvGx44T678fTyBGvT7uDfWy+6HPk0+Pk527QjE7jVkpIYSNrKg4XAE8gU58kX7c/vaaSYUQgmz8KpMXf8iuBz/XujQXqgnXDncIWVaRFAVZVpEVDUVzoaqu6v9a9f9rjzl9bQ2PF+k+gMMTwjJ0LKOMZZYxjfLC75ahV0ui2SbCNrEtC8vU6waCLIkk4/KEqzUb5Rvb7URRXaiau/q7dv13zRloKPjuDXax5+GfrZazMnRMc77NN16DqWNb8+22TYRlUi6m1kzzODV1ju7dTy4quCtJMpHu/TWpScK2SEycWnKS1UqaMoJWeenqwKWpUUp3mGwagGWUGDv7Arse+hwOV6Dm+aoahJNAdHDeUHK9lIwkL1uXrZSb48rbf0UuNUopN7vuRhBgbuw44a59DVMGJElCc3jQIv3zK4blMY3yqlZXlllm9MzzuHxtuLyNBy1FdcyHXneu+NhCCCqlNCPvfAe9lMasFO5qI6gX04yf/z7b7/3Jup+DJEmoDjeh9l3VSQnX+riohuAv08fzyVGGjv81ejFNuZjE5+hp+PpbxRfpZ9cDP4OsaPMGRUVa5r5bCqcnvGxSvxBi3gCa2LZBMTPN2Ve+xK248RxOH/se/XuomgtJ0ZAVFUlSmm636vAQ7ty75Guq7baw7WoNU8soc/rlP8Io109JWi0VPU9y4h26dz+56LrqBUnpxRTpmUtr0o56rNwXcdu6iFrb7szcZUbPPI+hryxKTpKkasDQkoODTSE9waVjX6nuKc7PlhtJhq0lllFi+NS3KWQmNuT8jcinRrl68lvVKN0WtUsIgV5MceXtr5GNX8UoZVf8vd65CBLjJ5m48OKK3dfVPr7MJM+2ycavcOnYVynlZhG2ST452tI+dm0lqDl9KKoTeZn7rhVIkoSsaKgONw5XAK3O5HjZY8gKDk8QzeVH1VzVley6tFtF1dw4XH4cnuDauqaFTXz8FGZl6QWUEILU9LmG+cFrwYquWvOHiT38AVRfNVdM9QZwhKJsiIrILdP6gXx25ChXT36DcoMctpUihKjuNY6f4sIbf04hNb7wXCE1vm7ugJsp5Wa4fPQZMnOXsW1r0xjD1NQZLh97hkJ6fL5wa/Ptsm2LbPwqF9/4c9IzFwCBbZuU8/HWNfg2RQibyUs/ZOT0d6mUs6vu45ZZYWb4DS6++ZWFz1cIm3xqbE3qw22xOSlmpsglhpfsT5ZRnteoXT9W5A7VgmFC+x8ge+kUZj5DcO99eLr6GX/uKwhzieCIOxUhiI+dpJCZonvXe4h0H5iXnFraHVR9a7UDCNsinx5n+sprJKdO14SmlwoJKuXchrhEAYrZaS6+8ee0DzxA+7aHcPmit+Raut7RBcIyES0Ki8vMXebcq39K+7YHifXdh8vXtqJ2LbRH2BRzs8wOH2Vu9K2amWlLqiTcAQjbYvrqa+RTY3TvfoJQx56FqOGVfta2ZZBLDDN15cdkZi/VBFIVMlNYRhnZeXdX77hbEMJibuw4oY7ddYPcoDru5G9YDKwHKzKCklL1rQvTnP9bQdKal/bKJYbrqq/YltEykd5rVMpZEhOnkOvoQuZTYw1VYJanGsQydPzrTF16mVDnPgLRQVzeyLw7xoUkywu+d8vUMfUCeilDMTNJeuYihfQkplHfPVAppYmPHV+0B1aNYFw/d51ZKTJ56UfMjR4nEN1GsH03nkAHDlcA1eFGVhzVAClhY9sWtmVgGiXMSglDz1EuJCmkJyikxim1cIVllLNMXPhbZoffxN+2jVD7bjyBdhyu4A3tkhHCxrZMzEqRSjlLMTNNZvYS2cRVjHKeel6CzNzlakWQmnM2754RwiI9X2j0ZiyzvGEr/mWZDxi6fOwZ3IEOIp378UX6cXnD1cAn1XlDHzexDB2jUkAvpiikJ0hPX6CYnWmYulLKzhIfO76ofI5tGQ3yQpfHKGeJj59oibJNs+ilNLfiffLv6SCwrZsSY5TH1s8FeDO2bS4d7d0iMrOXKeXjeINdNc8JIUiMn1o6sn4NkJZamkqSJKAqiN3/1C8w/aNvk796nrb7Hsfd1c/Ec1/BbrQStG1sY+MqKm8EkqyiOjzV/Yj5iQNCVAdj28QyK1hGaV0629ogoWhOVM19Q+CBtHCNwrawLGN+ICvj23sPkXe/d9ERypNjzL7wLKKFfcMzsJPIw0/gjvVgl8rk3nmbwpULCMtCCAvbrCxE9W1VQ18d1f0vD4rqWAg8uRb8ZVsGllWpRotuVsO+ydj7Tz5A18cPcukPf8j4Xx/f6OasOZKksP/xf0AwVivWrRfTnH3ljynlZlt+XiFEw5nRilaCldQcenKGrvd+CuP+JKrHh6w5GfiJv99QWb48O8HkC3/ZZJNvT4RtYpSz3K4mbnnEQqj1SlC8PlzdfYses/RqpGirTJEj2kHHU5/BEamGXsuAP/IEuekrlMbGWnSWLa5hWwaVUmajm7HFbYo31L0gnH0j1dSZoQ3Zk1+REbTKRSZ/8DVC++7DEW5HUlWQZIxcuuHGtlnYuKX9FncP7r5taOHFaROK24N31z5KY3deruoWW9y+SNX4iXr6yMImPn5iQwKlVpwnWEnNMfvq90CSiN7/Hjy9Oxh/7i+wjUbrni230xZrj+z21A3UUNxbwRZbbLGZUB2eurKTQohqQExyYzw3t5gsX02KNQq56xF/LbDcksOBs60DZLlaPmd6YtXH3OLuwMrnELa9uJSVEBiZta1BtsXmQtJkvANteLe1oQVcYEMlU6Q4kqQwmkSYi8cpSZVxdwfxbY/hCFeLuVbSJfJXZilNpBFW7STeEfESe3wn+StzZE5Porg1gge6cfeGQAj0uQLZ89NUkrXBa6rPSfBgN67OAHbFIn9plvzV+KZJP1oPQu07G0S7C1LTFzYsR7cpxZjixFWsYq4qqN0CPAM76HzqZ5AUFSOTYvS//YeWHXuLO5vS6FUq8RkcsesqMUYqTv7cqQ1s1RbriXd7lMGfe4TQoR5UjwNJlatanaaNVaxw8T+9yOyLFxde7+oIsOtXniB4oAvF60BWqxKQtmlj5nUmv/MOI185iq0vDu5xdQTY/gvvYubFC1RSRXb92pOEDnajuKrh/rZhMfKVowz/2RuL3ufbEWPXrz6Jf087ikNF2AKzoDP13NmmrldGQUbGwmwYk7HZkGSVtt7DNRJpAJahkxg/yUZ5D5sygkY2iZFNtqYFkoS7fzuq1w+ShFUucXsk4W+xGTDSCaa+8RcEDz+IGopgJONkTh6lkmh9hNkWmw/vYBsH/8XH8PSHyV2eI/H6VYrjKWRVxrutDU9/hOz56UXvscoGitdB5twUqeNjFMeqXoPQ4V56Pn4P/Z99gPxQnLmX60t3+XbE2PVrT+IIuBn5yjHKMxlUn4vAvk7S7yz2YmlhD7t//b0E9nWRPDbC9PfOYOR0Avs66frwART3ymoregnQKfcTltrRcGBjcd56mwyJ6nlw4MFPhTIlNp/qkS/cV1NFAqqu0MzspVusTNJamjKCrUSSFTwDO29jWbYtNhp9eoLZLRf6XYfsUBj47IN4+iPEXxvi4v/9t+iz+ZrX2DeVCjKyJc7+7ncxcuVFbtLUW6MIy2bb5x8i+q7tDY1gYF8nidevcuo3n13k+px49lTN/D326A6C+7vInJ3i3B88j5EpLZyrOJJk3z/78DJXKdEh9bJTPoRb8i7kQJrCQJaUhcWTBz+HlUfJiwwn7Few2TyeNFnR6NzxbpQ6WrTXqsRsZNrYRtcx4f9n782j5Lru+87PfVvtVV1dvaMbaKCxNFYC4L6LpEhKlCxKkSXZ8sj2xM4oPslJZnwsO44cT3ImsZNJHHlJ7NGZxLs01mLttCiRoiiSEimS2IgdaOyN3pfal7fcO3+87uou9N5ooEGyvzo4YlfVu+/We6/u76MZfmEAACAASURBVP5+v+/v+zPrUzPYfWtYwxrWsBCsVJT6uzrxSjYX/+a1GQYQQNrezCibAnu8OCNPqKQid3oQWfGwUpE5A1JeyeHKVw/OzP2pa/olaYLUPRtBEww+f6pqACcxfugKxUvzR9TqRIpt2j6ChMmrDL3yHEU1k3lfJE+FMnGRJEx0lpFWD8mW7dS37pjVC8yNXiQzfG6VZuZj1T3BYNt69OC7V7V/DWtYw/IQbq/DjAbI9QxR6lta7aLQNaz6MNHNTYRa45ixIFrAINyeRJgaQhN+dGoW4oo9XqyGUOeDZmiE25PIikvh0uiM9918hcpwjti2mXVzABoa7aILiwBX1Xl65FEcbG7T7qde1K6ZDhVKKk9ExImKBHl1a9RyhuMtrN/55KzdIjy3wsC5n9x0hZhrsbpGUNMJtXfCTW4eu4Y1rOHtDzMRBgH2eAnpLp6lbiZCrP/EHTQ/tg0jEkA6Hm6ujFdy0IOmbwDngbQ9vPLC4TstaKKZuv/5yuwKOk6+MuvrAAYWCdGATYWL8hQO8xuLCiU0NCxuDaciHG9h0/6PEow2zp4LHDzD+ODpVZrdFFbVCGqmSaij813byXsNa1jD8qE83/AJXSyaUqBZOl2/ej8tT+4k3zNEz5+9RPZkP17ZRUlF6s4NdP/G4wudeXGSw57yQ7EacxrW+dY+HR2LADnSCxpAAG8iDyhuZJZLCDTNQHous7E5hdAIRFIkW7fT2nUfgXD9rN/RLmfpPf3CLSGvt6pGMNCyDjMxf9PKWwl6OIoRi6OFwmiGMUHDdpClIm4hh1cosJI0X80KoEfj6OEwmhXwxarxFf6lbSPLRdx8DlkuLTDSSkxGw4gl/O8fCPqakZ6LVyriZtN4xdkYaW8P+vZKQpgWZrwOPRJBmJZ/naSHrFTwCnncXPqGl//okShGNI4WDKGZJgiB8jykXcHL53FyaXgHlCBVhnMoqQi1JNACBl5pYe/MSkVpfGgLsuJw+o9eIHd6ZteQhTzBxcIrO7iFMoGmKEZsFu9MCMy60JzHK0ChFi0IbhFAoXBvoHBjKNrItrs/hWPnsUs5pGfjeTYCDcMKEYw2YIUSWIFodb26FtJz6TvzIoV03w2b51JwU4ygsALo4QhGOIJZ30CguY1AUxtWqhFhBWonFIvT8alfW1IRafrNH5M7vjTxWT0coeXpT6JNnF95LiMv/APlvsu1czdNQu2dxHfv9/OX4ahvBPQJg+S6SLuMVyxij49QPH+GwrlTOGNL18ATuoFZV09w3XrCG7cQaGzxzxcM+ouZpgMKJSXKcfxFrVjAHh4kf/Y4pUvncHPZJZ93PujhCOGubcR37sNqaEYPR9BMC7SJhbVSwctnKfVeJHv0AOWrV1CeW702kx3Hlz8Bnab3/gyBlvZFH6Kkx+B3voIzvhI6hIL6Bx4l0rW9+kqh5wRjr74Ik+2hhMBqbCG2/TYiXVsxYnXowRDC9OUFkR7SsfFKJdz0GPkzx8mfPoaTXqEyI/z7FNrQRWz7HgJNrRPPaQChG/71nxC090pFvEKu6kXND8X4qy+SP3Nz+7stFqW+DOX+LMHmGPX71zP4w9ML7rvMWAA9YFAezlMZriWYCF0jvqMVYa5MekZJSfbkILEtzST3tjP2xsWa+QUao4Ta6uY83sOlQpEQUSwC8xo3iwAxkUTiUWRl14Dp0DSDULyJsGhZ+MOzQCnJ8OUDDF54nVtlk3xDjaAWDJF68HGCbR1VL0IYUwnS2dxkzTAJrd+4pPPkzxxb8tyEbhDq2IAe9NUilJKYdfU1RtCsbyD10BPEundXW0ddO2dhWWiWhRGNYzU2E926k/FXX2T4+W8vei6TC1h8135C7RvQo1PdqWdeI4HQNdAN9GAII5Yg0NxGbOdeKkP9jP/0JXLHD19/n0ehEd7QRerhJwl1bIBZevYJQ0MzTIxwBKupldjOfeSOHWL0ledxs2lkpYxS6rrC3UIIAk2thDdsWvQxynXRrOW3+qqdAFipptrzK8n466+gpI0WDJG88wHq7rivet9mfF/dQNcN9GAYs66eUOdm6u64j7FXXyT71oHru1eaTqRrG6kHHiPYth4mlHNmzkFH10PowRAsko2tlCJ79MDy53aDYaeL9H//BBt/8R42/cr9SMcjffSqX+QuBHrAINAQxcmWKA/mJo4p4RZtrPowkY0NOJkrKE+hhy0a7ttE8yPbVm6CCoZePkvzo9toee92xg9eIX3sKsqVGJEA6z60h1DL3J3oXWzG1BDtYjOdWjc98ig2M3OIOiYd2laiJMgyTlbdmmpJSkrG+o9x+fizq06GmY4bawStANHu3Zh1s8eFbyUIBEY0Vv070NpBywc/RqBl3aLnLibCTqXei4s+rx6J0fKhnyOyaStoi29aO/2cE/9BoLmN5qc+ipVqZPSl71f7Py4ZQhDfs5/G934IPRxZeE7CD9jowRCJ2+8h0NzGwDNfRlYqKNdFrJRBukVg1jeiGSaYJk1PfpjYjr2LvneTnzHrG2l68iOYiSSjrzyPmlODdx5oOsm7HiD10BMTIeqZ55+MqAj/5Es/x60Mqbj6jSOE19XR9J6t7Pjs+yleHsMeLfjsz1SEYHOcU3/wPOUJAoY9XmToxbO0fXAX23/jccYPX0FWPELrfAm1kZ+co/HBzSs2xfSRXvq+e4z2D+9l5++8n8zxfrySTWhdHUbYYvT1izTcP7OtEPih0CvyHCm9hTaxkbAeZ0T2ExRhBBpJ0UiMOlKihXrRhIfLJXkKj9XPs02HUgrp2YxcOczl48+ueM/Y68UNDocqkB5IObvjK3wa8vQfr1JqKsy06NOsgFstBEY8UQ1ttT79c1iNLdW5+T3Tpv2bnP+0nbdSCiebpjK4+Fi3VyoiK+VZF9HJcyrpzTiv0DTQ9JpjhBAI0yJ590M42TSZA68u69pEu3fT+PjTGOFaEeqa+UwLBSI0v9GyEAihEWzfQMvPfML3ljwXWL4RVFJRHuj1Q+rBEFoo5Bug6rOjLWvzcD3Qg0Gshibie+4gtnNfVbd0sq+e8qZp6k7OUZ/tXpkk73kYJ5Mmc+i1Jd+r+J7bST38pJ8vnvacKtehMthHub8Xr5BHKYUeChNoaiXY1jGrwfSPc1GOjZPL4GbTuJnxW17H1y1UOP3HP2T80BUaH9pCdFMDwZYEypPYowWGX+4hf35KjUQ5Hhf+8ic4uRKND2ym4b6uiRKGMXr+n5cY/sk5whtSMyTTwPdkvIo78d7i7pVyJRf+6jXKQzla3ttNfEcr0nbJnujnylcPYSaCJPevR7mz52iLZDnpvck2fT91NFCnNVTf2yh2TOxrBA42F+QJhtVNuF+KBUW9JjdfSroUMv3097zC2NVjSLm8qIceD5N89DaUUow988ac12s5uKFG0CsVGf7BM9W827UItm+gbt89oE/F4L1igZEXnlkSeaDcvzLq40YsgRGN0fy+j1QNoFIKr5CjdOUilcE+nPQYslICTUcPR7BSTQSaWgi2tKOFwtjDA0sTb5YemUOvEd26E8yJULGU2GPDlPt7qfT3Uhke8MOL5bIf5gmFsZpaCG/cSnTrzhnemmaa1N/7HgpnT+IuUUjaamii8dEPzGoAnbERsscPUbxwFic9hvJctEAQq76RcOdmot27MOv8UFuwbT0N73kfwlycLNR812fkh8/6OVhNQ2gawrDQQ+HqdWh46An0UPj6zrMECN2g4ZH3E2zrmOqs7rmULp0j33OS8tUruNkMSnq+wUw1Ed60lVj3HvRorNYYGib19z9C8cIZnPGZtWRzwUo1kXrwveiBKcKFUgp7qJ+RF5+leLHHb2o9bbMiTJNg8zpSDz9BeOPWmnl4hTyDz3yZ0tUrKNfxGxJ77tI3pFPfzF8ob4JAtCw7DDx3kqGXzqIHDIQ+cU9c32ipGYoxZS7+9Wtc+cpBP/8nFdJ2/bIHBW999pv4G/jaued7hnnjn37B/3x58d6WV7Tp/dphBp497p9P+aQZWXERhsZrn/oL3OLc4cFxhjnsvUKr2EBKayFMFB0DhaRCmawa46q8QIaRG64l6lTyDF16g0CkHisYwzD9JuKTHAnpubh2EbuUoZjpZ3zwNLmRC7jOPOQ9XYMFctRGIkLDR+5DScn49w6+fYygchzyp47O/b7nkdh7F4IpIyjtip8n8W6+S28mG6i//zFC6/38j1cqkjn0GplDr+NkxufM3WhWACMWJ9LVjZNNL3nhKF+9TPFSD4GGZgoXzpI7cYTKUJ/PuJxlLDeXoTLUT+7kW2QPv07Tkx8m0No+bVETmPEkse7djP/0pUXPQ+g69fc/hlnfUPO6kpLcqbcYeeG7Ptlk2sLm5XM4o8MUek6SPvATUg89SXzXXoTQsFZICUi5zoxr72Z8UombyyDvfc9NNoI64U4/ZKaUwhkfZeSH/0Ch5ySyUpuz8fJZ7JEh8mdPkD3yJk3v+zDBdRuq90oIgVlXT2zHXsZ+/INFTkAQ33tndcNRncfYCP3f+OLskQilULZN6coFBr71JVo//ElCG7qq89DDEYLrNpA/ff0kGD0YpvXBD2HFkvS//G1Kw73XPeZiIKte2sJQnsKdo0bPzc3eNFp5Eie9TCa2UriFmYZOuRJ7vLjg4WUKXFAnuOKdxcCcKINQeHg42ChuTh8+u5zl3KG/R9ctNN1E0w2E0KdC7UohpYvn2nhuecFNUHBzK22ffoor//dXcYZXp8B/1RVjbiUEW9cRaG4DIXCzaYa++zXyPScXNGrSrmCPDmOPLk8EVtoVhr7/TZDSZwwudvfsef6i9sxXWPfx/7W23ETTCHVuJn3wNZSzuCR0cN0Golt3zghPF86dYui7X8ebr1HyxCI89L2vIwR+mPCdloO6Bkop3Mw4A9/+EqVLC0g/SUm57zJD3/0abR/7ZYxEsmbTEunaRvrNH/uh8QWgBYLEtu2uvb7SY/ynL1EZ7F/weDebZvTl51nX1lFlZwtNI7b9NtJv/Bg3d32LkRmtI9G1G820CLdtvGlG8N0AF+eGlkAsCkrhuRU8d+5C/8UisquTQEcjwlg9wZRV1w69lSB0A800keUSQ89+3aeGLzsctDQ4o8N+OGwZ4aNK/1Wyxw7WlJUIIQg0NM8Zip4BTSe2az9asLZuySvmGX3x2fkN4DTIUpGRF7/37ujnJz1GX/r+wgZwGsr9vWSPHax5TQiB1dCEEZ2bKTgdgaZW9GkkLvCjFvnTR1lsrqrcd5nKUK3BNOJ1BFoXX4oyF5xcmvTZI+QunyHfO7sI9VyId+0mddsDiFlkttbwzoKwDMLd7Qh9dTfLa57gNVBSkjnyhu8Bvm2gKJ4/Q/LOB2rqLo1oHGEs7hbroRDRrm0zvMD86eOUl0D0AXDSY+SOH6L+/sfesd6gUopS7yVy84T750L+zHGSdz9ULbsBX4hBj8ZgES2gzGTKr9WcBntkCK+0+FCddGwqg/2+bOEEhGH4IfnrrAv0KkWu/uDLTCQFF32cZgZI7bkPzTAZP3UAtYqdBa4HwYAgFBKYJmiarz/qulCuKIoltaR9dYAQEg8X523TO3BeCEFoSxuhTS0EN7UQ2bUBoes0f+pRZHHKs/RKFQb/9oeoyjXPgFKgCYKdTUT3bcZsTKAcl9K5fvKHzuHllh6uXjOC18ArFckcfO1tp6jhZtN4pWKN5ycM3S+yXwSCLetq6hNhIqd75tgy2LqSwrnT1N1xv1+X9k6EUuRPvrUstR43l8XNZrBSjVMvCrFoT1APRaqs5OqY+ewUI3UxkBKvmK+p4xRC+IZ4DuHopWNpY+ihCKHGdVTGV6a33CMPBnjwPp849A/fL/HmoZlpgY51Op/6uSiG4V++539U5tXXZ4b52tt0PvmxCMGg4NQZhy9/vTaPZ1mwpcvkkQeD3LU/wJYug6ZGnXBY4HkwnpZcvOzw5iGbbz5T5NgJh4W4HToG27U7CIkIZ+RhRtXA/Ae8DaAFTZp/8TEC7Q0IXUMPB0ETRPZsrFln3GyRob/7EeqaW6E8Sd0je2j82QfQQgFA+XXTQlA63UvvH38LZzC9pDmtGcFrUO69iJNePEvvVoG07VnIRMJXDFkEgm3rqwyvSXjFPJX+5VGuK4N9eKXiO9YIKtehcGFpob5JSLsy0Ty6FlpgcaHra+8T+CSzpdqt2RjYmnEdYUih1VLnFYs0zH65S6S1Ez0QmhhKqzX0ix5rChvWG/zvvxbHNMEwxKxG8N67Avz2r8erTPBkUuP1A5UZe+Db91n8xr+IEw5pfO6/Z2uMYOd6nd/9rToeezhIIq6h6zPFCuoSGhs3GDx8f5Bf+mSUP/iTLP/vX+aw53F2DUxiog4TC1stnCt+O0CWbC7//pcRmsBIRFj/2Z/DbIhz6d99AXtgWgpFKWRh5mbEqIvQ9ImHyL56ivRLR3HTBYIbmmj6xEOEd6yn4UP30P/n31+QbVoz5kp8sXcKJkNcyy4yX0UoJZe/e9d0zFTjjGJqJ5vGzS8uF3gtpF3BGR9dMYborQYnPbboPOkMeN6sz9hcWovXQs5CdJqs/Vv0EyCEf8y1Y9vLIzvEu3bRcu9TNQZaui79L3+T/OUz8x4XXbeZYEMrwVQLCI1QUzubP/Eva55n5br0vfwt8pcX33XgxCmHckVhmoKd3easDu7dtweqBhBgR7dJJCzI5qbn12Frl0kw4H/u0Fu119914fa9FvVJ32gXiorLVxwGhjyKRf/869t1NneZGDo0pjR+5zMJevtcvvnM3JEEDQ2TABVKlLkJ+sA3CbLgG3ShaxPen8LLlxcVyhSGQe7N4wz8xXPIsn8f7KujKNtl/W9/nMjuTvRoEC+zMON2EmtGcBqU5y6pVuumQdPRLMsvitYNvzB9YqcsNL9+zohEEcbyitI108SIJWbsXp2xkSXvvqtQ6m3pUS8WTmYcuRyVlypmMVeLTJ+62TTKdWpyilZ9I0LXF11aJAwTq76hZuOjlFoaO3kavFKRSnoYIxhBCwQJJJtASjRzHu9WCOIbdxFqWlc9v/8fvjaumvbsKc9b8rN49pxLNiuJxwzWteok6zTGxqfGCARg320WSsHAkKS5UaN7i0ldQiObm3IFLRO6t5pomiCdkZy7UHvfr/Z7fPErBZ58LMQz3yvxw5fL9A14ZLIS21boOqTqdZ56PMTvfCZBY4NONAL/2y/F+MGLZfKF2a+3L6AtkUhuFZ3N1YYs26RfPFo1gJMonR/AK5QxklE002Apyaw1IzgNyvPwSrN1Q7i50EMRjLokwbYOgi3tmHX1viCzFUAYhv9P8w3hVBG5vmwSitD1Wevsrpcq7xVuLXmklYRXLNzwbhBzoTLQh1cq1ZBjzInnpXixZ1FjmPEEwXXra16T5dIMxuhiUeg7T6HvPGgawWQznR/6FYzQAh3OlaLvpW9UPeCGfQ/RdOdjlEcHuPzs3+JdUy4i7aWFBItFyamzDu3rDBpSOq3Neo0R3NBh0NaqU6kovvqNAp/+xzEScY1tW0wu904zgpZg+1Y/THy1z2VwqPa+KwV/+j9yfP4v8qQzMw2150H/gMdffjFPIq7xb34zgWkKurearO8wOHFq9s2Uh0OBLGFi6JiLaqe0IDTtpjHebwSU7VDpnUUYX0qU46JN5BiXgjUjOB1SrtrChtAw61PEd+0n0tVNoLl1QbHxFYOmz1pKMVveatFQCq/8zshjzAbl2Mv3kq8TTmac0qVzGLumajGFaZG852Eqg/0LbuSEaVF310Po4VojVRkeoNx/nTV9UiKdSo0XN+/Hpxm2yTCvkhK3XPSVma4DjgtHjjk89nCQ+nqN1lad49MMzpZNJo0pnbG05IWXynz8IxEaGzTu2BfguR9OzStZp7G+w0ApxaVej5HRmd8tl/f9tvngefD17xT5P/5ZjGSdTn1SI1U/d5Wag82AvMxmbTfNop1L6syC51gIsU07CDW3kzlzBHt8FOXeOkLWi4GSClmaI2S/zEuzZgSvxU2QeboWWjBE3e33UXfn/T5D8Bo91ZmYbOo50bRzcs7L1NAUQsxOtrjO3OhqqP7cLCh5HTnY6z856YOvEunahj4hbyeEILJ5O41PPs3oi8/OrlwkBHooQvKeh0jsvasaClVKoRyH9Bs/XrSwwtsBUsKJUzaO4ysSbtts8vyEcRMC9u6xsCwYH/c4ftLhTI9DU2OAPTtNAhZUJi7Fti0mk5ylo8ftBVmd82Fk1CObUyTrwDAgHJr/93pVnSesYnRoW7BlhWF1dUGB7PlKKax4PY13v5fU3gcp9PaQPXuU/MXTuKXCqm3qlowV/tmtGcFVhh6J0fS+DxPr3jPDiFUFq10HJ5PGzY7jFvLIctnfbTsO0rFRjoNmWSTvfQQjskAIaim43kV+tYzEuwClyxdIH/op9fe+p7ppEppGfNd+wus3kT9zgvJAry+9pxRaMEigoYXI1h0EGpqnnrWJe5Q9emBZLcludfScd8nlJal6vYYco2lw1wQppueCy9CIx6kzDg/cG2DzJpP6pE7/oG/tdnSbmIZAKWaQYubChL67H5kTk108fJaq40x09hACfZ5CcQ2NBPVk1RgJkWK7dgcdbCavMvOqxoyofsbUzGbBU3PT0IMhYl27iHZux8mnyZ07TrbnKKWBK9ffhm3JWCuWf9dCGCaNj32A2Pbbqp0IYGpnXrzUQ/70MUqXL+CVCr7R89wJ+m+tgTHq6kncfh8swwj6AtAzt7eLLbSfE+/QQvlbAkoy9pMXMGJx4rv2+8L+E4bQrKun7s77J8L7E16Dplc7fVSHmOh6kT12iJEXv7u8dk63OM5dcElnfCO4fZuJroHrQUO9zpYuA6XgyFEb14XjpxxcFzas12lq1Ogf9DAM34PUdT/kefbc3NdICFjXqrN7p8n2rSabOk0aGzRiMY1QUBAMCMJhwfr2xf2uAoTYo9+HhoGGX34Sp564qJ/3OMerMMbcRnBqvsIXSKhrwNr/EMmdd1Ee7iNz+hD5S2ew0yvRlHpuKKl8IWwBejS48AE3CGtGcBUR7d5FbOfeGQbQGRtm+PnvUDh/5uaEp6Sc9Txa4PoezEVLtq1hWZClIkPPfh1nbITE/ntqGL5CCL990yxhbphigqbf/AmZQ68tq+j/7YBsTnL2nEPXRpOmRp2mJp2+fo8d3T4L1PMUbx33DduRYzYVWxEJC/bstDhyzCESFnRtMhBCcOGiU0OsmY7Nmwz+yS9Fef/jIdpaDAKB68/je3iMq2HEpKe0yMBKkbkJaW4xh1PIYYSjM1p76cEQkY4uwus24uTSFHrPkTl9mNLA5RtCGJQVB3tgnMD6RuoevQ1nJItXKCMMHWEaeJmbQ1JcM4KrBGEY1O2/d4b8lZfPMvCtL1G6cmFp4y2YR5wbynPxSjPraharYDLHhKr5qjXcOMhyifHXX0YPR6i74z4QEy1tXNd3xCdrD6WHdBxkpYyTHiN/9gT508dwRkd4J9Pv/RCmw/veC/GYX6/XP+Cxa4dJNCIYHJJcvOR7y+cuuIyOSSJhnTtvD/A3XyoQi2p0dZoopTh3wZmV/Xn/3QH+6+8l2dFtIoTAdRW9fR69Vz16zjsMDXukM5JC0ZdM+ze/mSBVv3BNqE2ZI/LHK3o9smePUhkdJNq5jfjm3QRSzWhWbX9JoWlYiXqsRD2JbXupjA6QPXeCXM9RKuPDKxYuVRWH8R8cIby9g/on9hPZuaFqBGWhzKXf+xLKvvG8gjUjuEowkw1YjS01r/m6pW9S6r205PE001q0Osy1UK6Dm8/WSGgBfkul5UpoCYGZqFvWfNaweOiRGI2PfYD47ttB+H30Kv29jL32I6Rd8WXzFCjPwSuVcHMZnEzab3Z9q0FNEL2EWLEskVI+mcVxFdGIRnubwUHD5rZdFkJAb5/LlT5/oS0WFUeP26xvD7F9m18039qik6rXkBKOnXS4NmLc1qrzX/79lAHsH3D548/n+Ifvl7h42Z2hPBOLCn79n8dJzR/RvGFQnkt5uI/ycB+jh39MuLmD2KYdxDbtwIwnEYZZ25vUMAk2tRNsaie17wGKVy+QPXOE/JWzuIX8dZNpcm+eofdz36Du0dsIrm9Ej4XxckVKFwZr+jkq18MZzqCkrGkUUH1fKuyRLHqh7De1XgLWjOAqwUo1olm1XqByHYrnTy/rwdIjsUXLbl0L5XnYVY9g6gdg1dWjhyPLqvcTmo6ValrWfNawOAjTovHRp4jfdmf1tdKlcwx86+/8ove3GdxSHlBYsTr0YBjvOkskJnHxssvYuKSpQWNTp0EgINi13S8/On7KIZ/3F9WKrTh81OYDT4ZY16rT0qyzdbOJpvnlDYePzkwZPP1UiO6tvuHI5SX/6t+m+dq3i3PuG4WY6p292lCOTaH3HIXe8wy//gMi7V3ENu8iun6r31R7ovZ40igaoQixrp3ENm3HzoyRu3CS7JkjlIf6kM4y2ypJRf7QOfJHztdyCKSq2Xzb/WOc+60/9/+Yxch52SIXPvtXc74/H1bZCM58UnwW1TufUKEHQzNkspTnLXvxCra21yiILBXl/isoz0MYU/lJPRwh0LzON8xLhFnfMKPdzxpWFpGubmK79gF+ONwrFRn+wXdWwQCKCRFjDc20MKIJ/9kWAiMcwwjHkW7F1zeVcs5NXnHwMl6lhBGJ0/rg04yfeB2vXESYJroZpNB/AbeQXfLsrlz1GBnxaG7U2bzJpLVZp63V/+29/ubU4q0UHD/pUCopmht1Wlt0tmwyEAKKJcXps7VuYMCCO/YFfPFtpTh1xuHZ50vzBk7q6jSCgVutg53CKxfJ9hwld/4ERjRObON2Yl07Cbd1TuT2xZRBFDqBZCNWXQPJXXdTGrxC9swRcudP4uTSy/MO5cJ1lgsatyUav0msqhFUrjvze2s6WiCAd9Npum9faIEg0e27rysRX+m7glcsoMWnQpjCChDduoPixbNLVpkIb9xSBOrKDgAAIABJREFUFUNew41BbMdtNTnlylA/9sjCrZhWGpH2zbQ99CH0QLiqYqQHwiAErfd/gOa7n/DDWJ5DpucoAz/+zqzjVMaHGXrzhzTf9TjxTTuJrd+Kkh4IgfI8Lj3zl8syguNpyYXLLju3W3Su19nebRIMCoolxVvHa72746cc8gVFY4PGjm0mnRt8I9hz3iGTrf0N6LogWadVtUev9nsUivMv5HfuCxAK3rqbfCU9nOw4Y0d+QvrEm1h1KWJdu4lt7CbYtK6acpk0iLoVINqxmUjbRhrveoz8pdNkzx6lcPX8ohpE3wpYVSPoFWfGlDXLwqpvpPQOltwCX41FSa+GvSc0DSOWWNpOXgjie+4g0Nx2ffMpFSieP0P8tjtrGIax7XvIHPoplSX0FNQjURK7b5+TmbiGlYFVXytOrpnWokW4VxLKc7Cz4whtYZk9rzyPsLH0GD3yEqWBi8Q27sBKNPgebrlIeXSAytjCtP+5cOiIzQefDNPWarBru4VlCU6fcRgYrE3aXe13udrv0dSoc9ftAVqadEBw4rQzw8BJCYVpup+RsJhXlawuofELH4+w1KyFgUVSNBATSUwsFsqYDqu+FWm7JB2b8nA/5eF+xg6/QrCxlfiW24is30ygrrF27dJ1zFgddTvvIrF1L5Xx4apnWRkZ8DcztyhW1Qja42NI266h0muBINFtOyldvfS21rhbCM74qF+XNW0nL0yL8KatlHovLo6MIgSRzd2kHnjs+lrg4Idis8cOEu3egx6cKo3Qo3FSDz/J4He+7BdeLzQlwyB510PXbZTXsDCkXevFBJpbqX/gMTIHX8PNZXwZspsgWFDsv8il7/z5ioylPI9C3wUKfUtjRy+Ew0dtPKmoS2jcf08AXfMFtkfHatcYx4GDhyvs22Nxxz5fXNvzFCdOzSTF2I7izDkHKRWaJti+zWTXdrNacjEd9UmNz/yLOI88GFxSxCZAiO3a7aREC5qYf4OjUEglKao8o6xs70GvXKRw5RyFK+cxonFCLR3Eu3YSbu3EjNdVhdKFEAgrQKi5nWDTOur3PkCp/xKZU4co9J5blid/o7GqRlCWS5T7rxDdsp1JQobQNBJ776Z09YqvYPE2a267WNhjI9jpUULTywiEILHvbooXzvolEvMsYFowRGznXhoeenLFcm+ly+cpnjtFdMdtNd5gdOtO1Ps+wsgPvztvlwE9Gid55wMk735oRtPXNaw8ihd7CG3omlpUNZ3kPQ8T27kXZ3zU136d4/ejPA+vXMLNjFMZvEplaAC3kHvHbjwvX/EYHZU0pDTu2h9AKThwxMa5hoEvpf/6L0tF53oDKaFSURw7MZMUIyV8/4USv/qLUVL1OutadT73+/X8wX/Lcvykjef5smh37AvwC5+IcP89AdJpSSYr6dq4mE2roEPbTINoQ+IxLocpkqdeNGISYFj1oaERFQlCREkzwiV5mqy6kTlhhZvPkOvJkOs5hhGOEWxuJ9bZTXTTdqxEqmbtMCMxjK6dxDbtoDI6QObMEdInDuDkxm8ZRalVzgk65E6+RXjjlhpPRguFafngx8gd30L+9DEqo0Mo2/Z7oFkWWiCEEYmgR2MYkRj5syexh99eXZeVY5M7epBgy7pqCEsIgRFL0PrhTzL++svkTx/zuxVMPCxC09DDEcKdm4nt3Ed4/cZqHZiTHkNWKgSaW5c/J9dl9JUfEFy3HrNuisMtNEFs5z4CLe3kjh+ieOGs78l6HpplYdTVE+7YSHTbLgIt66plFaX+XgKNzTNqIRcLYZi+kIAQVZkvYZhogeDEvwBWqmnm+EIQ3rQVIxrHs8vISgVZKU+155mUo5PSr3m6RX6MS0X2rQNEu3cTaG6rYfGZ8TrM+NzlKTMo5krhZMbJnz5G5sBPsMdG3rbXZC4MjXhc7fdobNAJBsFxFG8cmJ3RePqsQyYjqU/6v610RnLq7Oz1agcO2/zFF/L8838SJxgU3HW7xRf/RwPZnMS2/ZKIYNAPkw4MSX7rd8fZtNHgd38zgbZAtwMTk3rRgkJxTh7jiupB4rFbu5eESHFSvomHi4lFp9ZNq9iIjoHNTcjFaTpGKEy4bSPRDVsJNrdjBGevCxYTGnKBhlaaGlqp2347o4deJn3izWX3r1xJrHqJRP7UURJ77qjZ0Qoh0EMhErffS3zvnb6iiZTViwm+KJ8QAuVJ36t6mxlBgOyxg8R23EawvbPmuxuJJI2PfZDUg4/7fesq5ar4sRGL+4u+NpWQ9wp5hp79Okaijqb3/aPrI8gMXmXkxWdpet9HpnWFFwjhl3WkHnyc+vse8e8JCiF8VmC1xyH+Ilu8eJbRF79H60c/hZZYuhHUQmGaHv8ZrIZmNCuAZgXRAhZCM/ygwcSzIISYKgivHqzR+OhTVe3VSZFxpSTSrlT/eYUCIy8+S6X/yrKv12rCSY8y8O0v0fTE04TWb1r0fZ/xOSGwkimSdz9IdOtOhn/wHfKnjr19BJUXgfG05EyPw7bN/vMzMOBx/uLshu38BZe+AY/gBIHl5OmZpJhJuC785z/KkssrPv3LURobdHTd7zwBvrdo24rXD9r8x/+a4cc/rXD/PQGGRySxmMDz5t5s6BiEiFCmQL+6iJzokufh+jJqE9EzB5sL8iRxvZ4ubSdpb5jKDWrCq5kBwus6iW/eRbSzGyOSmEOOTyEd239PN2o2aYH6JloefppIx2YGXvo2TmZ1y3lW3QjKconh575Fy4d/AauhaaI6wjd0QlDTTmg2KPX2ZZF6hTxDz32L1qd/HrO+cYbklR4Kz9rnbxJKKdzMOEPPfp382RME29aj7AriOuXOsscOolkWDY88hRYM1c5LCIQ2t1FTUlK81MPgM1/FzWXwigXMRHLJcxC6QbC90xd7XuqxQoBuzEofmDLsICsVjHCE1d+LLgNCI9C6jtj2PRixxMoMKTTMZIrmp34W5bkUzpxYkXFvBUgJv/6vx/js/+UbJ89VjM4hgTYyJvngx4cwDP8JqlQUpdLcxqpQVPzRn2b52reK3H9PgO4tJrGooGL7jNEDh23eOmZXu9W/+nqF+x7vR2iC8fTc6R6BQMcgrzJ409rEengIBAYG3oSQtovDiOynS9tFnWhgUK3kxk5g1TUQ69pOYts+AqkWn4R1zWZKKQVSUhq6SvbsW+TOHcNMpKjr3k+0cyv6RH9JIQSaYRDfshszGqf32b/DHh9ewfkuDatuBAHK/b30f/0LND76fsIbtyxb+eTtiHLvJfq+9gUaH32KcOfmRTMqpetQPH+GkR99j0r/VcAPaTnp8esKifqDS9IHX8PJjJN66AmCrR01+qZzwSsVyR49wOgrP8DL+wlwe3SYYGv79c1nDTUwYgnq73uE2M596JEpDUilFLJUxCsVUa4zq7LGpOesmSZaKIxmBWZqSIYjND7yFJX+q9fdWPlWQiaryGQX5hgoxaw9A+eD68GFSy4XLi0s8+W6MDi88PiTneV1odcwQh0qCDSChGs8vjJFtInXVwJaIEi4tZPEtr1E1m/BjCZmbfOmlMIt5ile6SF98gDF/ktVrdHK2BCFy2exEini2/ZSt+P2at5QCI1QayctD3+I3n/4wpKbJq8UbhlrUxnope9rf0t441biu/ZiNbb4Iq/WNDkwKZGug3JsZLmMW8hR7u9dVjds5bmUr16uYabKSmVGN+ubgUr/Ffr+/m+IbN5GfOd+rMZm9HCkGvZEKZTn+vMrFagM9JE9fojSpR5kZcqP8Qo5CudOVh8mJWW1UemSISWFsycp9/US2bKd+I69mKlG9FDYV7oRGsrzkJUyXiFH6coFMm+9SbnvSg0Zo3j+TI18mj00MOviPAOeS6X/KnIWTdOVgnScRTUOtkeHZ2i5OuOjyz6vkpLKUP+MjYWbzy14rJlM0fLBj/vpg4njpetSutRD9q0DVAb78Cplv3vEbJdZ+B6fMAy0QBCroZn47tuJbNpSUwNmNbYQ3baL9Ju12pUaOhEtQVHmql7IrQ1BXKvHUTYlNfP6WiKIJYLkZYZbTUNV4lGhRIgIBma1fVJR5dCFTkKkyKip59DEL2q/Lmg6gboGYpu2E996G8GGFoQx0+sDhfQ87LEhsmePku15y+duzFIKoTyXytggw69+j8ypgzTd8zjxbXvRJsKk0c5txDbtIHPq4PXNfZkQ8y1IQohVeioERjSGEU/44bjJkKj0kLY9sfDmcYv5dx6bTWgY8QRGLIEeDCI0HaUkynHxykXcbMavr7zZ0DTMeNLPSQaCBEI6n/7FINnRPP/zz3opZ97ZdZ23AjQrQMuHfo7o9j3VRUm6DmM/foHxV19cNslACwRpfuqjxHfvZ3IRVUqRP32M/r//65o2Wym9jT2hhzldeYM+p+e6v9ONhiFM7o18iDF3kOPlV2a83x24mwZjHQeKz81qJJcKDd9rW6jx7eLG0tip3U2TaOeYfK0a4owQ4079MWwqnJBvUFA5LAJs1+4gLuo5IV9nQF1e0rn0UIRQcwd12/cT6dhSFc+fzevzSgVK/ZdJn3yT/JWeJa9HmhWg9ZGPULfzziqvIXfuGFee+dsb1stQKTXn7uCW8QRroXDzWdz8CtWUaIJAIjBnSE9JhZ0to+ZJUt80KImbGcfNjK/2TGohJU56FCft7zytpMYH9zYyOib5G1m4GXy0dz3CnZuJbt1ZszAVek4x9uMXrmvxkJUy6QOvEt22u6pnK4TATCTRgqEa7VhH2ZRkjop8Z7ReKqsCpRX0ajusbjzl0Oucue6xJJJRNUCDaCUhUgypXhSKEkVGVD8tYgN7tQcpkcciSIAQJfKk1eL6AArdwIwnSWzZQ2zzLkJN66p6odOhlJpQkkmTPXuE7Nm3KA9d9SXwlvO97Aojb/6QaOc2zKjf/iuQakEPhnHzNz/8fosawZVFqCHMe/7kZwg1zB4rt7MVXv7Ms2R6lh/ierchl5P8h/+SoVhQVQHiNdxACI3o9j0wLWespEf2yBsrsnt2cxncQg7LmlKh0QLBGY2Vs3KE14vfrTIV3+64ZB/nMieQXH9ESUOn0WhnzF16emYuDKlePOkypgZRE+Faicd5eYKQHiNOkhhJQFGhRI88Spn5UggCzQoQad9EYtteop3d6MHwnLk+5doU+y6ROX2Y3PnjfvRtBcpn7PQo5eGrGJG4z4gPR5ddSnW9eFcYQQDN1NAtfUqce/L/hMCzPcRabTfgpyAnL9EE2WsGdN1/73vPl/3PzPGbmEhnolS11A/UDIH4NSwCWjCIlWqsWai8QsGv6VsBKM+rCXtOvFpNk8X1BhLalIEccftmhA9Tehu2KgGCmJ4k7Q1Tknnq9RYsEWTM66eiSkS0BCEtRs4bI6k3Y6sS494QES1BXE+R98bJytoNqSWCJPRGLBHEUTYZb4iKmumNWiJIUm/GEBZFmSMvZ0ZUBIImYz2W8FnUrnIYci/PGsLUMIjpScJaHB0dW1XIeiOU1ZR6koFJTK8nrjcQ1ZJ4ukuH2c3kxRt0L09clylEtDriWj2a0CnJHGlveNaNhYszK9OzSI4j3is0iFZCRHCwGVUDFJg/pBvp6KLloZ8hkGqe0TYJJmtIJwriz58kfeog5aGrK17PpzwXJ5uu/q0ZqyP5B+8SI1geK/GT3/4+wVQYKxEkUBcksameDU9sRphr+pbgyzr9/MfCPP5oiOYmjXJJcabH5avfKPL8i+Wq0dq8yeDP/rC+Wgf1o1cqfPbfpSlXaq2aYcBffT7Fcy+Uef2Aza/+UpTb91o4ruLZ50r8t8/nKL8taxNWB5phol0jSC4dG7Vc4tO141sB9GtKa6Rt+wQbICSipIx1BLUoMS3J0dJLlNypBVcgaLe2ojNBdtCSVFSZIecSTeZ6giLCuDfI0dJL1OnNbLC2k5PjxLUUpghwyT5Oo9FOQPMLrg8Vnycv/UUypbeyNXgnhjBxlY0hArjK5nT5Dca8Ka8rptWzI3QfIRGhokoIBBlvGI1rf+OCuN5ATEsS1ZMoFGOFATxVawR1THYE76XeaMFRNgpJQIRwlM3J8quMeX5tckSvY4O1g4AIYwqLmJ7EEFNezbg3WDWCAo1Oayft1jY85aKQWCJI2hviVPl1KqrWixNoqDm8VJsyfWpp8nLBxjZCLR01r03yQpTnUR7qJXP6CLlzx7BzN7bvpJwWwVDSW7W61HeFEVSuZPx07Y45tauZjkc2oa0ZQSwTPvMv43z06TDf+E6R7z3vkqrX2L/XonuryQ9+NGUEr/Z5/KvfTdPRrvPZzyRoa9Fn9aKFgPXtBh94MsTTHwwzNubx/RdKtLXqWJaYIVe1hkXg2hp3w5wRrlwuguvWo0ei1b+VUrjZTLUTwKB7kUH3Eg3GOm4LPTznBCN6gqOll7FEkD2hh3GNFo6WXiJltNFp7Sas+RJ/IS1Gr32Gc95h9oYeocPq5kT5VWxZZn/4vdTrreRlmpCI0R28h4LM0FM5iK3KWCLEtuCddAfv4kDxOSrKLw3YHNiHJQIcLb1MTo5jCotN1m1YonbzoJCcrRxAoLEteCeNRsdsXwYPhwH3Ar3OaUoyj0QS05LsDN3PBmsnY6VBQJH1RjhaepmIlmBf+L30Oee4WDlW5ZpO9zBbzE46rV1csI8y4FxAIUnojWwP3sumwB5OlV+vGr0AYXbr91BUObJqjLzKUKFEhfKKhaOV8r2+Qu950icPUOq/NL/I+Qpi+rMrHXvVRLbfFUZwDfMjHNa4fZ/FT9+s8G9/L0NxojDYNP12MdNDoqWy4sBhm7PnBL/2q/NrlgoBd90R4Df+9Th//61idRzTeMdKwt4wSMeeUS5iRKJYjS3X3T7JSCRJ3vnANcQxRan3gt/ubNprUnnzFhKUZJ6sN4opAtiqRMYbpiAzWF4IAVUPSSEZ8a5SlFly3jgxPcmY24dAo6JKBDTfcDUa7VgiyLHKK1XP0FZlrtgnuS30CPV6C/3ueSJaHQm9kavOWUa9vonPlbjsnKTBWDfrXBXSl9GbB8NubShy1CuR9UaIaHUIBGrifx7uRAmDL2LtzkK00TFoMzeTl2l67TO42BPn6KXJvUqTsZ7z4kg1zCsQhImSECnatI1IJalQokyRgsrWGEYXZ9GMVD/X51AZH/ZzfeeOUxkbusmemMAITHE03GIeea1C+U3CmhFcA4Wi5M2DNh//R2F++zcSfOVrBc5dcCkUFY5zfcm7sz1+o9HphnTNC1w6ZKVMZXiwVmJP10je9SDl3ou4uWUwqYXAqm+g8YmnCVwjaOAVC7502hLhKXfCS1F4ysNR/kI/6d1MFn0rFO7Eex4OrnKQSHQ0lJIIfIMc1xvQhc6GwA6kmto5mSKAQCOg+QtpUItgCJOsV5tL9Jmfy3/gDCwSegMxPUVABNGFQVRPLqukUBcmES2BQtIdvJvpg8T1BiwRxBCBqhGsUOKw9wohESFCnKioIyriREmQEPW0i00oFGVKFFWOAllyKs24GpqTHCOdCrmeo6RPHqRw+SxeZXVYvkLTKA32Iie8v8rY4KrNZc0IrgHHgf/8x1lGxzw+8dEIn/xYmAOHbL74lQLffa5E5Tpyd+msJLfGHr1+KEXu5BHiu/cjqiw6QWj9Rlqe/vmqcpDvuc1zvSd0Xs1EHdHte0jsvRszmaohSCjpkTn0+rJIN6rm3Oqav6/9pKr565qJAqALvfrOpGEEn8zS75ynIDPT3hMzwoQSOc8c5kdYxNgRup+wFiPtDVKSeWxZwVMu+jKWToHPIHXx/Os9rXQt542R9UbwpslAKiRZxqpdIYQSVUWYsIgTJkpYRAkRIyLi1NMMAs7Jo1xUp2adQ/rkQdIn3pyFBHVzoaTHyIEXmYrxrx5bbuWMoJjUlqx9WSnmpg8uZsxrlNaVVMvahd00aIIawpWamPPNOI9a/rUZH5f84X/P8df/X4GHHwjysY+E+dx/TLJ1s8kf/El22eHLd5qWwWqidOUC+dPHiO3c6wuX46u/hDdupb21g/LVyxSvXMAZH0WWisgJUovQdTQriBGJYCYbCLasI9DchhYM+kbxGvHj4oWzjL/2oxtKipiJ2R/ckszjKYdzlcOU5Ezmo5w4zlUVJB5BUVsGZWJNiE0vHe3WNmJakrdKLzLuDVUNbEJPEdWWrocrkVRUiZLMcbL0GnIWD3W+Ug0/7OpRIE9FlalQxKGCJyRC+cIABibzqcbcqGL0ZWGCibrauG4jqAcN6nc00bi3leTWBsLNEfSQiXIldq5CcSBPumeU9JkRxs+OYqcXLqs2QiYNe1tovrOd5JYUgfowQkB5tMjYqREGfnqF0aMDeJVbJ7EUaY3RfFc7jftaiXUkMMIWXsWlcDXL8OF+Bl67Qu7K4gtBo+1xktsakJ5i9Ngg5RE/vGFGLVru6aDlng4SnUmMiIW0XQoDecZPDTN0oI+RowPLerY8CcMjkq9+o8hzL5T4s8+l+PmfjfAnn8/NKyC8hpsDZduMvPBdjHgdoY6N13RdCRPZ3E24a5u/y/e8ar5rUi90eqePGWNP1MPkzxxn6PvfXB1Volkw7PayztpKi7GRC/bRGk/PL5eooIC8TFORRZrMDfQ756s5uZTRhimW2Mp9AgERQuJRkJnqeaNaHTG9flbpP6Wkb4i1SDVfOB2ushlxr7LO7CKup2qYrQINU1jYaub6KKreX5QYdSREiqhIYGBhYKCAMgXSaoQ8aUbUytUpvhuwfCOoCRr3ttD9v+wjtasZMzKz5gSA22D9E5txSy49Xz3G0c+/PvcCLSC1s5mdv3I7DXta0INGzZjxziSN+9voeno7A2/0cvx/vkn2/Ooqqxghg84PdLPlYzuJtMXR9NpFJrm1gXUPb6TwiRznvnacc984iVtceDfWck8H+3/9AaTj8fq//yGXnztH3ZYUe/7Z3TTubfNrHqehbksDbQ9sILntIiO/vbS2Uom44P1PhDh52mF0VOJ5ipYWnYYGjaERD3mNJ6vrEAoJdM0vhQgFBbat1sguNwFOepT+r3+RxkfeR7R7T1XhZRJ+z0XDvzGLhFISZ2yE9IHXyBz+KfIaPVUdg4TegC5M6vRGNDSSejMSD1fZ5GQaV92YepeMN8Rl+yQdVjdJo5msN+YTRrQYhjA5XPohjqpgqzKX7ZNsCe5nT/g9pN1BAlqEiBav5h4nERQRIloCQ5hEtDoMDJqN9ZRUfqIOcBSFZNTrp8ncQHfwbkbdfiwtSL3egi3LsxrWiiqR8UZoNjv9XJ0sYAiTK/YpyqqAQnHZPkFcT7I79CDj3gAlWcAUFlEtSdob4mzlQNV4Glh0abuIiyQBQlj4eVCbMhVKjKsRsmqUrBrHpoxNeUWK/t9tWJYRFLqg8/3b2P3pOwmm/PCDUgrPdrFzNtL2QIARNDCjAYQu0EyNkbfm8VAEtN67nts/8yChJr9WSDqS8lixajTMiEWgPoQZteh4ZBOxjgQH/tNLjB6/PnbccmGETXZ/+k42Pb0D3dJRSmHnKlTSJTzbQzN0AokgVjxAtC3Ork/fRXRdgrf+9Kc4hcXVdwlDI1gfJrG5nrv+zSMkuupRUlEeL+FVfJFkI2RgxgIITTB8uH/JXmAwKPin/zhGY4NGvqCQUhEOaaSzkv/zP6SZXif7O78Z5879AcJhQfc2E9eFv/uLBoplxas/rfCfPrdCUndrmBNuZoyB73yFyMm3SOy/h2BrO3o4uqhOHzDBDvQ8vGIBZ2yY3KmjFM4cx0nP3tctIEJsCdyBNhGCLcgMdUYTdUYTEklP+SBjXj8lmasSXySSgsxgS9+z8ZRLXqZxlYOjKuS8dNWbKstiNd+nUBRkplovJ5FcqBwh6w3TbHSS1JsAX0S6376AOy2HdtU5g6MqtJpdNBodZOUYp8qv025uqRJ0ABqMdXRY3dW/y6rIOmsr4Buyo6WXcFSFAecCGhrNZidt1mZKMsf5ylsIISaK4Wsh8ThTPsB6q0Cd3kRcr6ckaz3qiipytPQyzUYnDcY6UkYbrnLIeCMMOhdrvEcTi3bRhUCQJ82gusy4Gqak8pQprYg+6RqWI6AtoO2BDdz52fdgxQIIBHauQt8rF7n60kXyV7M4BRuhCQKJINGOBE372wgkQ7zxey9iZ2bfMSY2p7j/958g0ubT7nOX0pz+4hGGDvZRHvN3pqFUmMbb29j287cRW+/3UEufHuEnn32OQv/SxG9Tu5p5+A8/gBE2qWTK/P/svXeQZded3/c5N72cX+c4OeeAnAgSRCJIMa68q7TetezaUllll0trVVmyvVUub5XXXqpsLSXVarWRXFLiMoAAAwASGQNgBpPzdJjpHF7ONxz/cXt6ptHdr7un0wCc79TMdLjhvBvO9/zS9/f6f/8imcuLl00TimDbb+1j9+8cQagCx3QYfKOXaz+4QK4nhVmsoXp1Qh0Rup7eQvezW9H9Bo5pc/GvT3H+Px7HseZftW3+6i4O/g8PA9D30iX8zSGSe5tJXxyn98WLpC9PUMu6riBvzEt4Q5ymQ21c+tvTZC4tLaFBCGhtUdm6WSMWURAKTKYczp03Gf9YS5nHHvbQ1Dh3beXQsM1b71anj/m5J7yUK5I337lXFb9qUFQ8yUY8Ta14mlox4knUKZFzRdWmdR+dagWnXMLMZTHTE9QmxqhNjlFLTa5x7O8eFgsvfo6qn0XDwMakTJGyLFKQWXKkKcosFiYWCyRDLQKKbiB0Y7oBLnN59ZYIq5Bd9wScm1hRAW1v3Mfu3zniEqAQlMYKfPT/vM3QW/2zBKiLQ3lSF8a58co1hKrgmHNfEKEp7PgH+6cJMNeX4b1//QrZqzNXpoXBHIWhHKkL4zzwvz9JuCtGdGuSrb+xl5P/5l2kvXaugMjmOFu+tgehCqQj6fnxBc78yfszXJ2OWSV1foz05QlKowV2/+4RVENl85d3MfrBIOMfDS3qXB1PbkIogr6XL3PmT45R/VhctTScJ3V+nP6XL99ffvAVAAAgAElEQVRREo6UbhH84NDCD+zrby2O0KSEn7+2/rLaquGjceuDqLqHyd6PKGeWEC8RCg2bjuCNNJIbvkJ2aO6Mu5vbxtp34Ys2Mdl7gmphjbplOzaUagSJUb7cx8Tgy66FJWbk3d3656aO3T3c9ahQ5rj9K4IiQogYEREnJhpJilYUBBYmBZmjQIacTJOTKSqUcLAXlxGrKHjjTYS27MHf0oURTaL5gq47fbkkKKHnO/+GyujA8o6zBlgyCbY9uoFwd3Rac/Pcnx5n8I2+ugsR6ci6agCxrUmajrS5x6xaXPqbU2SvzTOJSMhemeTSX5/i4P/0CKqu0vHkRq794Dy53rWLD3Y9vRVv3O26nr+R4eJfnpw31icth54fXKDpSDtNR9rQQwYbX9jO5LlR13W8ABRDZfLMKGe+9f4sApxxntXIQv2EQ9W9NGw+iuGLUJwcWBIJCqEQ69xLpHUbjm3VJUHdG6Tz8AsY/iiKqjNw8uWVGP6i0LD1AVp2PEa1lCY/1otVcV1w956GTzokRXIUZY5RBlCkgo5BUEQJixghogREmFY20iZc13GFEj3OOcZkffJRvX6Shx8ntuc+VG9gTgHtZY1cyhmNgOtDzPpKzvH1nZa6LIQlkaDq1Wi5vwOhKUgpSV8aZ/D13uW9bQIaD7diRFzdwtJogaG3+xc85sh7NygO5Qh1RjEiXprv71gzEjQiXpoOuaQtHcng633T2ZvzwSzWuP7zK9Nk33SkHV/Cvyg3rmM69L54iWr609G+5tMIx7aoFtOoho9KYWndSHR/BMMfoZQaQjpLj/PUimkcu0Y1NzGt9XkPnzbI6Sa7VVlmUg5j4MVPiKAIExUNJEQzQREhQH0lJ9Xjo/XJrxDeutcttVlB8lsKfARJKi0ElQgq+oLb99sXycuVn+OXRIJG0CCyJTHdCHH0g0FqueXFe4QiSO5pnl6FpC9NYC7imOXJErneNKHOKIqqkNjZiGKoi7Kslotga2g6eceuWkyeG12UFZa6MI5ZqGGEPBhhD9EtiUWRoFU23YSXe7hrYddKXHvzr1ANL9X8ElyhQtC07SHCLVu58sv/iFleemLRxNX3yY1cxa6WsM31d0GvFHzbtpP4/LN1tyldukjqFz/9VBekqmgYeFzSE8Ep5ZgIXvxouLWBbhG+SUFmKddrpSQEiUOPuQS4Tl0bAMIiwS7tPoIisqAFKqXExmTIXppY+GKxJBLUQ57pbFAkpC+OL3sAQhEE2yPT3+f60nPW4MyCdGOHbVNavsGOCOoakaA34ccIuqnpds2mMLi4icvMVylPlNx4qiIIdUfhjYX3s0om5cniwhvew7rCLOeWTGKKohFu3oKqGXfsjnJsk0p29I72vZuh+vx42trrblMbG103S2a1oeNhq7Ifvwhi4MXAiyrcPmYmNUxqU0kyKXIyTVkWqVGmxvxGhCfWSGz30RkEKKXEzKcpD1+nPDqAVSpMFdUvz/1Yy87tEVFQ2KjuJCgi1KiQtseoyDKtajc2NuP2AJowCIsYfhFm1Onnun2FgszMebzlYkkk6Il6Zyi4lMeXPzFrPh3Ne2sYtVx10de+mr216vVEvAh1bV4GI+yBqesgbVcUYDGwTRurNJWqLcAb89XfYQpmqXbnqjv3cFfDE0rgDSUxK0vLbr6HTz80dJpEu1uPiUlWTpCTKfIyQ1kWKE8pxiwFwQ3b0QLh6e8d2yJ7/jjjH7xGLT3BWkSSvSJIWIljUuWs+R6TU8X9MaUBG4uL9nEADLxs0nbToLQzZPetWknIkkhQMWauHuwVsLoUTZmh8rOUY96ebeoeZ21IUNFuSU1J6cbsFgPpyBllEYtu4/RrQICK7sEXaSIQb8cIRBFCxaqVqGTHyI/fSvaYD7ovTLhlC95wIyCpZMfIDV9xf7mAZ0HVvYSaNuKPt6OoGtVCmtzwZdeqq6Os7w010LDlPoRy+2skSV0/TWFsfteN7gvhj7XiizQRbt6MqruF1237Po89oz+gJH39NPmPH0sImrY+iCfUMOPHtWKascvv4NgLizEomoE/3kYw2YXuDeBYFqXMMIWxXsxKgXtpNesPkyoXnOOUZJ4SeSzMZSWHCFXD39p929wlyV85w/CvfrDiTXPrwcCDjoe0M0ZK3hL2cLBRb+v9WKPCNesMUb2RjdousubEnN05loslkaC8bbIXQswgxTuFbdozJnnNs/ghqbdta9fsNSMLu2a72U9TGVWqZ3HXQagK6m3EZ1fvJTEARNq207bnKXzRZtdNI27llUnpUM1PcuPEi2QGL8y5f7h1G52HvoA31DAdr0ZKyrkxhs+9Nq2hORd8kSY6j3yJYEM3ijIl1iwdzHKeobOv1iUUzRsk3r0f3RN0x6y4rqpKfqIuCbbvf4ZYpxuTURQVhIJq+EhsODRzQymp5MZnkyCCUPNmIi3bEIoyldygUJy8wfi192EBEvRFW2g/8Azhps0I9bb3TTpUchMMnfkF6etnFmwz9ImHcHVVpW0jVFeAW1qu0IfQ3Hsp56jlFaoywxO0WvOOhcmw7Fux4wlFxYg1TBsLdqXM5Mm31pQAwXWHKqhUKM0gdVtaaMKHijZt9dWokpZjtChdBESYrFxa0tlisCQSrGYr7g2fmsd9ycCSC7M/DrtszVBP8cR9N/NhF8Tt7sRqpjyrTnG1UM1UkJaD0FUUTWCEvZTHFnYNq4aKPhVLRDItAvBrDwnecAOlzDDFietUcmM4toU33EC8ax/eSBMdh16gnBmlWpyZdOKPtdJ99Mt4gglK6SHS109TK2Ux/FFiHbvpPPTCvAkAujdE931fIdi4kVopS6rvJOXsKJrHR6RlGx0HnqubaFKcvM75l7+Johl4AnG6jn4Zbyi54McdOf8GEz0nAIi0bKV51+OY5Rw3jr84ZYXdQiU/R9xdOvS++z1UzUA1fDRsuY+mbQ8veF4Ab7iRTQ/9fXyxFsrZMdI3zlLNT6IaXsJNm4m0bqX7vq8iVJ3JnuN8mi1CX2eSzv/6CcZfOUPTcwdAVRj48zfxtkZpeuEQtfEc/f/+NapDUxmJiiB232aST+3B15HAqVrkTvYx+uOPqI6sfLxKQSFIBJMaZRaeXwy8qGhT5DLHAkYING9g+lurkKEytrha5ZWE2z/EuU3s233GalTwizAGXsrceg+qsoyG7krVrcLjuCQSrOWqVFJl/E1BEBDfnmT47f5lDUA6kmxvmsimBADhDTGEIhYmNOFuexOFgZxrVa4ByhNFqrkqvoQfxdAIdUTIXl14hWKEvXiT7kMoHUmub311T+8W5EaucvEX36JaTGPXZma2ZQbOs+Xxf4I33EAg2TmTBIWgaccj0wR49Y2/oJq/tSib7D3Oxod/k2Cya87SgfiGAwQbNmBVi/S++7dT7lP3uRu/+gGdh75ActPhecctHRuz7Mby7FoZx1qcFF45OwJTWuqGPwJS4lg1CuO91EqLE1m3ayX3WpUyU4X5C88OQii07nkSX6yVwngfPe98Z8b1Gr9yjOadj9G257O073ua4kQ/ldzyk9/uVghNxdeVpPHpfWQ+7KHhs3vo/r3PURlKk3rjIk1fOEjDU3sY+HM3e63h8/to/82HyJ3uZ+T7H6CFfSSf3E1gWyvX/s8fUZtY2biugY/96iOMyyEuOifmJrbb0KFspkl0cM5+nyxzzEdTlu9NuI1s117NyaSKSY2ACKOgTIuTF2WOJjoJKdEZcnMevKs6niX1GKnlq2SuTE5nbzYdbXeTRJYB6UjGTwxPq73EtiZvZaDWQaA5RLg7hkDgWM6iC89XAsXBHMXBHEhQPSoN+5oXlZTTsK8ZzeeuO6qZyixFnF9XSMeilB6cRYAAxdQApakCd08oMeN3hi9CuHkLAONX3psxoQNUCynGL7875zkVzUOsbScIQWbgPPnRa9xOJHatxOilt7DXYZJYLXgjTYSbt+LYJiMXXp91vRyrytjldyhlhjECUeLd+9dppGsHxdDIfNDD8PeOMfHLc3hbY4z+4EOGvvsu+QuDBDY2us2HkyFav3YfmQ976Pnjlxl76SRD33mX/j95Bf+GRhKP7VjxsQkEOh5UtEWVnTvSwUeAgAjPvYEE57aY83rVlFZkibIs4hV+wuKWIZN1JpFAt7qTsEjgwUdcNJFU2zAx5+ywsRJYEgnaFYvhd667rkAhiG1roOPJTdP+8TvF6PFBSqMu8/uSfjoXOqaAtsc2TFuk1UyZkfduLGsMS4FVthj4Ve90XLD10e4ZZR5zwRPz0fnUZsANSI++f4PKOhS/K14vRlMzRlPL9F+9odFtDbHuEKi6F90XxgjE8AQTeIKJ6RowRZ3puPBGGtF0H7ZZoTg59/0vpgZxrNlEpnsDeEJxpLQpjPfPqWhUyY3fUd3e3QpftAndF8SqFCiMX59zG6tapDDWB0Aw2Y2iLlzE/ElHZcrdaaaL2JUalZEMSLDzZRSvAQKC21rR40GKV0bwNETwtsXxtsWx8mWkaRHc2bbupRoWptuSCWPO30vHwSzcctuqXv+6jNnCZMJx3bABcWvezMoUWTlORCQ4qD/OIf0z7NMfwUeAnJOiKFfnXVyybNrAG71s/OJ2oluTqIbKrt8+hFmsMfhGH3Zl7pWF0BR0vw6KmLOfYOFGlv6fX2XHPzyA0BS2fGMPmaspxo4PzipCF4qg4UArW39jD4qmIB2H67+4uuhavZXCjdeu0f3sViKb4vgbg+z5p0c48UdvU5mcbc1oPp3tv7mP+I5GhBBUMxWu/fDCmlmutyN06AjxZ56fSgRwYWVzDP27/xcrvT7uWaGoBBIdxLv24o+1YfgjqLrXFfNV1FnkdxOax49QVaxKYd7YnWPVsGplN3nlNiiagar73IlhHqKTUmKW81MZp6uP1Y6+Gb4IQig4tok1X0mGlNTKOUCiewMomrGobNNPKqSUOKY7b0lHIk175pwj3H/0WABFV+n47cfdxpu3QxWAQKiKm1izTtAWUF2RjkVlbAh/q9uLUguE0IMRzPzq1N/Vw4B9lbQzRkbe8kbYmFyxTrFT8xAQEQIihIMkL9NctU/dHSUSANVUmbP//kOO/i+PY0S8eOI+Dv+LR+l4YiPD790g359xu0ioCp6Il0BbiOiWJMk9TfT86AJXvnt2zuNe/S/nSO5ppuFgC76GAEf/1RP0/PACw+/0Uxx2rcRAc5CWBzvZ+MUdeBN+pJSkzo9z+W/PzBtDVD0qnqgPza+7NYl+Hd2vE92SQGguEaiGSssDnQSaQphlE6tsYpXc/6uZypzkXh4rcu5Pj3PkXz6GHjRoe6QbT9RHzw8vMHlulFquiubXiW5O0P3sVloe7ELRFOyaxZXvnSF1Ye1jLULT8HZ1I7SZfRoX24JnVcakarTsfJzmnY+jaAa1QopSeohaKYdtVpCOTWLDgTmJSAgVECCdeTMZ3S4Kc2T4iamsSimRcr6Jq77m7UpjtdfkNxOEpJR1U+2lY4O87Rp92jHrUsy+Nk7VQjoON/70lxSvzRYmsIvVFemYcLve5s2vBQKBgqjTh86Dl6TSgsShyjxuQ8eheOOKWyyvG2iBCP72TWQvHF/2uJeKGpU53Zs5meIj83ViSiOG8FKVZdLOGLX5PtMK4I76CY68f4OT33yXvb93P96ED82n0/poNy0PdrrlA/aUir2qoBqqGy+TU7V886AyWeLE//0Wh//nx0jsbMQb97HjHx1gy9d2T9cDKrqK5tenC/YzlyY4/kdv1c3M7HhyE3v+26MouopQBEIVU/8r0+NRvRo7f/sQ0nbc1aAtp0S/Jef/0wmufm9u4h56q5/Tf3KMPf/0KEbYQ3JfM/EdDVhlC8d2EIpA82qoU2IAdtXi2g8vcPk7Z+ZMvV5tKD4fnvaOFRXKXS7CjRtp2fUZhKIyfPZVxi6/i1UruxqaUiIUjWBD15wk6FhVpHQQio6izL0KFooypyXpOBaObaLqXhRt7ri2QKBoc7uWPomwqkWklCiKiqp55rWeNcMHQmBbNZw70DL9NKLUN4ZdNtFCPoqXl96zc3EQdIntBKdchBoaAkFEJNipHJl34aKgEBRR/ASpUCYr58/YL1y/QnlsEH9rN4qmkTj4CMUbV7EKi0vIWgtUKTPiLC/hcim4IxKUtpxyQWbZ9pv7aTzYih40EJqCNhfRSVcYuzRSv+A515vmvX/1Ctt+cx8dn9mEJ+q9VVJwG2q5KoNv9HHxLz+iMFDfDap6NFfpRp2bgG8m+SiaAnOM/XY1m1n72g69P75IcTjPjn94gMSuJhRDxfh4/aR05eCufO8s/T+7Mq/beL6xrRT0hka0SHRFj7lchFu2oWgGpdQgoxffwqrOXNAIRUH3zi0IXC1mcCwTzeNH94fdrMuPQfcE0DyBWQXzdq2MWcmjeQPzujsVTccIrPb1kqxVGUIlP45jVVENH95wkuLk7E4DiqrjizYBUM1PfKq0SJeDUu846Xcu0/j8AaxChcL5QaRtowa9+LsbyZ3qozK4/HCCLnQiIo6XgGsBCoFX+vGKhZMFTar0OufqllM41Qpj7/6Mjmd/C9UXwNfYTuuTX2b4Vz/CnEfm7NOOOyJBcP3nk2fHOPa/vUa4O0pyTzPRLQl8yQCKR8UxbWrZKoXBHOmL42R7UosSiy6NFDj5zXfp+eFFGg+1Et/RgC/hR0rXWkxfGmfsxDC53tSilFoGX+8lc2Xyjn1NCxG3tCWjxwZIXxgnvqORxsOthLtjaH4du2xRGMoxcWqEidMjS5KZG/hlD+mpGky7bK2IOo9v0+a7JAHmFhTNteBsszKn1RFq3Ig3PHftXSU3RrWQIpjsIN61j/zotZnuSyGIduxCKOqsTDizUqQ4OYAv2kKsfSfjV9+bpUoTbt46K5a40nAsE6ScskhX1+ospYYopYcJNnQR7z5AKT08y90bSHQQatyEdGzSN87d6z04BWna3Piz13GqJi1fvW/GotouVSlcWol6O8lV5yw3uIqfEA1KKx1soUyBtByf1xKUOFRkiZQcJc/C8b3i9SuMvP4jmh59Hs0fIrRpN0Y0Serk2xT6L2HmM6veDFcgVq010lJxxyR4E3bFIn1xgvTF5RXN3w5pOWSvTi6q9m4hVFLlNSlKr+WqjBy7wcixlclSrUyWqUyu3LiFYeDt7Fqx460USukhkBJ/vJVI81Zyo9eQ0kZVDYKNG2jf/wzzrWAcq8b41WP4Y80kuvdTzU8w2XcSx6qhqDrR9h0kNx2ZO64nHSaufUCsYzeBRAft+59h5PzrWNUSQlEIJNpp2/cUErnw+kkoCFWfzrQTiuoSr+OwkJVXyY1hWzV0X4iGzfcxevEtNxFFCBRFw6qV6tQfClcxZirWJ4RAUTVsocwp92abFUYuvM6G6Ddo3HI/dq3CZN8JbLOKEAr+eBsdB55FNbykb5wlN3J5oU/+iUZ1OEPP//UTSj1ujC93so+eP8pj5dz3bvTHJ1B8xnQijJUt0f/vXmX0xY/wNIQRmoJVrFIdzmCm6i+WFws51ROwQomSk6dJ7SQrU1x0juMsUCe4aAhBvvcCQtNoeuR5NK8fT6KZlie/gplLUZ0cpZoaw8ylsWuVOybEQt9F7MrsREEVjS3aPqqywpDdQ5X1FQ1ZNgnewycDaiiM0dRyV8UDATID5yhsPESwoZuND/0GpcwIzhQpeAJxciNXyAxeoHnHo3PuP9l7gkCig+TGQ7Tvf5bGrQ9h1Ypohh/N42f86vsEkp0EYm2z9i1M9DN09lXa9n6Ohs33EevYRa2UQ9UMdF+Y3PBlChPXSW6cXTBvBGI0brkfzRucVm7xBOMgBA2bjxJq6MaxTGy7Sjk9zPiVY3Mm71Ry40z2nqBx64M073iMWNde7EoJoWqouofrx39E+vqZWzsIheSGgwSSnaiaB0XT8UaaAIEnlGTD/V/HNivYVg2rWmLi6vszVGcyA+cZOPkybXs/T9u+p2jc9iBmOY+i6XgCcQCyQ5e5cfzHn6oayblgl6pkT9ySpKuN56mN3/JWlXrnSF5zJJUbk1RurL7r0MKaoZyyEvC3byS+9wGMSAI9kkD1uKpbN+cFI5JAD8cJblhu3aOk56+/SXkOEvSJIE1KJwBjzgBVeY8E72ENYDQ1oYbnKaJdR5jlPD1vf4em7Y8QadmCL9w4rRc6dOYXjF89RiDRQWLDgTktOseqcuP4jyinh4h378cbSqJ7w1RyYwye+jmp/lN0HfkS3lBy1v7SsRm9+CbVQoqGTUfwx1ox/BG3yP7a+4xfeY9Yxx5iHbtnuVN1b4jExkNuEsnHxuMJxPAEbhUB5wO9jF/7YHZqPa426uCpn1ErZoh17sUbSqCFfNi1MpX85LQizU0IoRBu2UasY+es8wohCDVtnP6ZbVbJDV+eQYLSsRm7/C7l9DDJTUcINnTj8UexbZP8WA/pG2dJ9X2EVa3fJPoeVh82JjmZon4u79Lga2wnsv1g3cXwSiyU63nRdQx0PORlmpJc/+4pol7yhRBi+pfC48G/eyeK79ZLL2s1SmfO4ZTvaWDe7Uh84UtEHnpkzgfcymYZ/JN/s251guBO7orumU7JdxzLdQNKiVBUVN2LY5t1XYOKZkxngt6+raJ5XDehWZ23c7ui6lPxSYF0bGyr6p5b1VA1L45dm3FuMSV6vZhiY+nY2LWF3pGZ43dLOyw3ZvixKfBmDeXCJ5bTZSZznlFRUTRj6ppLHNtatPTbWiC4/yBNf/+36m6T/+g4Y9/7DqxyDGu9oKAiECtWI5c4+CgtT3xpRY5VD1JKev76jymPzg4PxUUTh/QnmHCGOWm9sSaxQSnlvC/qoi1BxeMhdP9R9OZmFI+BMAzsXI5qX/89ErzLITwevB2d6z2MupDSmZcopGPPyhqdYyscqzqnOsx8P5+xjW3OWRQubQvLnu2SktJZxJiWgvnH/3HYZoWV6CizOHK+h/XETV3NlYK0LezqGtxzyby1u25DYBNd3B3lR4smQTufZ/yvvoPiMVDDIRr/yT9azXFNI3j4IN5tW5n49nen5bPuYWnQ4wm0ROKuiwfewz3cgwsNHRtr1a2i7KVTFAfnb/O1cpDUMnPHTUsyT16mCYoIfhGmKNe3RnHxMUEpsbNZbMAuFlc9hRYAIfDv3YMWj627Lt8nGUZLG2pgdVP97+Ee7uHOoKKxRdmHhsZF5wQmrktaoOAniIW5YhmUdqWIXVlJD8YdjAGLHussu/X72aju4pJ1YlUVYRbCXZ0YowaDGK0tOJV7BbvLgX/z5ntW4D3cw10KFY24aALkDNk0Lz7uUz/HqBzgvPPBgq2UPikQCAoywxXrFFu0/ezXH2HUuUHBydaNfRZkFouVj1mvDQkqCnpjA96NG1AjYaRpUhscptLbi6zMjIEITcNob0NvapzaPoLwGMSee2ZG7ZM5Ok7h/Q+WNSahqiiGBy0aRY1GUf0BVJ8foetu3y3hJklIy8apVnDKZexiATubxcrnkKY51Vn67g3KK4EgnvaO9R7G+kJREJqG6g+gxWJo0RhqIIDQDYSuu/qhZg2nUsXKZbBSKaxsBqdW+3QlXKgqQlVR/X7UcBQtHEYNBlE8HldPVtMB6coHmjWcahW7VMIu5F0vULGAtG3XC3QvNLFiUFAwMCiQ+1gt4E3NULHYPuOfCIREjB3aEQzhxYMXr/ATURNIFep9ypPWm9PdJ1YSq06CwuMh8uQThB+8D+H1Tlsk0rap9l8n9YMfUxu89cH0pkYa//E/QBgGQtcQuoaqBQk//MCM45YvXqbwwYdLU7QQAi0SxdvVhaezG09bO3qyAUXX3QlCCFCU2a5XKaf/SsdxibFmYqYmMcdGqQ4PUxsZojYy7CYJrYfKhhBugbVhoMUT6IkkerIBT3s7WjxRf1fDIHT4PpzSyqbFO7UKhdOnkdV1sOSFQA0E8W3ahLd7I56OTvREwu1MMbXAmf4Lt91fG2wHu1ikOnCdcl8v5auXMScmPpETv9A09MYmvF3deNo6MFpb0aMxhKa5i4Obz/s8z7x0HHAcpOPgVMqYkxPURkaojQxTGx6iNjaKNM1Pt7KMUPBt2ozR5NZj1oekcr2f6o25W1XNe4qldbX7RENDn+556JL++r5Xq0uCqkr0qScJP/YI1f7r5N85hjU+jhIIEDi4n+DB/SR/42uM/umfY2dcuZ/ayCjD3/z/QICnu4vkN76KlUoz+h//fMaq3FnCiyc0DW/3BoL7D+LbvAU1GJrVSaH+AW5Xdp+C14cWDuPr3uBOENUqTrFI5XofpcuXqPT3YaVTqzY5CE1DDYfRojH0WByjuRmjuRU9kUDxehGGZ9GfUfX5iH/2qRUfo5lJU752FWstSVAIPG1tBA8eJrB9J2o4srjrcFPtRVVBd/su6okEgT17sYtFKr095D54n0rPVaR194tKa4kEgR27CO7Z5y70fL4ZXcUXxO3XYwqqz4cei+PbtAUcB6dSwc7nKPdco3ztCpXr17Fzd48Q84pAUQju3UfyC38PJRCo+xxJKSldukDx3NyC+/PBwcGkho8ABl6slUj9vYuRkRO8U3tpyfutVtxwVUnQ095G6KEHMEdGGf+rb2Onb+naVXv7ULxe/Lt3EjxyiOwvXnV/YdvT9WpaPO6uRm0bK5VeumtKUfB2dRN95HG8Gzeher2rkmAjFAXV53MniWSS4L4DWNks1Rv95E+eoNLfh1NcfjBab2wisGMnRksrekMjamDKfevx3Iv5CYHR2ETkkcfwb9+JGgyuyDURiooWChPcux//1u2Ur10l8+avqPT13pXWj97QSPj+Bwns3I0Wja5KmywhBKiq+/wFAhhNzYSP3o+ZSZN57RXyx5cRpriboCgE9x8g+fyXUPz++gToOBTPn2Pih99f8kLAxiQv0yRFKzvVIww7fVQo48GLADx4iYvmRccESzJfV0R7veFgU7mLxreqJOjfuxvF46F46swMAgRwKhXKFy/j36sivFkAACAASURBVLUD76aN5F5/A1lbuRWQ4vcTffgxwvc/gOKvv4JbaQhVRY/H0eNx/Dt3k3rpx2TfeWuZBxUE9+wl/tQzKzPITxGE4SF8+AiRRx5Hi8VW7V4rXi/+nbvwdHWRP/Ye2XfexC6srKzVnUIJBIgcvZ/Q0fvRYvG1XRQJ4bpdo1Gs7No3aF0VKArBAwdJPv9FFN8iCPD0KSZ+/APswtIVUGxsBuQ1wiJBVCSJqQ0zfh8XTcRpWvTxrtqn6ZUXFj8AIVA9PlSvD6EZrh7tCjw/lclRpHX3W7WrR4KqgtHaCoB/106M5tk38WbpgxrwIwxjxUhQjURIPv9FArv3ui/oOlpJ0rYo961FXc6vJ9RQmPjnnyF04NCtuO4qQkzFGqOf+SyetnYmXvwh5vjYqp5zgQHhaW0j/vRz+DZvWbfnXUpJZWCA6tDgmp97xaEohA4cIvH8F103cj0CtG0KJ08w8eKPcEp3bt1MyGHOOu/SJjYSEnFUVAQKBh4c7OmyicXAWqS6jNB0fM0dRLbtx9fUgR6Konr9i1MjWgTmU4y527BqJCg0HcXjcd1Urc3oTQ1zbudUKlMxlpV5cdVAkIYvfhn/zt3uEdeTAKWkcu0a5sTad5H/dYAWT9Dw5a/i27RlVdx+8+HmpOjbtp2mcITx7393yYkQKzQQ/Dt3kXzh76FFouvrEpeS0vmzK55cteaYJsAXFiZAyyJ//AMmX35xRVSzUnKMlBxHQaCiERBhDilPMCFHuOScWHQXicVIrGnBCI0PfJ7I9gMouqvcspLPTz05Th8BGtUORuz+Rdc/CgQ+gvhFEAuLoswuaWFQD6tnCU5llGFZpH70E0pnz827qbTtFYmZoShEP/NZ/Dt2LfqGytsyP2/GeKSUbibgTbeAy6bTWXSLflhsm8KZk8ja3aPH+GmBFovT8JWv49u0+BrI6RfTcdxY882vb95TsbR7LITAaGmh4avfYOy736Y2OLtJ7apBCIIHDpF49gvLin/OmKxufn17Itgij+uUSktOCLnrcJMAn3sB1eevu4CWlkXu2LukfvYSTnUlu21IHCQONSqyhIU5ZQlWV6yVkuYP0fbU1wl2bbv13K8h/CLEZnUvBSdLVZanayPlbf/eDgWVDepOOtQtqGhIJGVZ5Jp9mjFn+e/cqpGgNE3sbA5UFcXjwc6sftaYf+s2QocOL2gV3CQ+K52iOjTopnqPj2FlszilItK03AlBCISuowaDaJHIdNmBlkhiJJIogcD0MWdNFlJipiYpX16hnmxSYk5MULx4fkm7KR4v3o5ONyV+HjimSfV6P465smRtF4srGue9CSUQIPnFLy+KAG9O8k6lQnXgBtWBG9SGBzFTKexS0U22EgqKx0CLxNAbG/F2dOJp70CLxRckRCEERlMzjV/5BqN/8xdrZvUHdu0m8ewX0EKhRe8zTXhSYhfy7jXI57ELeZxKebrUQWg6it+PGgyihsJo0ShaKOyWDzH7WZdSUrp8CSuzfgLsy4aiEDp02F1ULECAjmmSe+ctUq/8bFUXuDfJb0UhBMmjn3EJ8LZ5sp7lthpQUIkrjURpIKokUFApOBmGnD6ycmZv2qTSQpe6HQWVksyhCI2giLBdO0zVLJOVy2trtaqJMeWLFwkc3Id/907yx97HKSzN2pO2jZQSoaoIXa8r1SZ0nciDj6B4ffNuA24Quzo4QO7dt930/XxuwazTGTEfIVC8PjcrrrkF36bNeDq7MJINM7I0pZQUz5x2J9oVQuHMKYrnl7baNppbafnHv40anH+ydMolxv/uP6PLIi07Ioxdy1MYX4F0ZClXvJxA6DrxJ5/Cv237oqwUO5+jcOoj8ieOY05OIOus2msjI3DpAlnVzQj1bd5C+L4H8LS11y0vEEJgtLaS+MKXGP/ut7GLq5ss4+3eQOL5Ly6NAB0Hc3yM8rWrFC+exxwfxymXXCtmvvpHRUHxeFCmyoE8nV14uzdgNLWgx+PT10Sapuvx+ASUjswJRSF0+CiJZ59H9dbvDOKYNTJv/IrML191Fw2rCBubnExTpbxihfLeZAuR7QdmEqDjUE2NURy4Rnl0ALtUmLN7yVJRTc8fKxdC0Kluc7tkTF3vqNJAUrZxwfyACXmrdrxR6UAVGv3WRfrtiygodGs7aVM20a5uIWvdxSRYunCRyrUevBs3EH/hebK/egM7mwVHIjQNJeDH09FOpbcfa2J2Z3o7m8UpV1BjUfw7tlE6fwFp2dNWze0TmqetHW9X14JB7PyH75P6xU+x83fYx0pKd/IolzAnximePY3i92M0NOLbsg3/9h0YDY1Iy6Jw9vTKptHfdDEvZbiWufAQpLtd01YfX/3DffzkD05z9uWV7fMlBPhjBuWsiWPf+TUJ7N5L6Oj9C1v7jk35yhVSv/gp1YEbS7sPto2VSZP/8H1KF84Tvu9+Ig8/Wld/VQiBf+s2oo9/hsmf/mTVlGbUUNi1AKOxhTfGneBqI8Nk332b0qUL7vu3WDgOTrmMUy5jpVNU+vvIvv0mWjiC0dJKYOcuvBs34ZRKVHp67vATrTMUlfCRoySeeR5lgRIqp1Yl/dqrZN98fU2yHm0sLjknkLBCkmmCYPd2NN+t59gxTVIn32Lyozcx82ub2StQSMlRJu1hbGyiIkmD0s4mbQ8Zc3y6XjIgwjjSZsIZmo4h9ljnSBgtREQCD75laasujgQVhcD+vegNDQivx5Va8nkBiD77NHYm4xbO5vIUPjg+vUJyCkVS3/8hia99meDhg/h3bsdKpZG2jeLxoEYjCN1g5Fv/YU4StNIZSqfPEH70YRJf/wqhgSGkaSIMnWpvP+mfvDy9rW/TZoThmfcjuIWsF5l86ccrrkXqlEpU+vuo9PeRefNXeDu70OMJaiPD8+5j+BSkA2b1k6dCcifwxwy+8K/38fIfniU7dGcPrBZPEHviybquXZha7Jz4kMmXXlxWxh6AXSyQ/uWr1MZGSX7hS6j1ElCEIHT0PspXL1O6dHFZ550Tqkr0sSfwdHQuzgoul8m//x6Zt95YVO1a+OB9BDZtJXf6OMVL87jdHQcrk8bKpCldOIcaiaB6fTjroQq0XCgK4aP3kXj6ufoEKCVOtUrqlZ+Re+ftWQ2WVxMrlfwBbumWv7Vrhrcqc/E4o+/8dF1KGVLOKKett6f1QAe4yhZ1H+3qJsIiTkqOAq62qo09Q0SgSomckyKuNOERvmV1p18UCQpVJXj4EJ7O9ps/mfaF+zZv4qbZbOfyFE+enuEmqA0NM/Znf0HgwH78O7ejNSQRmoZTKlO5cpXylWuYwyNzn1hKMj97BTuXx79vD3pjg/sS5nIzLDmhaRjNLQsGsrNvvb7qYtyyWqV85fKC65JDzzay4+E4J38+zuVjGUpZ826WIF02Et1B2vfFUfU7zOIUgsgDD6E3Ni1Ys1U4fZLJF3+4cvdaSopnzyAdSeNXvzFv4bQQAsXjJfaZz1EZGMBZYbeot6OT0OGji0pksPI5Jl9+kcJHJxYt9+ZtbSe4ez+VwRsUWVzs2c5ml2Zd3i1QFMJH7yf+zHMonvkJUEqJU6mQ+ulPyH1w7K7Qkr2pJ3oziWaxbkuhqBjR5PRntctF0qfeXRcClEjGnYEZgtgShzFngE51K17h/9jHmv0Zy7KARhsa+rLGsigSlKbJ2H/6S1AWePkkc8Zc7Fye3BtvkX/7XbgZW7mp02jVf6iccpnsL18n9+bb04F5HGdGfFBoGlq0fpG0NTlBbT3ruT6G3ESNls0Bdj0SJz1S5cxrE5x6dZKBiwWs9bYOBTRsDtF5II7hU0kNlOh7f4JqYeYKWPeqdByI07AxiJQwejnHwKk0tnlr/A2bQ7Rsj7Djsy0Yfo3DX+umlHEf/Px4hVM/urGod1hvaCSwd9+CslXVoUE3ZX0VFjuli+dJ/+pVEk8/d+s5/hiEEHg6uwjs2EX+w2Mrdm435v2wK4m3wDVwyiUmX/wRhVMf3ZWqNuuOmwT49HNuDHAeTF/Ln/yY/Inj6yqULxCEiBIXTQREBA19SnmlRFZOkpKjC8utCYHq9U9/axayVCbnMUBWEZKpxMQ5SjkcbCRu4gwwo6vGx2FhugsCoSwrfLnomOCys6BuJkncSfBcyvpBaKG47ow6sMvlu6pU4fybKfrP5Nl0MMKBzzdw9IvNPPT1VgYuFvjop+NceCvF5GBl7ecwAZsfauSR391KtWiie1WirX56j43zo399ikrOvQ+hJi/P/P5u2vfGyY6UEQqEG31cfmOEV//4AuWsu93WR5to2xMluTGIogladkSoVdxnYLJv8a7K0MHDaJFo3W1ktUr6lZ+tnmXiOOQ/OIZ/yzZ8W7bWdYuG73+A0oVzK5Yk42ltx79958JuUMch86vXKJw++WtBgNJ2ljYBKirh+x4g/vlnUH0LEGCxyMSLP6Bw6uS6iqfreNig7KBFdKEzO+TjCJu8zHBFniYj62QnC2bE0u1SYV2sQAsTC4uIiDNMH7ffwKCIoqDgF0EEAgMvqlDnJEMFBYlcdmbrXd1PcNEQLOgiEqp6y5K8CyAdKKRMTr0ywbk3Jom1eNn5cIy9TyZ55ve6+NzvdnLlWJoTPx2n91SOUnZt4hBCQNehBC/9H6cZPJNB0QRHfmMDD/zDTWw4muTCK8OohsKj/81W2vbEePEPTjFwyk2N33Bfkqf/xW4KE1Xe+NZlHFvy3l/1oCiCh357M0f/qw28/IdnSV93yc9x5KImMDUYIrB7z4IEULp8ifKVFSpJmQdOuUzmrdfxdnUjPHPHoMWUiot340aKZ04v/6RCEDxwEGEYdTe7KeCcO/buJ7LjxZ1A1qosmgVVlcj9DxJ/6pm6i2YpJXY+z8SP/o7iSie3LREaOtuUAzSJDiQOOdJUZBELEwUVD14CIkxESbBLHuWs8978JQMStz3YlDEo18myLcsiZVmgWe2iKPOk5CgSh4AI063uQCJpVTdiCC8KGh78gMQjfLfdakFQRLCxsJcpOP7pIEFHIqv1rTwtFkcNhu5KRQurJhnvL/PWYIWej3Lc96Vmjr7QxMFnGtn3VAOjPSXe/PYQJ346Rq28ypObhMuvj3D1rbHp9o3nXh7kyDe6adgU4sIrw4SbvGx7oplzPx3k6ptj09meF14ZZuujTex9voP3/6aXUrqGXXOwYdpFalVtzMrSXj7f5i0LZkM6lQq5D46tSZp+paeHSl8P/m075t1GqCqh/YfcAvJlEpIWjeLfsnVBTSWnXCL9y1fru4KFgre1ndCeAxgNTUjLpNzXQ/7cqbqTotB1fJ0bCWzdgZFsQEpJbXSYwoUzVIYGZn3GyJEHCG7fzfjPf4JTKRHatQ9v5wYUjxcrl6F4+QLFKxenSOzO4VSriyMpVSXywEPEn3rajQHOAykldjbL+A+/T+nCuXW3pptFF02inTIFrjiupXd7soyCip8QG5QdNIl2NondnJJvYTP7XkrHxsxnMKJuazXVFwChcHuf1rWASZUB+yrbtINs0w5So4LEwcCLQGHI6cWUlekSihoVqrJCh7qFkpOnRpWISBBVGqjKEmW5vOS3TwUJStvCymXw0D7vNmowSGDXbjLjY+v+YH8c/ohG1+4QR15oYvPhKIoKp16Z4OTPxwlEdY58oZEv//4m4m1efvatfmxr9cYvgZFLuRnvRaXgljVoHteSDia9+CI6Y1fzM8odHEsyeiXH1sebibT4KKVXwP2sqvi371gwI7Q2Nup2dlgDSLNG4fRJV66tzrg8HZ3oDQ2Yo6PLOp+nrcPtCblALLB47iy1BbQ7Q7v30/DMF1G9PqxiARwbX9dGAjt2uzWzc0Dx+Ul+5mnCB464bcPKZRDg79pE+OB9pN/6JZljb85YgOjROL7uzQR37iGweTtGssElZyHwdXYT3nOQzIfvMvGLF5dVb+dUKwu/z6pK5MGHiX/uaVfKcR7IKQGN8b/7z65HYZ3nCQ2dZqUDCVx2Ts2onbsJB5sCGS45H+FXg0REnBAxMszOtpe2TXlsAH/7RoQQaIEQejiGmV1end2dYMjpRbEU2tUteIQXgYZJlVH7Bj32OWwsMnKSgAiRcSbQhcEe7SEOGk9QkxV8IoiOwaDTs+wWS58OErQsaiMjC8qlRR54mEp/P5Weq2s4urmhaIJoo8HeJ5McfLqBli1BUkMV3vrbIU69MsFYX2maiD76+TjP/bNu7v97Tbz7X4ZJD6+wisTtkGCW7Vk/mzF2VbiulTnq/Zwpgla0lXE9Kx6P63pcsI/b8q2KpaB87Sp2sVA3TqmGQnjaOpZNgr4tWxeui6zVyJ84XtcSNhqaSH72WRTDYPKNX5A/eRy7VsVINpJ4/CmCO3bP3kkoxB56nMiRB6kM9DP5q59THR50G812dJF48hkST3weu1Qkd/KDGcQhNJX4Q09QHRlk+Lt/SXVk0C232rqThqeeJ3LoPgoXzlDuvbP3UUpZV/wAWBIBmhPjjH//e1R6rt3ReFYaGjoBwpQpzFJR+ThqVJh0RgkqUYIiQmau7aVDof8ysT33oxoe9ECEYNdW0qffXaVPMD8cbK47lxl2+gmIEAoqZYpTVp37DI07A9yMcCpS5bp9iQ51M4aI4OAw4vRz3b607LHcPUGy5UBKyr09dRNfhBCo4TCNX/26211iKQ1GVwF7Hk/wz//qAM/+XjfFrMV3/tfLfPMfneQX/+E6oz2lGZaYWXE4+8tJDJ+K7ln/W1acrGJVHSItvpm65wKi7X5s0yE/tjLZmZ7WtrpF6uAugsq9aztxWdks1eH560DBTULwT3V2uFMIjwdvZ9eC29XGx6gO1lfsD+7cixaNUbxykfSbr2FmUjilIpXrvUz8Ym4RaCPZQHj/YexSkbEXv0/p6iXsYgG7kKNw4QzjP/0hALEHH0P1Bz62t0CaNcZe+jtK16b2y+fIn/yAwoWzCE3Hv2Hzoq/FLEjpxrjmg6oSeegR4p/9/MIEODbK2He/fdcQILiZkSoaplycbmiNytQ+85cMlAauURrqm1biShx42C2bWCeYVMnICVJylLIsMF9818Hmmn2GD83XOGO9wwnzV5yzjq1Io90Vn1EFCgFvA02xnbQlD9AU20XQ28Dts2XI30ws1I3XiNAc30Mk0IZAIexvpSWxl4B36Teler2P2vhY3UwhIQRaPEHj136Dxq98HU9H57olyxg+hbOvTfJv/+kZ/ux/PM/xl8YoZa15PTCVos21D7NUS+tfp5QbLdN/YpLtT7YQa/dPJSZBw6YQmx5spPf9CUqpmSv0asFCUQXR1lvbL6ZxiNHShtDr1wHZuSxWKnXHn+eO4DhUr/ctmJnmae9Y0JVbD/pULLsepJSUr1yun/0sxDThFC6cnSVBWBsfpTY+22L1tnWihSJUBvqpjs0m/crgdaojQ+iJBjwts8MR5Rt9s44rbdv9mZSuJumdwnHmd6WqKtGHHyX+2acWTIKpDQ8z9t1vU73ef+djWQU4OFiY6MKDsoip2it8SOSM2rtZxzRrjL/7M6xCFiklnkQzrZ/7Gp5k80oOfVUgccjLNGPOAFk5gTNH3PNOsMLuUEFX8wN0Nh7Fdkxsx0RX3cLi3uE3GRg/jsShIbyFhth2ytUMYX8ziqJzffQ9GmM78BhhLKvMiSt/Q9WcO0YxF5xKhdx775D84pfrTppCCITXS/DgYfzbdlC6fIn88fep9Petuhbg7Tj58wmOvzS+aAmxoStF/vJfXqRaXH8SrJVsXv/WZb70B/v5xjeP0vf+BELAxvsbMEsWb/2HK1i1mSvXvuMTVIsWT//+7qmkG0kxVeWdP6uz8lZVjMbGBWNhVjaLPU88azVRHR52i6frkJzi96PFYphjd1ajqsViqH5/3W2kabqtnOotADUdLRxB1mpY2dlC19KysPKzS0uMBlecoDY2MufxnVoNMz2Jt70To7GJ0tWZSjlmamJOF620p0TqlTv3yEjHmdsSVBQiDzxE7MmnFrQAq4M3GP8v31swlroesLEokiNCkrhoYlTOb+l78RMXzdhY5GV9+bPSUD9Dr36flie+hB6OEejYTNeXfof02WPkr52jmh775OrA3gFWmAQlqVwvhfIYxcoEtm3iMUJs73iarqYHGMtcoGq6dVN+T5z+0ffoGX6DA5v/Pm3Jg5zv/zGKorF341eJBtsZTS+tY0Lx9EkCu3Yvqp5KCIEaDBI8cBD/zp1UBwYonPiQcs9VrExm1YPiC8mlGT6FYEwnNeRaVI4lqRRWlwAreZOBUymK6ZlWnGU6DJxOk7lN7mz4fIa//ecfsOf5dtp2R0HC6Z8McvalAdIDszNwx6/m+f7vn2DfCx00bwtTK9tkh+vr6ghNQ08mF7yXZjq1Li+tncviVCqowfndtYrHi55I3jEJ6smGuuLd4CaH1EbrFz3fLBGSjj2vEP1chHKTRJxabe53wnGma80UY7bFNe9+KwDpOMiPdz4RguC+A8QW4QKt9Pcx8Xffc4XT70JYmIw6N4gqSbYo+1AclZQcpUZ1WktUwyBImA3KDoIizJgzSIH6dbJCVamMDTD+/qs0PfIcmtePEYnT+ODTxPc/hJlLUU2NY+bS2LXKHavkZC59hF2qXyeroKJjTKng1EeVyopZf7djxRNjcqWZGUw1q8B45hKb2p5AVTyAe1FsxySV68G0yhQrkwghyBRuYOgBTLuModePA80Fp1ol9dOX0OMJjKbFmfdCCFSvD//mLfg2bnLbH129QvH0SarDQ+tWUrFhf4THf6uNf/d7a9ejbfh8lr/+72arnJRSNb7zz96f+UMJqetFXv+3iwtMSwf6P5yk/8PFZ6IJVUMLRxbczpqsnzSwWrDzeZxqtS4JCk1DC9cv8p9/Z4E+1c6pHpxyBStbf/UvbRscB6Go85Kq0GZ7UG6WW9xskD2L0BRl2vPi1NZYP9SxZxG3f9t2Es+9sKALtNJzjfHvf++ub3g9Iq8TlUmaRCc7lSOUyFOVZWxsFBR0YeAnhCZ08jLDNedsXaIIdm8jeehxtGAEPRhG8dwSDBBCoAfC6IEw/pbuZY1bSklpqI/yPCQoUGhSOmhVN+DFjyIW9gicN9+f1hNdSaw4CSpCI+hvIuJvw2uEUFWDkK8Zwcy+bFLaWLZb6Go7NVwxHWdKAcBB3GG4sjYyzPj3v0fDl7+2oM7kxyEUxe0ZmEgSPnyE6uAgpUsXKJ47gzk5uabu0mBMIxhfnibeJx1CU1EWSIoBN0llPWCXSwv3YBQCNRSam0AWAXUxi4BMqm6bMXC7hFi5LHoiOXdG61T7qI+jOjaMlBKjsWWqpmzmeRTDgx5LguPMGVNcVTjOjDiop7OL5AtfrttkWDoO5auXGf/+f8ZKr3Ec+Q5gYXLJOUlVqdAiuvATIqDcdp+k6zYddwa56pyhSP2wgCfeRKCrjtrRGqFF6WK7dniqSa6Dg4OKBkhsHARiOg5qYVKS+YVl4e4QK0qCquJha8dTNEa3kisOU6xMUDOLmMZst5ec+jP9/ccLNpdxjyp9vYz+9V+QeO4L+DZvvdUhfhGY3k7T8XZ24enoJPLIY1SuXaNw7gzlSxfdHoFLnNB0j0Kkob7ix61BQKK9fl/EXweofv+ikkpWsmfjUiBNc8GFkVgOCQqBFl44ccTO5xc+tpSUeq/i27CZ4I7d5M+dmuHmMpKNeBqbZu1WGbiOlU7hbe/E09xKdWhmXMr9eQu11KRbOrGGkI4zvQjRG5to+NJX0OLx+uU0lkX++IefCAK8CZMqV53TDNFLXDTiFyE0dGxsKrJEVk6QJbVoV+F6E6CGzv/P3ntH15ne952f5y23N1z0QgIkwF6GfWaoKZqmGY1GUySNiu1Im9hee23HsbNJ1om8OWd3kzjOxi22YzvKyjq2miWrTNeMRtMLh72BHBKs6P1e3H7vW5794wVAggDuvUQhwBG/5+AcAnzL87bn9/za99s0rhIfk4P0WhfIk2ODtgtTmpyzjqGhE1GqqVeaGbJ7OGMeWXiB4cnxLCCqwq3UR7dwrvc1eoYOYdkmIGmu3UtlaB6l0HNAYaCfge98k9DuOwjt/VhJgu0ZMa4qrnp9+DZtxrd+PUYsRurYUVLHj2IMDpQ9sTVvDfK//sVmyiJ9F6C5FHrOLK4463KHKMLscTVmKu2/IZASO1f63KV06oruW4TfcgJWqrz3JHXqOOEdt+Nfs4HKex4kcfQgVj6Hq7KK6D0PzihIbcRGiB98n6oHHqHmsc8w8vor5Pu6x5veV1F1/yMAxPe9tehiwtNg28iCgRaOUP3kZ3E1NJauBdB1Kh54iMJAP4W+6c3nyxUSSZoEaZlAyInS6qmORDmwzQJG+sYUkc3GQOQVfnwiQJYU7cYHZMdTZAXp5DqHbee5DNhdJJRR1mrbSapxLluLIE/GAhtBXfOjCIVUdhDLNsb/5qMytGpJVh92Nkv87TdIf3iK8N67CGy5DcXvn9NYhBCg6biqa6i4/0FCu28nffokyQP7yfd0lzSGqiaQtuT9H/SXpSG4clMQX/gjwWUwZwhNK8t2LGUlWznnninXVhaEKFkUA+UXnxSGBhn+2UtUP/I40XseILzzDqRtobjc5Hq7iX/wDhV33T91JymJ738X1eMlvGcv9Z//J5OMMarHh7RMRt98lcSxgzecYUXaNsKlU/nop/GsWl3Wdy2EQK+uoerxpxj87rdK5lKXI+R1yCddi7HTR0hdXBxjci3M9MzC3BouNHRG7QGyXIni2FioaAjEuHm36bcv0SBX0ai00m91kmfhazQWdJZNpLspmBnaGu5nwFsDKERDqxBCw14qGZLxRtjhZ39M4uB+QnvuwL9+I2ooNGfDLBQFLRwmdPte/Ju2kjndzth77zgiukUmgrHBAi/8+aWyjOCex2u5+0sNcxrfRwWKplFOXFwuIVm0LCEFBs51CCGue9oSqubk4UqOodxciSR54giFkUGCm7fhqnK4++qITQAAIABJREFUQzMXz5E6dQItFMLTuALjGsMgC3lGXn+Z/IUL1G65BzOoYNl58gP9pM+cJNfbA7aFS/Xhd1UihCCYD0D3EMYMYUeBQsAI4B00kBkPWU8didwA1zuxC00n+uAj+DduLsmoM2U/IfCsWk300ccY/vEPli6SsASwjTy2ceOYlWaCGM/4OTm+K8/clAYu4UZDn+RHtbGJ28M0q+vwiQB5udyNYKafU5eepbF6B3XRLRSMNAOj7Yylu9nQ/Bi27aya82aadG6YiTVNLh+fZESQ0iadG8EwFvhibYtCdxfDPT0k6t4lsHUb/s1b0aPROTczCyHQgkGCu2/Ht3Y9iQ/eJ7H//RnDU5mEyYUjY2UryefS5rw0sj4KKNexWNIMRzme6hwfpFBEeRd3PR6YtMn3dM3ILmOlEnR/469n3s0yCQwrrDzl4WTfiwymOqZtE3BXs67mPnTFg2cgxPn2t0mM7J+2naa4qBsM4fnJRWr0CqpqP8GBzu9gyesrfFCDQUddY46RncCW27DGxhh95aVl2xenoaOikSfHtROCgkqYSoIiTJ4ccTlMvqSc99LDxsLCwo130usDyJEhLCpxCy+GLFy1vYkiVFShLcqcuOB9gsOJcwwnpnMBHjzzjcl/9wwfpmf48OTvZ7pfnvy3YWY4eu47CzusKUO0KfT1MtrXy9h77+Bbu47A9p24G5tQvN45f1BqOEzFQw/jbWtj5KUXyHd3TZmcutpTfLd9+sQxGzrbU7zytc7rHstHCdI0kMjSdmAeDdfzRTnhSmmac9I8k5ZVpkLCjQmb580U6cIIeXPm3N9o5jL7Lv0dQU8NO5uenvU4hp3jYNf30BQXtzU8ga6Wl/u9FqX4ZJGyqIcoVJXQnR/DiI2S2PfekhNmz4Qm0UqDsopL9ml65aXJvyuotCmbaRJtkwK0CUY5ZR0o2Se41MjLHAY5/CKEhmuy4CVlx2lS2ogqdaSsBCCdqIGIIKU9vXhygfBznXSykgmShw6QOnYEV0Mjgc1b8G3YjFZRMZ6Puo72CiFACDyr26j9xS8z/OyPyJw+NecPK96fJ96/tGGLpYY0yvOGhb50r3E5+T5pzM3LkJZVVqhX0XUmCiUWE6OZy+y/3DXZqD0TJPZky1NxSEy7gJQLmyaRUiILBdKn2xGK4uhQFlkkKS4X0QcfxozFyHx4feQciw0VjQpRg5fANO7QatFAk1iDwDF+Om5CRGlVtnDCfq8srtGlQo4MKTtBVKmhQqlm0O4GYEyOYFBglboRBZW0PUZYqaRKaaBAjtwi5APh59wITkCaJvnOy+S7u4i9+Tre1a34N23Ft2Ytis83WSVaDoQQaJEKqp96miH5PccQloCiCseZEQJpSWxLLsdF6Q2HncuWtYhQvcVpxRYNQpRVvVmW3M8sKKc31XlHKcsGulU/NcE1DCbPkbdSgKAxvAkpJf3JM9jSxKX6qQ2uZSB5loKVptLXTNBd44wHyUDyLDnzxtPUFcOEp10Y6Cf+2quk20+ieNwobg/eteuKfr+K30/lp5/ASo6R71k+9GkqGj4RxMQgfpVQroZOo7IaBcEleYZL9mnceLlN/RgVogo/YZJMp8ZbLpDjChAu4Z7S1pGScQbtTpqUNbSpW5GqPd4rKOizLpKRMxfazBe3jODVsG3sdJr0ieNkTp9Cq4gS2Hob/s1bcdXVl20MJ3rDqh57kv7Y6Ky0TJ6Ayo5P1rDtoSqqm70oiiAxXODMvhj7ftTPcOcNZuBYZrBzWacZuoSiuuK/Vr3gxkDornEvbHZMqJTP1QiW0/5QjLHmWuiqh9XRO8mbKQZT5/DqIdqq7gEk8VwvmUKMiLeB1sq9DKcvgAVuLUjE24jPFcHvqiJVGFlWRnBCUilx6ABjb7852QNopQyGn/sxtV/6paLtE0II9Moqqh7/LAPf/XvM2PIwIAoqLtxkSE4hxQ4QJkIVOTJ02+cwMTAxGJUDNIrVBESYpFwe1zAbBuzLDNs9UwSCJZLz5kmkBtVKIzoucmQYtHu4aLXPObdeCkuvy7NMIU0TY2iQ2M9+St/X/8ekzIpdJmuMEAKtspKKBx9GzDCJuzwKn/29Nj7zf7QSqnYxeDFL79k0UsLdX2zkl/94E7Wrl8jDWSaQpoWZKr36K6bpt5hQfT6EXpoAwUrN0QhKiZUond/RK6Jlq6HkrQwFK4NPjwIQcFdhWBkkEp8emfxb1hgbZ3SC3sRJjvY+w6XRA0VDoUsBadvkLl2k/1t/x8gLz05rgjeGBhl+9kclm+OFELhXrqTyU0+gTJOEWho43YAKpjSmGIAapQkFlUHZO6UQJivTCBRczM6ZulxgY08xgBMokOOMeZiDhVf5wHiZA4WfcdY8siCSSbPhlidYBqxkktSRQ2ROt+Ndu47IXfc6EjkliiKEEPjWb8Db2jYtLLp6Z5itD1Txk7+6zL4f9pOKOcbV5VVYvT3ML/zfa9n7uXp+9F+Wj77ZjYa0TMx4DHddfdHt9MrKGzSiqVCDwaIkzeCEM80yDNnMO8uyKOFUfwDV5y9LScOwcmSMOAF3FUKoBN21pPIjaKqLkLuW0Uwnflcl6cIopn31JCWxFzh/txDInj/H4D98u+i15y5dZOTF56j+zNMoXt/sHqGi4N+0GXMszuhPXpxOzn2DIZFYmKjC6Z0DcOOlclwtYkj2TGXdGv+3uM56acXlQfP5UXSPE9lYIHm53EDPnNoxJLbTP3iDUkK3jOB1wM7lSB8/RvZcB+E77yJ89z0onuIVpULTCe7Y7ei9XVWGXbvKR3wgz/5nBiYNIEAha/PhezGOvzbCio3XTyL+UYI0DIzBQeS6DUXvsVYRRej6DeV2BccDVTzFjaCdz2OOlE8afi0KQ4NOU3ixKke3G726pkw5KUkiN0BtcC2q0Ah76olnu1GEStjbgDqm43NF6E+cWXZe30ywUsmyaPPS7SfRQmGin3ysuD6lEITvuBNzLM7YO2/BEvagWpjkSOMjiJcAKeLUiEb8hIjJIcbk1PfKJdzjhrO8xYoWjBBZv4PgqvW4KqrRvIEF1Ve98K0/JTtQXOh5OeBWOHQOsDMZYq+/yvCzP8bO5UoK+XpWNk8jQs6MGRh5e9Z9pZSkRm/spL7sICWFwYGiUi5CCLRQuCyi6YWGq66+ZHuGncvO2DBeLszREaewpggUtxt3Y2PZx0zk+vFoQTxakKC7mrFcH2O5fvyuivG/h0jm5yb9tGxh2yT27yPx/rtFycaFEKBqVNz3IP7NW27gAKfDwGBEDqChsVHdzUZlN6uVTdhY9MjzU4pKBAI/ISSyrNChv6mV5id/mdq7H8XX1IoeCCNU1dFbXYCfhYQbL41KKx4WJ0x9ywjOFbZN6uhhxt56o+RqUQ2FpoXsOvaPYVuStl0RVF0gFIccRFEFtat9rN4e5vBPBif/PvHz84Z8T3fJPKwWCqFHb3BIVFHwNDeX/OALfb3z8lCNWAw7Xbw0XCgK3tVtZZM+ZIw4ljSJ+puxpEGmECdrjGFLm6ivGQGkC3P3XpcrpGEQe+2npNtPFm09EUKg+nxUfvLTuFc238ARXgtJj32eBDGCRKgXLWi4GJDdDMqpVaxuvPhFGAuTlCweQndX1dPwic/jqW5AiPLFBcoasZSOzqNpLFhfX0BEWK/tJKgsTu7/Vjh0PrBtEgf24d96G+76IhRnQuCqqSXbcXbyT7rHIch++vfbuP3JWgYvZ5E2RGpcrN4eJpM0ad4conlzcHIfKeG1b3RPCZ9+1GEMD2GMDKM2rZh1G6FpeFtbyXaUp224ENAiEVy1xXOVUkqy5zrm1YRtJcYoDA2gV1UV3c7T3IxWEcUYKu3BmVaOnJGg2r+abCGOYecw7TymnafS10y6EJssipk/yuzdEOKG5IDsbJaRF55BDQbxtBTnNNYqKqh6/CkGvv33mKNLsyjIkua49R61YgVe4SchYwzK7mmKES485GWGUQYmCalnhKJSffsDuCJTxaqvjkg5JA22U/NQxsp74jhGOkG6s4NM7yUyPZfIjSyMWLEqtLL0BueKW0ZwnrBSKbLnzuKqqy/6QamB4JTfW3eG2bA3SiFrU9fqp671iqtvWRK3T2X7I9VT9pG25N3v983BCJYzuyytvMpskIZB9lwH7samovfXt3Y98TdemxSBXWx4W9eglmjNsNNphzloPpCS7Nmz+NdvLKpEoXh9BLZuI/baT0saXdMukDXHqA2sozt+ZLy5HTKFOLXBdfQmTk4WwWiKh4i3AU1xU+lbiUCh2r8aXfFg2jnGsn0Ydg6BQoWvCV314tejKEIn5KmjPrQR086Tzg+TMRxO0oC7Gp8eQVPcePQQmuKmIbSRvJkhb6YYy/WxmBbRjMcZfuaHTutEEfFtIQTuxiaqHn+Swe//A/aNVskYR44Ml+WZorckSZxj9rvjynyze2Ce6noCK6fqCdpGgXTXORLn28kN9TriyBKqbn+AyIadAMSOv8/I0XcRjGtIBiP4GlcTaF6Lq6LaOZ6UxE8dInXpDAv5/DS06y72ub7jzwNuj6CuQcXnF0gJ6ZRkeNAim5l+AyqrFCqrVRQV4jGbwT5rxiiizy+obVDxeATZjKSvxyKfW8ad41JijIw4IdEi1aLXhqqO/XSIjv3Xz2B/vSwyjqJ4ifuniDnzpy46pCTz4SlCt99RtCler6rGs6qVzOn2RR+ScLkIbL2t5D0r9PdRGJj/ajh76SJWJlPU6ApFIbBjJ8nDpbXyJDaxTBc+vYKRzBVqvuH0RfzuSmKZ7smiGK8eorVyL2LcI0jmB4l4G4l4G7GlyYeDr2Hk+lEUlZboHlyq84zShRHcmp/mip2ApDt+nMxYHBDUBtZSHWgFwJYWBStDY3jr+PGHSA4MYsvF5fIs9PUy/NyPqXn6i0VbbISi4Fu3gegnHmHkxeeQ+eXJ4iSxMcsoZPI3taJ6rnxHVi5D/zsvEj91cFo1rJm6UmhlF/Lkh/um/P/YmaPo4ShVOz9OxZbb0fwhGh56mp6X/4F051lA4MVR7cnK1GT1qtP/WB5VngA8YnFbVuY889U3qfzWvwmzeZsbtwcQgkJOsu/tHH/6H+Nk0s4Fazo88riPz38lQFW1ilAglZS88IM03/1Gilz2ygS9YbPOr/3LMK3rdDQNTBOOHcjzV388Rk/n8ivPvh5cq0CeTVpkk1cltsejDgtNjydNc1Zdr8lzaxqq348xtLDnXijkuzop9PbibZ1dk1K4XIRuv4Nsx5lFJ0P2rmrF07yq6DbStkkdP7ogYzEG+8l1XsK3fmNRb1iPVhL+2F2MvPRC0WIigJ6xE/SMnZjyt/7kafqTp6f8LZkf5IPOb5Yco2UbHO7+x5LbgeT8yLucH3m3jG0XF9mOs4z85EWqnngKxe2Z/d4KQWjXHqyxMWJvvlby3i5XCFXFW9M0GVGQ0mb0+PvEju+bceKR1oTKg5i1H9YYG6X/reeQlknljnvQgxEaHniKyz/+Ono8zzbtXlSh0m7sY0Q6C8KQiLJZv7PscWvMUYqs7OPPAYoCT/9SgO173PzFH47R8aGB2y1Y1aZh25C7ynO7+wEvv/PVCD95NsMrz2YwDMk9D3r5yv8WIp2S/OO3Ukgb6htVvvqfo6SSNn/w1RhDAxar2jR+41+F+d2vRvj93xmdYjCXDYRAC4dLlhbb6ell3IoKjesDbLmvkoo6D+cPjfHBM/1oukK00UOsL0chOz+raOeyJbkrFZcLPVpJ7tLFeZ1rseCogR/A09wyq/clhMC7ug3f+o2kTx5ftLEoXi/hu+6ZkQBhAlJKjNERMlflgOcDaZqkjh7Bt25DcXFeIQju2kP2/Lmy6PpuAVLHjqBHIlQ88Ako8m5JVSVy730Y8RipI4eWJdl2SSgqrkjlpLE30ynipw/PuvK2TXPCBqIUIYWQpsHQgdfwNbTgrW/GVVFD1a6Pk379TVzCjYKCS3gnI6QqKj4RwJZ2WW04YpHrN+dkBIWAYFghk5a0HyvQdcnEtuHk0QLjoWEA/AHBZ38xwIVzBn/zx2MkE85/dF402bbbzRNf8PPCj9JkUpKPP+yltl7lD//PGCeOOF7TuQ8NqmtVfvVfhNmwWefIgaVtXp0JitvtTM4lKqyM4alulhCw54k6Hv/dVXj8GppLwbYlB54bwBvU+JU/3cSrX+/kgx8PzGt8di5XWv1cUfG0rCJ59PCS9kUVQ/pUO6E77sSzsmXWbYTLRcUDD5Hv6Voc6iuhENy1B29rW8nnnTp6eEGLKbIdZ8n3dDskDUXovxSPl8pHH8dMJCj0dC/Y+T+ysCzi776NFqkguPv2WfsxhRDgdlP5yKOY8Ti5C9OVcpY7hBBoviu9x2Y6QSE2e/jH0al0rGAxIwhgZVLE2g/grVvhfCetm4gdeodT8f1oaAzZ09/FfvsSg3YPpfKH1UojjWpr0W3mgzmZWMuCl5/NoLsEf/S1Kn71d0Js3uZC16cukMIRhbZ1OpYJDz3m48kv+HnyC34eesyHxyNoXKnh9zsf9PbdbmwbNm9zTW73xOf91DdpBAKC+qZZ7LUQqKHQgjZ5Xg+8rW24m1YW3cZOpzBGp+Zpqlu8PPqbLZx8c5Q/+oXDnHrryoSZHC0w2pdj7R0V8x+gbZfMSwkh8K1bjxZZGvqxcmBnM4y99y62YczaWymEwFVXT+Wjj6MsAqm2b916Ku57oOi7JqXEHB0heejggp7bSqcYe++dkosURzm9mprPfQF3kYraW7gCmc8z+spLZM+eKdnzq4XCVH36SfTqmhs4wgWCmBrWtLJppDV7lMg2jUn7pOjuknNsurMD2yiMG9sg7toGBuxOeuwL4wK6VyCRxOwhBu0uBu3uoj9xe3ju11wG5pwTPPRBnt/9Z0M89jk/9z3s5TNfCnDiSJ7/+d8SfHjSuWBNF/h8gtVrdD77i9PZT86dKTBRlRiKKASCgk8/7Z8WaThzypix2AacfFb04UdRvT6SRw6Ru3wRK3FjyH1d9Q1UfOKTRRkopJTke3un0V+t3hamkLN4+a8uM9KTmyK2K20Y7c1Rt0DcobnOywR37SlevRoKE77rHkZeeB6KfBhLiXT7CfwbN+PfsnXWbSaor6RRYOTF58oioC4JIfCt30jV40+h+PzFvUDLIv7WGyWLU+aCzKl2Mh1n8a1bX5ylSAhc9Q3U/sKXGf3pT0ifPL64bDqqiqumFsXrJXfxwk0ZKrRSKYaf+xG1gX+Cq1glshC46uupevwpBv/hWwvzft1AiKtaHkppXNqFPBKJIgSq24Oiu4oSN1i5DIWxUbw1DnGDp6YRTh+aeWPJFFLwYjCZyp260JizEZQ2XOgw+fP/PMZ3vp5kx+0efuW3Q3z1D6L8i/9liNERG6MgSSZt3n8zx1//8di0fLK0ITHmTP6xEZvBfovf+80Rkonpq910avYVsOoP4F+/Ad+69RijI2TPdZA+1U5hoN+hklrgj1JoGt62tUQfeRRXbV3xSVFK0ieOIQtTq8rcfpVc0iKXniHJLpymectcmHHnuzqx0im0a9o0ppxSUQjtuh1zZITEB+8vS6VtWSgQe+2nuJtWOJqPs4UFVZXA9p2ooTCjr7zktCnMMcyr+HwEd+0hcs99aMHZ7x84k0rmzIekjh5eFENg57LEXnkJd119Sa/dUUaopPozn8O/aQtj779Dvqtr2ns4Vyh+P3pFFE/Lanzr1uOqryd38SK5y5du2sIRY3iYoR//gNov/RJ65ex9mUIIvG1riH7yMYaf/dGyrRidBjnu3Y1D9RSXAbOyqcn3WPX6Ud3eokZQ2hZW7gqxgx6aOZKVJ0uvfaFsaSQLc1EXVnM2gl6vIJt1dO+GBmxeeS5DfaPKr/x2iEBIYXTEJjZq0360wJbtbtxuQU/XlY9DUUHXxeTctO+tHLff7aFtvc7rL2en5Go9XkFJLlvhlPnr1TXo1TWE9tyBMTJMvqeb7KUL5Ds7sZJJh+bMnMOqWFVRfT5cdQ0Ed+3Gt34jittdUt260NdLeoay/VhfnmClTqTORTo+dTy+kMbKTUHO7rv+FoqZYAwNku/qQl1fnINTcbuJPvIoWmUViffedkK4czAeQtMmxWZL5iOvE4X+PkZ/8gLVT30OUUTLTygK3rY11NXVkTp6hOShAxgjI+UZAUVB9Qfwrl5N6I6P4WluKRkKklJiDA4w8vILi9qrmO/tYfTVl6l67AkUT+kyc8Xlxr95C76168h1XiJz+jTZ8x1YqSR2Pl/SQxS6jtB1FJcbNRjE3dCEe8UKXPUNuKqqEeME4o6o9IJc4pIi39XJ8PPPUv2Zp4sueoSiENy2AzMeJ/76q8ty0TgN0sbKJGHcOKluL6rXj5WdmXvVSMYnmXX0QBjNH8JIlMi1X2WsFNfMvLopOUa7+UHZw87IJJetM2Tl4njdczKCmg6/89UIY3Gbjg8LZDOS+kaNh5/wcfaUQSLu3LhcVvL3X0vy//xJlP/w3yr52YtZRoYsgmGFNet1Ok4X+MdvprFteOOVLPc85OV3fz/Cxq0uLnYYaLpgRYtGKKxMabsohslJXtNw1dSi19QS2L7TYfOPjWIMD2OMDGOMjmDGY1ipFFYm4/TITPTTKQLF5Ubx+dDCEfSqKly1dbjrG9Grq0BRy6IakoUC8Tdfd/TkrsH5Q3GSIwWe/ndreP3vuvEGNWxb0rozzN7P1ROI6hz+ycLwNzrVlfvxrVk7awXcBITuIrz3LvwbN5HtOEv24nmMgQHMVOrKCl8RCE1HcbtRvF5Unx/V70cNBlGDIUfVIOAn39vLyPPPLGyxjZSkTh5Hi1ZScf+DKEWqNIUQaMEQ4bvuIbhrD/muy+TG2y2M0RGsdNq5JiEQLt151tU1eJpW4GlucfI+5XAhjmsGDj/3DMbA/AqZSkJKkocOoIUjRD5+f0k9Q3Dug3C78batxdu2FlkoOO//6ChWKomZSiELBWciH+8ZVT0eFK/PeZbBIFqkwulTHF8MCOfAi3utS4TMqZPEwmEqH30Mobtmf/6qSuSeezHH4iQPfLDsw8DStinER/DWOTUMqseHHqqY1QiamRRWNo3qciM0Hf+KVrJ9l2c9vlBUNP9VC4cF+u4zMslZ68iCHGsmzMkIShuGhyzufcjL45/3o6pOWPPk0QLf/FqSeOzKxZ88UuDf/fMRPv+VIJ/5BT+h8arSSxcM3njZmnxvxuI2//HfjvL5Lwe4+wEvjz/tx7ahv9fkrZ/mMIw5vGDiCs+AcLlw1dbhqql1osu2DVI6cfHxn4lVj1AU5wOfmADHf78ejj1pWSQO7CPdfmLG/0/HTb73H87x9Ffb+PIfrkdzOZPL5o9Xkhkzee5PL9LZvnBKytmOs2TOdZSVTwJHo07bfTvBnbud+2Lb4/2GYsr9mdznqp+JCdJKZxZnorQsxt55E8XtJnzXPSUNgRAC1evFu2Yd3ra1zrVMPHfbGidmHR+7ooCilD/JS4mVTjPy/DNkzy1MS0RJ2Dbxt15HcbsI3XmX43mXKfYMjuqEu74Bd33DlfswfeMp+/y8IXngA2ehce99s74HQgjQXUQf/iRmPE727Ic3eJTXB2mZ5Ib7CMltzjfh8eGtbiQ3MHMVsW0a5IZ60UNO6iG8dhvxkwcwMzPPS65IFa5QdPL3q0OjyxlzMoKWBV//ywTf+dskuuaEQSwLchmbwjVhSynh1HGD//RvR/H4FFTVsT+FnCSXk1O+v9Fhm6/9WYJv/s8kuu5wCRYMSS4jFy7NMDFJj7O7LMYnLm2L1LEjxF59pWiYpPNkkr/+jZO07QzTuM6P5lIY7c1xZl+c4a7sgjbO27kcsZ+9gru+ATUUKn/SVNWSuolLAWkYxH72Ckib8F33OmG7Etc0aawnjNx8xyAlVirFyPPPkDp+9IZ6ArJQYPSVl7HzeSL33g9lXP9MEFctZm7hCqRpEn/zNbSKCgK3bS/alqL6A1R9+kkGvv13FPp6b/BIrw/Z/i5sI4/q8jgsQy3riLUfmLlZ3jTI9F4kuNqh7PNUN1B9x4MMvPMi9jVpBcXtpWr3fVOqT/OjM0eyJlyTxSx2uR7MOSdomZBKSMrliCsUoFAoPatb1vUddzlBSumEHg/uZ/SVl7CzpfNh6ZjBsVeHOfbq4pYBg5PvGHnpeafK0VtcB/FmgGMIf4o5Nkb0oUdQ/CUqNxfqvOPGzhgcYPi5H5M9d25JQmHSKBB/4zWM0VGiDz3s6CrCLaO2QLCzWUZffB4tFMazanVRQ6hXV1P1+JMMfOdbWHMVUb4ByA50YaYSqFEnn+xrWIUrUjlrv2Dq0hmqdt2H5gsgFIWKrXfiCkWJnT6EMTYKUqKHKqjYvIdA87rJeyQti0zvpRmPGRZVtKgb6LcvE7MHybOwdQPXi2VKGHmdsC2klEs6qUspMWMx4m+9TvLg/pIFB6om0FwK+cwNrKSTktSxIwhFEH34Uw7TzU0OaZok9u/DGB6i4sGHHeKCRe4ZlYUC6VMnif3sp2WpNizqWEyT1JFDFHp7qLj/QXzrNpRVMHML5cEcizP8zA+o/dKX0WtrixpCT8tqKh/9NEM/+kdkCQ3IpYKVz5K8cGqS9FrzBwiu2sDILEYwPzJA8sIpIpt2O2QMqkawdROBlnVYhTxIier2INQrpkRKSbbvMrmhmb1iFZUatYkqpZ6sTDEi+xmwukjK2LR+whuBm94IOmGL15GWhWdVK2rAP6UXZtHPLyVWKkm6/SSJ99+l0N9Xeieg5bYQD//aSn74h+fpP38DY+e2TfLQQYzRGNFPPIJnZbMT8ryZvQfbJnuug0J/H8E9dxLatdvxihbYGNqGQaG3h/g7b5E51T63KuPFgJQU+vsY/N538LatJXzXPXiaViA8RfgwF/L0loU5FnfaI5Yp49B8UOjvZ+iZH1D7hV9Ai8xOYCEUhcCWrZhjMWI/fXl5VoxKydjZY0Q27nL4bS+dJt01O/ujkPS4AAAgAElEQVSNtC2GD72Br3EV7oorqjZC1dC8M5sPu5Bj+OAbs+YE0zJBt3WeqFKLVwRZoYRoUFpJylEG7W6G7F5yMlVUDWMhcdMbQaQkd/kSua5OXHX1+Naux7d+A66aGhS3Z1EmeCklMp/HHIuTPnmc1MkTTi7gOkJi9Wv81Lf5pzTJ30jkLp6n/+//lsBt2wntuQO9qrqsvFpJjBcbyULBaRW4gVFCK5Ui/tpPSR09RHD7TgK3bXeM4TyuS9o2di5Hvqeb5KEDZE633zC5puuFNE0yH54ie74DT/MqAtu2421bg+oPLMyznTiPlEjDwM5myXV3kjnVTvZcB2Z8EajqlglyF84z8uLzVD35WVTf7CQWQtMI770bMx4n8cH7y3JRkB3oov/NZ8gN9ZEb7is5b+WH++l/8zkaHvgMWiA863skpcQu5Bh8/xWSl2YvEsqR4ZT5AR58RJVaapQVRJQqIqKKiFpNi7qBUXuAAbuLuD1IgQKLOZGIEjRBN19iTgiEqqJFK3E3NeGuq0evqUOvrEQNhsaFIgVCEcBVVY0zQcorlYSWhZVJU+jvI9/TTe7SJfLdndj5/JzyQXd/sYG7v9TAX/7KccaGlpYTVfF68axaja9tLZ7mVWiVlU7F4TVVoJOQ4zlbe7y6dtxQGKMjGMNDjoRQfx/5vr4l02BDCBSPB+/qNrxta/CsbHYaoGe7rokKYWmDZWNlMhR6e8h1XibbcYbCQP/yXNkXg6Kg+vx4mlvwtKxyevtqa51wqVBmf74weT8mqqalbWElkxhDgxT6+sj3dpPr6nLIKBZpotdravFv3FR0m8LAAJkPT92YnKwQ+DdvRa+sLLmpnc2SPHIIeW2l4E0MX+Nqqm9/EF/jKhRNmyJ9I02T7GAPw4feIHnhtFN1XSYECn4RpEpppEZpIigiKKhIbDIyxZDdzYDdTVKOzrmYRko56wrwpjaCE+ziRZnIx5vohaqheNxOH1swhOr3O31ubo+zSlaVyR4oaVlI08LOZbHTacxkwumpSqeRpuFMhvP86FZtC/GVP9zA1377JD1nZu7TueFQFITuQvX70KtqaLh3HdU7mlHcbtJDOXrf78bKOf1kdj7n9FimU1hjY5jJpCPbZJlLzhii+3RWPrAKzeMEOoSqInQd1R9Aq6xE8YfpOTCMkZUITXOet+F4rmY8jhEbxYzHnN65MujGvFVe6nY1YOUt+vb3YKSXSZj0aigKQtNQXC60cAQtHEYNhVF8PhTNaYhHUZxFn2Vh5/PYuSxWOo2VTGDG49jZLNIyF+T9nwmVm6qp2uiE3DJDGXre6cQ2F87A+usDNN01O89vLpaj641LC3rOjxoU3Y27qg5v3Qo0n9MTaGaS5AZ7xgV558eeo6ETFFFqlEYqlFoCIoRAYGJyzHiHUTk3fc5iRvCmDYf63JVsbnkCRaic6nyeRGaWXNx46EYaBnYuixlfGBaW+aKzPcmB5wd44J+t4OW/6WTochbbWuI1h20j8znMfA5zdJTIJ8O0bqtEiDTd73bR/vJrmNnl7w25wm62/fpOvJUzh61y8RwX/r93iZ+ff/hOdans+p07aLq3GWlJTn3zBCe+fgRZSsj4RsO2kYUCVqGAlUqRX4YKE417V7Dln25DKIKBw330H+hZUIMUWV3Brt+9YzwKNB2jHw7T817XLSNYBLaRJ9t3uWjT/HxgYhCTA8SsQVyWm0qlnpXqOkJKBapQFyUqetMaQUVR8boiKEJFETffZbRsDVFR72bj3VHW7ImQHClgX8MV2nM2zXf+/Q1qwP4IwS5YjJwexlfjR/NoqG4V3edCDyxcXmwCnqiHirWVqLoKOlRvqUH1aJiZZegN/pyjkMgz3D6E5tVQ3RqaR0UPuNC9iyvaegvXA4EXHxGlmhplBUGlAjfeK6mKRcDNZz0+IgjXuKhs9NDXMXso1DJurUjnguxIlrf/3WvOZOfR0Dwa1bfVsudf7UV1LWzjv5mzMDJX5J3yiTzSuvXcliOGTg7ys99+yVkYjb8XrY+tZeMvblnqof3cQ8dNSESpVVcSFTV4haM6ZJAnZg/Qb18mLhenl/qWEVwiHH5piMMvzS5oeQvzg1WwsAoWjDk5Ck/Uuyghynw8x+lvn2DDFzeTT+Q5/Z0TWPmbU0XhIw8JZtacEtJPDy6TfPzPIVQ0/CJEjdJEpVJPUEQQKFhYpGScAbuLYbuXpIyXpUA/V9wygrdwC/PEpZ9eoPO1S07rwFLndW/hFpY5NFzUK83UqE2ERRUqGhKJQZ5hu49Bu4uYPXjDGudvsBGcIKEez8tIOV7yWnziEIyXcjNekj+5fTkTzjXnnHaMW7iFeUJyq5jiFpYMvqZWsG2nOtMsLHs1i5CoYJ22E4HAxiImB8eb5HvIycyien0z4YYYQV31UhVuozK4Gr+nGl31YEuLvJEkme1naKyDWKqTaw2TquhUh9dRW7ERv7sSW1oks330jhzHsLKOMZtNABpByN9IbWQDIV8Dbn08xmxmSOeGiaW7GE1cIGfcGBX6mx5S3lo33MItLDcoCrV7H8FT3YCRipPoOMHgvleWZZP+1cjKFMOylwGrk5QcWxK6tAksuhEM+xpY2/gQYX8jQqjY0sSyCghFxe+pIhpcBQhiqaklt6riYm3jAzRUbkMRGgUzjWUb1IQ3UB1eR8/wkSKNk4LGqu201d+HrvkwrCymlUcgCHhrCPubqItu5nTXS/SOHF3sW7BkUHSFQH0Qd4UH1aViGzaFZJ70QBojdX1NvNK+creFphCoC+Cp9E4eNzeaJdWXxJ5HMY8r5MZf58cVcCNUgZW3yMWzpPtT2GWQr39kIMBT4cFX40f3uxCKwMyZ5EaypAdTSHN5rUZUl4q3yos74kXzaghFYBs2RrpAdjhDLn79zEHSvqIwI1SBvy6At9KL6tawzYn3LYVdWH75V9Wt4q8L4I443520bArJAun+FIXkwjbPa74gejiK6vGierwU4iMU08YJrb2Nyu13AY5eYP/rP8ZI3VjC7zE5wkHjtXHi7KV/lxfVCPrcUTY2P07AU02uMEbPyBFGU5cxrTyKUPC6IlQEW+gbvVZzT9BYuY3Gyu3Y0uLCwFsMxE5jSQOX6qOxajtN1TtRhY5lT3+pvK4ILbUfQ1XdXBx4h4HYaUwrhxAKuuol6KujIrCS0eSFxbz8KQg0BtnxW7vRA26svMnRvzpYsk/NE/Ww47f24K32Iy2b9r8/zsChqf2Qiq6w/Td3E2mNku5PcfjP92NmDOr2NLD2MxuoaIs6RlBXsQ2LQsog1Zfk8qsXuPjSOfJj5TW3ToT7qjZVs/4Lm6jaXIMnOmEELXKxHCOnhznz/XYGjw2UnxsTEKgPsvpTa2jcuwJ/XQBXwOUYwYJFPp4jfjHGhec76Hmva8n7FDf84hYa7mia9f8vv3qBc8+cmdvBBYRbIrQ9sY66XQ34qv3ofv2KEYxliZ0d5dyzZ+g/1DerAajaUsPWX96OUBXMjMHBP9lHur885h7Nq7Ht13cRXu1wZHa/fZkz3zs1bTtFVwi3RGjcu4K63Q2Tk77mGTeC5rgRHMkydHyAjmfOEO8YLbs4yTYdvc/oukrWf3Ez1Vtr8EavMoKxHLGzI5z5x1MMHOpbFuFo3a/TdHczqx9tI9Qcxh2eagRTfUm63+7kwovnyCxQQY4rFEW9SsHdoUGb/V7ogTD+plYAjGRsivTRjYKFicXy6TdeRCMoWFG9h4CnmryR4MSlHxJPT23QTWT6GIifnranrnpoqtqJECq9I0e40P8OUjoffJYYqe5B3HqA6vBaZqpGd2k+PHoQw8rSObifgjn1hRvL9NA9fGjhLrUMaF6dmm11eCq8GBkDPVD65VNdGlVbagitCGMZFudf6Ji2jVAEleurqNlWR3Y0S8ePPqRuVz2bv3Ibmm9qX5zq1vC6NTxRD1Ubq6m5rY4D//U9siOlpUxs02bFvc3s+K3deKucJvSJY6tuDX9dAF+tn5rtdRz/2mHOPXOm5MQkFEHDHU3s+Od7CLWEpxwTQPNoaOPHrdvZwKWXz3Pkrw6SG1066ZXwqgh1u+pn7Tcc/XBuZdxCFTQ/uJptv74Tf50Tur/6HLpPR/fpBBqC1N/eSMePPuTkN47O6Flkh9IEGoIEV4SQlqT/YC9nvj/dkM2E4Iowqz+1BlfAhW1YnP7OzKLQ67+wmY2/tAV32D1trOB4h6rLizviIdJaQdPdK9n//75H99udZS3+bdOm4c4mdv727fhq/VPOobpU/LV+fDU+arbVcuIbxzjz/VNL6hUGGgJs/809rLhnJUJTptwPoah4ol7cFR4qN1Sz4uMtHPqTfQweH5i3I6T5AgjN6XOUUmIkRpd9TnC5YdGMoFsPUBlsASQ9I8eIp3vK3jfgrcHrCiOlRd/o8UkDOAHLNuiPtVMVWjPj/qaVo2Bm0DUvTVU76Bo6hGEtD9d7MeEOu9nwxU003b0SxaWS6k0ycLifVE8CJARXhqnf3YC32ocQghX3NpPqTXL4L/aXvDWRVRXU7qjHU+EhcXmMgSN9ZPrTCFVQsSZK7Y4GXCEX7nG2luxwhq43i7NK1N/eyB1fvQtP1AsSssMZBo70k7gcxzJsfFU+arbVEV4dQdEVVn9qDYqu8MF/eW/JmtG73rhEPp7DHXLjCrpxh91UrK3EVcaiZlYIaH5wNbv/9ztxBV1IW5LuTzF4uJ9kTwJpS/y1AWp31hNsCqF5NdZ/cRNCFRz97wedVpCrkO5P03+gl+CKEEIVNN29kvMvdJR1z1Z+vBndpyOlJHY+xujpmY16qieB7tWQtsRIGyQ6x4h3jJIZSmMVLDxRLzXb6qhoi6LoCt4qHzt+czdjF2Iku2dWJr8awaYQO3/7drzVPpLdCQYO95PuS4IQRForqNvVgDvsRg+42Por28kOZ7j08vny7vcCw1vl5fbfu4u63Q0AmBmD4fYhRk4PU0jm0f0uousqqbmtFs2nE11XyZ2/fw9v//5rjJ4Zmde5FZcboTi9r9K2sJaphNNyxqIZQZfmx+MKY9kG8RmKXorB76lECJW8kSRvzBzGyebjM4ZCATL5GD0jR2mp3cvqunuojWxkIH6aocRZ0tkhbLn88ggLAUVVaH5gFbYtOf/cWU5+4xjZ4Qz2uLusqAqh5jC3/97HqNpcg1AEK+9voeOZD0l2Fi8QirRVIC1Jx48/5OTfHiMXy06GthRNoWZbHXv+9V6CK0LoAReb/+k2hk4Mzuq1+esDbP+N3ZP9e11vXubYXx9y8oqmDdLxjnS/i7Wf3cDmL29F8+qsvH8VQycGOfvD00uypul5p4ve97pBEY5IvaZw3x99gtod9XM+ZqS1gm2/thN30I1lWFx4oYMT3zhKbiTrPLvxe+GJeNj0ldtY89R6FE2h7Yl1DB0boPP1S9OOefm1i6z+1Bo0j0bV5hrCzWFGZjFoE3BHPDTc2eSklCT0vtdNLjbzpNp/qI8LPzlPqjtB97tdpHuTWIblvBPS8fJ1v866z29i85e3oro1givCNN61kg+/217ynoSaw0hbcuHFDo5/7QjZ0ewkCYGiKVRtqmHPv9lLeFUEzaOx+Su3MXSsn3T/je37UzSFzV/ZRt1O5/knLo9x6M8+YOBIH3bBdu6HcLzX2p317P6XdxJoDBJoCrL1V3fw7r9/A2MeCzqhalcI0KVEXgdx9S04WDThPVXRURUdW9rkzetTEtBURwfNtPPY9syxY8s2sGb5P4nNxYF3OdX5HIlML35PFa3197Cz7Ze4bfXT1ETW35RUa2VBEfS808XhP9/vFJSMG5SJMv74+RjH/sfhSZJnX7WfirbSrPgA/Qd7OfY3h8kOZ5yc38RxDZv+g70c+csDWAULIQQVbVEa986eO2v79FoirU7eafjkEAf+6/skOsecwppx4yYtSSGR59S3TnD5ZxeRUqK6VNZ+Zj3ukHvWYy82pC2Rpo1t2Jg5c169gUJTWP/5TfjrA0gkve93c/gv9pMZSF95djj3IjuS5djXDjNw0MkL616dtZ/dgOqezoIT6xghdnYEKSWaR2PFx1uK1UsAULE2SqQtihACI2PQ+cbFWbctJPIc/OP3af/mccYuxK7ch4nx2pJCssDpb59g4IhDeiwUQe32+pLjmMDQ8QGO/PeDZAbTyKvfY8Nm8Gg/h/7sA8ysiRCCcEuEpnuayzvwAqJyYzUtn1iNUBUKiTyH/nTfONG8dSX/KcHKW/S+383Rvzk0+Y3U7WqgZlvdvM7vUIk55xFCQVFvUcBdLxZdfdbp0LtOvsYpc8ps+xbv9bNtg77RExw5/12OXPguvaPHsawCVaE1bG5+ko3Nj+HS/Nc3rpsAhWSBD7/XXlTJYOjEIOk+Z2GiaArhlnDpicmG8893UEjMUkgjHSM5fGIQKeV4GK55xgnaE/Ww8v5VCMUpfjn7w9NF83x2weLiT85NhvMCDUGqt9SWGPDNgUBDgMaPrXQWfRmDM98/VfTZGckCF18+N5n/qmiLEmmNTtuukCjQ9dblSa+scW8TnsjsivNCETQ/sApFU5BSMnikj2RX8eiAlbdKeuNm1qT3g55JWjlfnX926bJrcOHFc+Rn8UQBBo8N0H+wd7JVqumulbMKvS4GhKbQ8tBqXCE3Ukr6D/TSd2BmNXXA8a7f7yZxySHx1zwaTffOz3Dbhato+hQFPTS76O8tzIxFM4KWXcC0CihCxaOHrmvfgpVBSomuelGUmV9qVXGhKqVXPYaVZSRxnlOXn+PQuW9yvu8NbGlRX7GZFdW7KXtZepMg1Ztk5FRxOjbbsEj1XcnLuMrwqvLJPCOnix/XSBsMHO5zJl4hCK+K4A5Pn3ij66rwjxc75ON5Bo+WlkdJdicnQ3OqR6NibfQj8ehqt9fjDjn5xFRfilhH6RxR/FwMY7xK1hVyO4uYGdD9dif5uHPPwqsqqNxUPeN2gFN8tKMBIZzKzs7XLy9YJW5uNDvpLWsuDUUt/eCMtMFIe/H3zcqZDBzqQ1oSIQTBFWG8ld4FGXM50H06dbsbJotget7rKhkVsHImo2dHJ3+vXF+JMg8+WzOTRJrOokkIQaBlHYq+dFGSmxGLtmzKGymyhRhBbx2VoVaGk+fLZgFPZQexpYlL9+N3R8kVpssf+T1VqIoL0yovESyRZPKjXOh/B1Vx0VK7l0hgBariwrLnp4G1nDB2MV4Wd6WVuzLBlUMqnRlMl5W7iI2XwQtF4Kv14wq5p5aDC0fSRh3X+rNyBrpPJ7hy5ol8Aq6APulNCCHw1fgRquKEyW5SCNUJG4tx78vIGLgjHlwzLByuhh5yTVYACsXpoZvI412NVF+KvoO9tDy02qk+fWA1ve93zzhR12yrI9DgVKVmBtL0fnB9UktCEQ4ptVtFaAqKqjiaq4qTy7zSZDq9knQmZIcz5JOlv8vY+Ri2ZaNoitOrWOEtq/BmIeCt8k4u5qQlsQp2yfdYKGJK9abud4rJskOZOY2hEBvGymdRPY7x9zW0ULnjbkaPvY+Vu8WLWg4WzQgWzDSDY2cJemupj24hnu5iMH4GW05dXSpCQwiBZV+ZYNO5YVLZQcL+JpqqdzGW6cO0roTLXJqfhuhts35Muuq8EKaVn0bBI4SY3M/JN968k+hMyAzN5cUvPSkVEvmyStAzg+lJY6W6VFzBqVWTQgj89YHJZxBcGeZT33yqvFFepQOneTWEuLnrfRVNcYz5+L2o3lLDY9/+TFn7Tr0XM0dE7IJF52sXWfnxFhRdGTd0wWlhTsdArnIWFVLS/c4VD7LoGFSBt9JH7a56qjfXEGgM4Y160P0uVLeKoqkouoLqUhFleH9Xw0gVynrfskPpKQVa175viwl/XdAR48a5Fx/7v+4ta78p7ROqMi9lEzOTJNvfiR6qcOY2VaNm78MEVm0g3X2eQmwIO59FjjPIuKuu5CCFquNf0Ya7YvYIwXyQ7rmAfRNUqy5qAL1n+BDRQAsVgZVsXPkYNeF1xNJdGGYWVdHw6GGCvlrSuWE6el+b3M+yC1we/IBNzTVUh9ewqfkxBmKnMMwcbleQ+orNeNzhWb3A2oqNrKjeRSx5mWR2gLyRxJYWuuolElhBQ3QrtrQYTnRMMb4fBSxWM7lVsMpqdL72/Lr/mglagCt4JVzjfLhziGsusC7gUkCoypR+0bnei2K3YvBoP8nuBJHVFfjr/NTuqJ9mBMOrKqhcXwWAkTLoeutyybCeK+Rm7WfX0/b4enxVXoQ2nlmRE2wvE4VTzr+vty7AMuyyRKbNrDm5EhLCqSa+UXAFXZPrx3k9u/m8y1ISa99PoGU9qtspKESo+Bpa8DU0w8SzmDjfVYsn1eun/v6nFmklKbnw3T8nN7D8xJuvxaIawbyRov3ys7Q1fJzq8FpqKzZRU7Fh/KMAIVRAkslPD3cOjp3B0xdiVd1d1ITXUxVqQ0obIRQy+VFOdT7Pmob78boi0/a17AI+d5SApwYpLezxCiohFBShYlp5Lg/uo3fk+GJe/vxRZujoaiw7RfMZcPWHmB5I0be/97obfIfbh26Kay0FMWE7pCTZmWDwWOn86LUYPjU060RWSBTofruTcEsEBKy8v4ULL3ZMober39Pg9GoC8QujJXNxrpCbPf96Lyvva3EmfgnZoQzDp4YYuxAj1Z+iMJbHyBiYOZP63Q1s+eXtczASy/v5CuWKaTfzJr3vdVMoI4R7NfJjeYz0/KjU0p0dxNr3U7ntLhiPdE2KBqizx3kmPMfFwFwWPkuFRS+lyhZinOp8noC3hopAM35PFZrixpImucIYiXQv8XTXtP2ktOgc2k8sdZnq8Fp87kps2ySR6WVo7Cw5I0HY14jfUzneCH8Fg/EPyebjRAJN+FxRNM2DQMGwcqRzQ4wkL5DOjUxrwl9uUFQFRVv0At6yoLrUKcZrNmi+qa/UTJWOZvbK31I9ST74g3c+EgbtumHLKZ7zaMcI+/7TOwt6CmlLut64xJqnnLaS6NoqIm3RySZ41aNNVurals3lVy9Oa76fAgFrn1rPyvtaUDQFq2Bx/rmznP6H9nGO1+n7BpuurzAOQNWVyVBjMWhe7SpRGlm0snahYWaMybWbbdic+NujxM7Or/l9LpCWxeB7LyNNg4otd6B6pjI63UJxlGUEva1rCGy6DVdlNdK2yHVdZuzgPqyEQ7xace+D2Jk0dqFAYNNWFJeL7OWLjO1/DyudwlbA+6n7GTj4Pp4VPrwt9UjTINXeRerEOaTlTARqIEhw2058q9aAIshePE/iyAESfW8CEN59J66aFeRfOQbAhf638La0Evns42R/8B3sfI6aJ54m/eFJiFZTaFtLQUrSZ0+TOHIAWRhfcQmBp3Elga3bcdXWgW1TGOwn/t5bmGPxybGEduzB29IK0iZ99jTJY4exc+MGV1HwrmoldNsutEgFdiFPvq+Xsf3vYiVLK1OU836qbhXNtzz6flwht1PFVmKS8VVfyXHZBXsardcEGwoShyi60ovqVpecE3QpYJs22eHMpMPjq/Kh6Mq8SMhnQvx8jOH2QRrvXIE74qbxzqZJI1i5oYpws1PMkR3O0Luvu6gDpvtdrPpk2+TirOe9Lg7/xf6iz6/cBdSU8wRdaC6VUj6Sr9o3eWzbtK/bE5sP/v/2zjQ2jvO84785916Su7yWx5KiSIq6rIOSLFuSDzmO7fqqncJBbBSIAzhxG+RD8q1ogQIN0BZFP7VAP6QHgjRunRaOa8BHfSn1JVuSJVsnZR0U7/va+5iZnX4Ycnktd0lZlhxzfgA/kFi+Mzvzzjzv87zP838SY/P7kYpLxlF+67Iyc5kUox/9L9HLZ/G378BTvwHZ7UNUVUtRZvaZFGQFcdb7M02TnJYtqjX6ZVhtIuStprQRFATcLW3kUkkin36M5PVRfschZJ+fsVd/C4aBGqzC1Xk7meFB4ufPILndlN95N6LLxcTrr4Ao4KhvpKq6lmT3JaInj+FsbKLqocfIZTMkLpxFdLqoevgJZL+fyMljkDPx79qDs76R0Vf+i1wyiVxegVpdOx9DAiS3B2dDGGZXjY7aOpzhZtK914ieOoFaU0vw8AOYhkH0008AcLd1UP3od8gMDZA4dwYkEaUimJ8oostFzR8+haCqxE6fQpBlyvbeiRqsYuKtVzF1HbWqhprHnyJ27jSJL84jebw4Qg2IqoNC62gzl8vvswgCiErpzXBXlfvLyXHdQDw1lphzsbotwCq2nn0pJcYSy19KJsx0T2NoBpIq4ShzUtZcXlLN5JvInHjBXHaju8aDp9Zbsj5vrRhZg753r1G7pw5JkWi4q4muF89hpA1Ce+vz+5Jjn1n7h8Xw1vlwVljZqzkjR9+Ra8UXMII1d9bqlbgr3VZmcYmsybKWCsTZZz89mSo5P28kieE46ekU3pAPRIGqbTWMHC9SJ/hVkzNIjfSRGulDkBVkjw/J4UKQ5Nm9QoGK7fup2LoXACMVZ/jIy2ix5dtRN4LsdPGw+teF0kbQNJl8+/X53wUBSXXg2bwNyeHESM5nI46/9jL6rHcoebx4OraCOP+yz46PMvnWa5i6TvLKFzjrG3E1tZC4cBZXcwvOxiaGX/g3MsOWzmi67xr1338ez8ZNxM5+tuovZcSijL/xCmY2i3BRxlFTi7uljeinxxBUlfI7DpHu72X0f34z7x0uwNO+BbW6lqFf/wvZsVFrzGSCqoefYObEx2jjo5ZmnyyTunaFVM9VTK24h2RkDPSM9bIQZDGfWr0iAtTtq//a1MKpPgfBzVXEi6Sfyy7ZUsAQrFVmtHemYJeKya4JkuMJfPV+HH4HDXc1MXVpcl12ZR89NYyWyOIos1onhfbV33AjCDB8fIjkmHXNffU+gpurmOwap/5Ag1Won9Hpeae75Dac7Jz36nJajsxMcc9LdsrUdK5dUk52KwS3VhXttCI5JGp2hRAkwdpTHYySmry+UoPrQUtkGT05jE7RTcUAAAvXSURBVOdhq7Sk4VCYS7+9UPKa3AxMXUOLTC3r0ueqDVv7dYKAaeikxgZ/b4zVV8WqNpxElxt3+2bK9h+k4uC9OBrCIIqLYnqZsRGMxLw8mh6JWCsQac4ImqQH+jB1yxCYuo6RiCMoCggCam2IXDqVNzoA2vQUeiyCo6FxTV8qPTSQN25mLocejVnHEQUklxulIkDy6qWCBhBBwFFbhyBJ+HfvI3DfgwTuexD3xnZEVUUNWll0mdFhoqdPUvXIE9Q8+T18O/cgulc2bJlIhsxMGtM0EWWRmj11BdVU5vCHy26JDNSKCNDyB60oRVLQq26roeq2ausBy5kMftS/qB5xjuRYgoEP+qyHURRofbTdUoBZjcEXuL5s0q8pkZ4Zhk9YqieiJNLx3a35NkYlEVh1mDE5nmB4VrlF8arU7A4RaA/ibfDPLlgiTJwr/TLMxjVys/0MRUUsGgIURIHwvc0ENlWu6hyX0vJga75LRSGCHZXzHT1MGDw6cFPD6jktR8/b3WgJzZIKbAvQ/uTm+UzZEtyKeWyk4ra+6BJK3i0lUEno6Wcp338QyePFNPSCXo+paUsWkcuXlMW8JYG5ItIF/5fvZl5kssjLnVkzu/Q4C8acS0kulo0oCpiA6HAiudxILjemrhP99Bj6zPTsMTJMvvMGw//xSzKjw5TffoC6p59FCRZ+4LV4lonZrDtBEGi8q4mmb7UgKotvgSBaOoh7frofb51v5XO8icylWIf21rP9B7tQyxyLb4kAgY5Kdv14L7JTzr9UBz/qW2FAuPRSF9GeGUzTxBl0ccdfHKL5/o3ziQ4Lf0QBySkR6Khk8/e2sfHRdljNy3/BGEvDcVZ4aMFn1sLCcUVh2bVAXP3YOS1H1wtnSY5ZKkm+Rj8H/vJu6u9stOrHllwLQRSQ3QqV26rZ9v2dhA83r+6cTej7XU9eazO0r466/Q0oszWGgx/1r6o2MDEcy0vciZLIhgdaC9bmiYpI+PAGdj6/B8khLUrTL3mqs5+tuq2GHT/sxFHhXHaNy1sD7P7JPhSvimmaxIdi9L/Xs7oDLJhXwpJxhTXcO7A8+d53r2HmrMXtlme2s+O53bhrPMvHml3Aeet8NN3fwo7ndlvf7SaiJxOYhm0EF1IyHOpu24Ts8TL0wr+iTU6AIBA47EatrL6hJ5IZGcLfeTtKZTXZUUsgWC6vQPL7yXxqhUdNTUNyuREkadY2CjhCdWs6Ti6dQo9EcDZtIHbmVN4zzWOaZEdHyLWmmXr/CPp0kWwvwyA7Okx2dJjYZyeof/Z53G0dRCYLZ/h1v3aZpm+14KxwIrtk9v7sDhoOhhk5OYQWy6L4VAKbKgntsxqqRq7NICoivsa1Z9fdUEyY+mISX4OPjj/aTM2uWoY+HiA2EEUQBQKbgjQcCuOutjxhPaVz/leni+7nxPqjnPyH4+z/80O4gi689T72/9lBon+8nckLE/mEEcWj4Al58Tf6cQbdqD6Vyy9fLCSQAlj9/mp2hVB8CopHRfWqKB4VT8ibX3CoHoXOn95OajyJltDIJrJoCc0Kb50aIdK9PAQnO2XqDzRa3d69KopHscb2qpS3zmt3hu9pxhvyoc2OmU1k0eIaidE4gx/2FUx6mbw4wef/dII9P9uP6ndQ0R7g4M/vIdIbYaprgvR0yoqW+FSrT2CDH2eFC9WncvoXq++LOXlhnJmr01Ruq6KiNYDqcyCIAtlYpmTbqzn0tM61N6+w40ediJJI6PZ6Dv78Xq69eZXUeNLqAh/y0nAgTM3uWiSHzNDRAapuq15UH1qK6ctTuKs9tD6+icrt1Qx9MpDvdFLRFqDhrnC+76KRNbjwwlnigyuH6mt211LeGkT1WvNC8SjWvVvgdXvrrQVINpYhG5+fE5lohsGP+kkX6LuZ03Kc+edTeGq9hPbVoXhUtjyzneZvtzDZNUF8IIqeNpBdMq6gC1+4DE+1B0eZg/hw3OqGchMxUonZRERbWm2OkkbQSMQRnU7cG9vI+spw1Dfibt10wzN/Uj3dpAf7qXzoMSLHj1qJMXtuR5ucJHnF6tSd7u+lbP9BAnffR/LKJdTaEO6WNtaylM+l00ROHKXywUepeuRJklcvW21fglXEz50mOzZC4uJ5vNt2UP3Yd4if/ZxcNoNcVoHs9TH5u7cwsxnc7ZtxhZtJD/SR07I4QvUIioI+PbXisacvT3H6FyfZ9Sd7UP0OFI9C+N5mwvc2L/qcaZpEumc49rcfsvGx9ltuBHOawelfnMQT8rLjh50EOyrzxdVLycYynP/3M/QeuVZyf2n42CAf/9X77PzTPQTag8gumUBbkEDbyl0tzJyJkVk55FW7r57On+wrWloiKhK1ncsXT6Zpcuofjxc0gmqZg9ue223V2xXB1+AvWBIwfWWK0ZNDZLUCIXgTet7pxtAMdjzXib+5DMWjUrmlisotK6t5mEZuVRJ5c2gJjf7/66FyWxWyU6GsqRzTNBk/O5YXdS6JCVde+YLKbdXUH2hElEXq9jcQ2ldv3RdRsLJBBQE9rXPppS7O/+o09/z9twluXt2L1zRMzv3yc2SXwq4f76WiLUCgvfCc0JIaF188R/cbl4vOt9bHO9jwwMaix1W9Kg2HwsuPkdKID8YKGkGwsmo/+esP2PmjTsKHNyA5JLwhn5Uws9J3NE1LgOIm74PrqbjtCS6hpBFMXOpC9pfh3bYTQRRJXrvK5Nuv49uxOy/Fo0dmEGR5UYjRSCbJTozl/5YdH1uURINpos1Mk0tZ3kIunWL8tZcp27OfijvvBkEg1dNN5MTHGHFrhZfsucrEm6/i37EbV3MLqf5eJt58lYpDh2H2XLTJCYzEghWhCXo0ushox8+fIZdK4dvZScWBuzENnczQYL78wUgmGH35N/h377X2+hQFPRoh3nU2X86hRyPIZRUENraDJKFHppl481WSVy6teC3N2T5/yZE4m57aSkWbtRoXFTGfnJCZSTN8fJCL/3meSM8M3kY/wY5KcoaJVqCLuGlCbDCWz/DLTK+i67ppFalPX7EM9kpSa8mJJDNXpklPp5i8MM7wsUHigzE2P72NQFsQxadaXQf0HNmERqR7mq4XzzN0tL9kV/m56zF8fJBIzzRN92+k6b4NeGq9qB4lnz2bM3IYaR0toREbiDLwYR/97/WuWFeYmUkzc3X6uvdbVgoJ5vSc1erpOrVKY/2Roi880zDpO9LD9KUpNjzUSuNdYVxVHhS3kjfoOd1q3aTFs0R6Zxh4v4+BD1bnwc0x8GEfDYfC80o1pml16Ciwd7sSmUiGY3/zIR3f3Ur4vg24Am4kp4TkkMnpOTIzaWKDMS691EXfEavusP+9HkRFJD4UWzE0mp5KMXN1imxcs3pRTqZIDMfZ8sx2Ah3B/LNiGubsNYjwxX+fp/+93pJlJQvn+1ox0vqi2tZCJEcTHPu7o/S/38vGR9otT9vvQHJYiUSWtqiBntRITSYZ/XyEvnd7SK8iBH0jMVJJTOObpZL1ZRGKxeoFQVh/6Xo3CVER8TX48db5kN0KggCZaIZoX8RqDJpbso8JxT2r1Xzmev6nwGdERaJsQznekBfZJWNkDBIjcav0YQ2eyVIkp4w/7Mdd7c3LrRlpnUwkTWI0YemSrmbl/GXyDUoNf71jr/FJUjwK/nAZrio3issSD9fTOpnpNInROKmJ1PULDCz9Dtf7lAvgqnRT1mx1CxElAS2lkxxLEO2dWZ6kslIMu9C5LZxvsoi/uRxvnQ/FLWNkDRKjCSJXp9dkvL/SebHwMJIlau6t9+HwOxFlASObQ4tnSE4kiQ/FCyaM3RREkfDjP8BZWYsej9L/2q/Rote3OPh9wjTNFe++bQRtbGxs1hELi+fnIlvfdIoZwW9oe3UbGxsbm0LYJRKL+XoIU9rY2NjY2NwCbCNoY2NjY7NusY2gjY2Njc26xTaCNjY2NjbrFtsI2tjY2NisW4qWSNjY2NjY2HyTsT1BGxsbG5t1i20EbWxsbGzWLbYRtLGxsbFZt9hG0MbGxsZm3WIbQRsbGxubdYttBG1sbGxs1i3/D1vOI5xCDTXKAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df1 =df.loc[df.spam==0]\n",
"df2 =df.loc[df.spam==1]\n",
"\n",
"word_cloud=WordCloud(width=800,height=800,background_color='black',stopwords=stop_words,min_font_size=20).generate(str(df1['text']))\n",
"\n",
"fig=plt.figure(figsize=(8,8),facecolor=None)\n",
"plt.title('Non spam messages')\n",
"plt.imshow(word_cloud)\n",
"plt.axis('off')\n",
"plt.show()\n",
"\n",
"word_cloud=WordCloud(width=800,height=800,background_color='black',stopwords=stop_words,min_font_size=20).generate(str(df2['text']))\n",
"\n",
"fig=plt.figure(figsize=(8,8),facecolor=None)\n",
"plt.title('Spam messages')\n",
"plt.imshow(word_cloud)\n",
"plt.axis('off')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.024308,
"end_time": "2020-11-19T10:44:49.566675",
"exception": false,
"start_time": "2020-11-19T10:44:49.542367",
"status": "completed"
},
"tags": []
},
"source": [
"**From the above two plots, we can see the keywords in the spam and non spam messages**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"papermill": {
"duration": 0.024504,
"end_time": "2020-11-19T10:44:49.616193",
"exception": false,
"start_time": "2020-11-19T10:44:49.591689",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
},
"papermill": {
"duration": 9.674277,
"end_time": "2020-11-19T10:44:49.748426",
"environment_variables": {},
"exception": null,
"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2020-11-19T10:44:40.074149",
"version": "2.1.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| 0047/231/47231803.ipynb | s3://data-agents/kaggle-outputs/sharded/010_00047.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.005757,
"end_time": "2020-11-19T10:48:00.889219",
"exception": false,
"start_time": "2020-11-19T10:48:00.883462",
"status": "completed"
},
"tags": []
},
"source": [
"Wear a mask, mind your distance, wash your hands, and PLEASE be safe. Thank you kindly :)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"execution": {
"iopub.execute_input": "2020-11-19T10:48:00.906278Z",
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},
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"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Good to go\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import plotly.express as px\n",
"print (\"Good to go\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T10:48:01.921266Z",
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"file_pathway = \"../input/covid-data/Copy of full_data.csv\"\n",
"data = pd.read_csv(file_pathway, decimal= ',')\n",
"subset=data[data[\"location\"].isin([\"United States\", \"United Kingdom\",\"Japan\",\"Germany\",\"Italy\",\"France\",\"Canada\"])]\n",
"subset=subset.fillna(0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T10:48:02.031929Z",
"iopub.status.busy": "2020-11-19T10:48:02.031247Z",
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"shell.execute_reply": "2020-11-19T10:48:16.277430Z"
},
"papermill": {
"duration": 14.258074,
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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" <script type=\"text/javascript\">\n",
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
" if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
" if (typeof require !== 'undefined') {\n",
" require.undef(\"plotly\");\n",
" requirejs.config({\n",
" paths: {\n",
" 'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
" }\n",
" });\n",
" require(['plotly'], function(Plotly) {\n",
" window._Plotly = Plotly;\n",
" });\n",
" }\n",
" </script>\n",
" "
]
},
"metadata": {},
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},
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}
],
"source": [
"fig = px.bar(subset, x=\"location\", y=\"total_cases\", color=\"location\",\n",
" animation_frame=\"date\", animation_group=\"location\", title= \"Total Covid Cases of G7 Countries Dec 31st 2019 to Nov 16th, 2020\")\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
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"iopub.status.idle": "2020-11-19T10:48:16.455020Z",
"shell.execute_reply": "2020-11-19T10:48:16.455532Z"
},
"papermill": {
"duration": 0.150942,
"end_time": "2020-11-19T10:48:16.455693",
"exception": false,
"start_time": "2020-11-19T10:48:16.304751",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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},
"metadata": {},
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}
],
"source": [
"fig = px.line(subset, x=\"date\", y=\"total_cases\", color='location', title=\"Total Covid Cases of G7 Countries Dec 31st 2019 to Nov 16th, 2020\")\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T10:48:16.528057Z",
"iopub.status.busy": "2020-11-19T10:48:16.526959Z",
"iopub.status.idle": "2020-11-19T10:48:29.844039Z",
"shell.execute_reply": "2020-11-19T10:48:29.844583Z"
},
"papermill": {
"duration": 13.360775,
"end_time": "2020-11-19T10:48:29.844764",
"exception": false,
"start_time": "2020-11-19T10:48:16.483989",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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],
"source": [
"fig = px.bar(subset, x=\"location\", y=\"new_cases\", color=\"location\",\n",
" animation_frame=\"date\", animation_group=\"location\", title=\"Daily Covid Cases of G7 Countries Dec 31st 2019 to Nov 16th, 2020\")\n",
"fig.show()"
]
},
{
"cell_type": "code",
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"metadata": {
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"iopub.status.idle": "2020-11-19T10:48:30.039845Z",
"shell.execute_reply": "2020-11-19T10:48:30.039195Z"
},
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"end_time": "2020-11-19T10:48:30.039966",
"exception": false,
"start_time": "2020-11-19T10:48:29.897207",
"status": "completed"
},
"tags": []
},
"outputs": [
{
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" var display = window.getComputedStyle(gd).display;\n",
" if (!display || display === 'none') {{\n",
" console.log([gd, 'removed!']);\n",
" Plotly.purge(gd);\n",
" observer.disconnect();\n",
" }}\n",
"}});\n",
"\n",
"// Listen for the removal of the full notebook cells\n",
"var notebookContainer = gd.closest('#notebook-container');\n",
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" x.observe(notebookContainer, {childList: true});\n",
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"\n",
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"if (outputEl) {{\n",
" x.observe(outputEl, {childList: true});\n",
"}}\n",
"\n",
" }) }; }); </script> </div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = px.line(subset, x=\"date\", y=\"new_cases\", color='location', title=\"Daily Covid Cases of G7 Countries Dec 31st 2019 to Nov 16th, 2020\")\n",
"fig.show()"
]
}
],
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| 0047/232/47232001.ipynb | s3://data-agents/kaggle-outputs/sharded/010_00047.jsonl.gz |
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{
"name": "stdout",
"output_type": "stream",
"text": [
"/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load\n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the read-only \"../input/\" directory\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
"\n",
"import os\n",
"for dirname, _, filenames in os.walk('/kaggle/input'):\n",
" for filename in filenames:\n",
" print(os.path.join(dirname, filename))\n",
"\n",
"# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
"# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
]
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{
"cell_type": "code",
"execution_count": null,
"metadata": {
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"start_time": "2020-11-19T10:58:23.089081",
"status": "running"
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"outputs": [],
"source": [
"!pip install jcopml"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
"_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
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"source": [
"## Wine-quality-classifier 🍷🍾\n",
"\n",
"Just follow this notebook till the end and you could predict quality of wine is good or bad.\n",
"\n",
"**In this notebook we'll classify the quality of wine into Good or Bad.\n",
"Alltough in the data the target variable have numbers between 0-10 but we can classify it by classifying all obsevations as:**\n",
" \n",
"* quality >= 7 as good\n",
"* quality < 7 as bad\n",
"\n",
"Let's firstly import data and analyse it "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.compose import ColumnTransformer\n",
"\n",
"from jcopml.pipeline import num_pipe, cat_pipe\n",
"from jcopml.utils import save_model, load_model\n",
"from jcopml.plot import plot_missing_value\n",
"from jcopml.feature_importance import mean_score_decrease"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
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},
"tags": []
},
"source": [
"# Import Data & Feature Engineering"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
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},
"outputs": [],
"source": [
"df= pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
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},
"tags": []
},
"source": [
"Let's check if there are any null values"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
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},
"outputs": [],
"source": [
"df.isna().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {
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},
"source": [
"Woow our data is not having any null value. So there's no need to clean the data. 😃😃\n",
"\n",
"Now let's check the information about the features we have."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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"duration": null,
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"source": [
"df.info()"
]
},
{
"cell_type": "markdown",
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"duration": null,
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},
"source": [
"So we have total 1599 rows each having 12 features.\n",
"Let's explore the data "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
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"tags": []
},
"outputs": [],
"source": [
"# Check Missing Value\n",
"plot_missing_value(df)"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
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},
"source": [
"Dataset is Clean for NaN Values"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
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},
"outputs": [],
"source": [
"df['quality'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
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},
"source": [
"So we can see there are only 217 (199+18) records having quality >=7 i.e of good quality\n",
"\n",
"Now let's change our target variable(quality) into good and bad."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"papermill": {
"duration": null,
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},
"outputs": [],
"source": [
"df['quality'] = ['Good' if x>=7 else 'bad' for x in df['quality']]\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
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},
"tags": []
},
"source": [
"See our quality variable have values good and bad.\n",
"Now our data needs just one thing more to be ready for modelling.\n",
"\n",
"I used Pipeline Model for Fast Practice Build a Model"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
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},
"tags": []
},
"source": [
"# Dataset Splitting"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
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"tags": []
},
"outputs": [],
"source": [
"X = df.drop(columns='quality')\n",
"y = df.quality\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)\n",
"X_train.shape, X_test.shape, y_train.shape, y_test.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
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},
"tags": []
},
"source": [
"# Training Phase 1 : Simple Thinking"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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"duration": null,
"end_time": null,
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},
"tags": []
},
"outputs": [],
"source": [
"from xgboost import XGBClassifier\n",
"from sklearn.model_selection import RandomizedSearchCV\n",
"from jcopml.tuning import random_search_params as rsp"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"papermill": {
"duration": null,
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"outputs": [],
"source": [
"# Pipeline\n",
"preprocessor = ColumnTransformer([\n",
" ('numeric', num_pipe(), ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\n",
" 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\n",
" 'pH', 'sulphates', 'alcohol']),\n",
"])\n",
"\n",
"pipeline = Pipeline([\n",
" ('prep', preprocessor),\n",
" ('algo', XGBClassifier(n_jobs=-1, random_state=42))\n",
"])\n",
"\n",
"# Training\n",
"model = RandomizedSearchCV(pipeline, rsp.xgb_params, cv=3, n_jobs=-1, verbose=1)\n",
"model.fit(X_train, y_train)\n",
"\n",
"# Evaluation\n",
"print(\"Best Parameter for XGB is {}\".format(model.best_params_))\n",
"print(\"Score for Data Train is {}\".format(model.score(X_train, y_train)))\n",
"print(\"Best Score is {}\".format(model.best_score_))\n",
"print(\"Score for Data Test is {}\".format(model.score(X_test, y_test)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
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"status": "pending"
},
"tags": []
},
"source": [
"# Phase 2 : Feature Importance"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
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"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"df_imp = mean_score_decrease(X_train, y_train, model, plot=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
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"tags": []
},
"source": [
"I think all feature is importance to include in model... :)"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
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},
"tags": []
},
"source": [
"# Phase 3 : Polynomial"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
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},
"tags": []
},
"outputs": [],
"source": [
"# Pipeline\n",
"preprocessor = ColumnTransformer([\n",
" ('numeric', num_pipe(poly=2), ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\n",
" 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\n",
" 'pH', 'sulphates', 'alcohol']),\n",
"])\n",
"\n",
"pipeline = Pipeline([\n",
" ('prep', preprocessor),\n",
" ('algo', XGBClassifier(n_jobs=-1, random_state=42))\n",
"])\n",
"\n",
"# Training\n",
"model = RandomizedSearchCV(pipeline, rsp.xgb_poly_params, cv=3, n_jobs=-1, verbose=1)\n",
"model.fit(X_train, y_train)\n",
"\n",
"# Evaluation\n",
"print(\"Best Parameter for XGB is {}\".format(model.best_params_))\n",
"print(\"Score for Data Train is {}\".format(model.score(X_train, y_train)))\n",
"print(\"Best Score is {}\".format(model.best_score_))\n",
"print(\"Score for Data Test is {}\".format(model.score(X_test, y_test)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"source": [
"# Conclusion Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
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},
"outputs": [],
"source": [
"parameter = {'algo__colsample_bytree': 0.5163168100195784,\n",
" 'algo__gamma': 1,\n",
" 'algo__learning_rate': 0.24410494920734804,\n",
" 'algo__max_depth': 8,\n",
" 'algo__n_estimators': 172,\n",
" 'algo__reg_alpha': 0.015391401698302503,\n",
" 'algo__reg_lambda': 0.025101867367220122,\n",
" 'algo__subsample': 0.7658130344388201}\n",
"\n",
"# Pipeline\n",
"preprocessor = ColumnTransformer([\n",
" ('numeric', num_pipe(), ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\n",
" 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\n",
" 'pH', 'sulphates', 'alcohol']),\n",
"])\n",
"\n",
"pipeline = Pipeline([\n",
" ('prep', preprocessor),\n",
" ('algo', XGBClassifier(parameter=parameter, n_jobs=-1, random_state=42))\n",
"])\n",
"\n",
"# Training\n",
"model = pipeline.fit(X_train, y_train)\n",
"\n",
"# Evaluation\n",
"print(\"Score for Data Train is {}\".format(model.score(X_train, y_train)))\n",
"print(\"Score for Data Test is {}\".format(model.score(X_test, y_test)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"source": [
"I found best Parameter and I fitting to model :)"
]
},
{
"cell_type": "markdown",
"metadata": {
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"duration": null,
"end_time": null,
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},
"source": [
"**So we can see that Score Highest of XGB Classifier on Phase 1%**\n",
"\n",
"**Conclusion: for 3 Phase is 91%**\n",
"\n",
"**PLEASE UPVOTE MY NOTEBOOK IF IT HELPED YOU** 😊😊"
]
}
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"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load\n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the read-only \"../input/\" directory\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
"\n",
"import os\n",
"for dirname, _, filenames in os.walk('/kaggle/input'):\n",
" for filename in filenames:\n",
" print(os.path.join(dirname, filename))\n",
"\n",
"# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
"# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
]
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" 'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
" }\n",
" });\n",
" require(['plotly'], function(Plotly) {\n",
" window._Plotly = Plotly;\n",
" });\n",
" }\n",
" </script>\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"import seaborn as sns\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn import preprocessing\n",
"\n",
"from plotly.offline import init_notebook_mode, plot\n",
"import plotly as py\n",
"import plotly.graph_objs as go\n",
"init_notebook_mode(connected=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
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"execution": {
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" <th>lunch</th>\n",
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"text/plain": [
" gender race/ethnicity parental level of education lunch \\\n",
"0 female group B bachelor's degree standard \n",
"1 female group C some college standard \n",
"2 female group B master's degree standard \n",
"3 male group A associate's degree free/reduced \n",
"4 male group C some college standard \n",
"\n",
" test preparation course math score reading score writing score \n",
"0 none 72 72 74 \n",
"1 completed 69 90 88 \n",
"2 none 90 95 93 \n",
"3 none 47 57 44 \n",
"4 none 76 78 75 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"meta = pd.read_csv('../input/students-performance-in-exams/StudentsPerformance.csv')\n",
"meta.head()"
]
},
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" race/ethnicity count\n",
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"1 group A 36\n",
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"3 group B 86\n",
"4 group C 180\n",
"5 group C 139\n",
"6 group D 133\n",
"7 group D 129\n",
"8 group E 71\n",
"9 group E 69"
]
},
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"source": [
"race = pd.DataFrame(meta['gender'].groupby(meta['race/ethnicity']).value_counts())\n",
"race[\"count\"] = race.iloc[:,-1:]\n",
"del race['gender']\n",
"race.reset_index(inplace=True)\n",
"del race['gender']\n",
"race"
]
},
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\"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Test preparation of students : gender-wise stats\"}, \"xaxis\": {\"tickangle\": -90, \"title\": {\"text\": \"Preparation Status\"}}, \"yaxis\": {\"title\": {\"text\": \"Student Count\"}}}, {\"responsive\": true} ).then(function(){\n",
" \n",
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"var x = new MutationObserver(function (mutations, observer) {{\n",
" var display = window.getComputedStyle(gd).display;\n",
" if (!display || display === 'none') {{\n",
" console.log([gd, 'removed!']);\n",
" Plotly.purge(gd);\n",
" observer.disconnect();\n",
" }}\n",
"}});\n",
"\n",
"// Listen for the removal of the full notebook cells\n",
"var notebookContainer = gd.closest('#notebook-container');\n",
"if (notebookContainer) {{\n",
" x.observe(notebookContainer, {childList: true});\n",
"}}\n",
"\n",
"// Listen for the clearing of the current output cell\n",
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"if (outputEl) {{\n",
" x.observe(outputEl, {childList: true});\n",
"}}\n",
"\n",
" }) }; }); </script> </div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = go.Figure()\n",
"fig.add_trace(go.Bar(\n",
" x = male['test preparation course'].unique(),\n",
" y = male['count'],\n",
" name='male',\n",
" marker_color='#6b005e'\n",
"))\n",
"\n",
"fig.add_trace(go.Bar(\n",
" x= female['test preparation course'].unique(),\n",
" y= female['count'],\n",
" name='female',\n",
" marker_color='#e65ed6'\n",
"))\n",
"\n",
"fig.update_layout(\n",
" title=\"Test preparation of students : gender-wise stats\",\n",
" xaxis_title=\"Preparation Status\",\n",
" yaxis_title=\"Student Count\",\n",
")\n",
"\n",
"\n",
"fig.update_layout(barmode='group', xaxis_tickangle=-90)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:05:59.590143Z",
"iopub.status.busy": "2020-11-19T11:05:59.581287Z",
"iopub.status.idle": "2020-11-19T11:05:59.594074Z",
"shell.execute_reply": "2020-11-19T11:05:59.593248Z"
},
"papermill": {
"duration": 0.055404,
"end_time": "2020-11-19T11:05:59.594237",
"exception": false,
"start_time": "2020-11-19T11:05:59.538833",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"le = preprocessing.LabelEncoder()\n",
"meta['gender'] = le.fit_transform(meta['gender'])\n",
"meta['race/ethnicity'] = le.fit_transform(meta['race/ethnicity'])\n",
"meta['parental level of education'] = le.fit_transform(meta['parental level of education'])\n",
"meta['lunch'] = le.fit_transform(meta['lunch'])\n",
"meta['test preparation course'] = le.fit_transform(meta['test preparation course'])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:05:59.684665Z",
"iopub.status.busy": "2020-11-19T11:05:59.683651Z",
"iopub.status.idle": "2020-11-19T11:05:59.689125Z",
"shell.execute_reply": "2020-11-19T11:05:59.688401Z"
},
"papermill": {
"duration": 0.057688,
"end_time": "2020-11-19T11:05:59.689273",
"exception": false,
"start_time": "2020-11-19T11:05:59.631585",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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"text/plain": [
" gender race/ethnicity parental level of education lunch \\\n",
"0 0 1 1 1 \n",
"1 0 2 4 1 \n",
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"4 1 2 4 1 \n",
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"0 1 72 72 74 \n",
"1 0 69 90 88 \n",
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"3 1 47 57 44 \n",
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},
"execution_count": 11,
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}
],
"source": [
"meta.head()"
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},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
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"tags": []
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.heatmap(meta.corr(), annot = True)\n",
"sns.set(rc={'figure.figsize':(10,10)})"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.035312,
"end_time": "2020-11-19T11:06:00.462857",
"exception": false,
"start_time": "2020-11-19T11:06:00.427545",
"status": "completed"
},
"tags": []
},
"source": [
"# **No effect of *'Parental Level of Education'* as a feature in determining test scores.**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"papermill": {
"duration": 0.03621,
"end_time": "2020-11-19T11:06:00.534633",
"exception": false,
"start_time": "2020-11-19T11:06:00.498423",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
},
"papermill": {
"duration": 8.456442,
"end_time": "2020-11-19T11:06:00.678505",
"environment_variables": {},
"exception": null,
"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2020-11-19T11:05:52.222063",
"version": "2.1.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| 0047/233/47233040.ipynb | s3://data-agents/kaggle-outputs/sharded/010_00047.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"execution": {
"iopub.execute_input": "2020-11-19T11:09:23.364704Z",
"iopub.status.busy": "2020-11-19T11:09:23.363938Z",
"iopub.status.idle": "2020-11-19T11:09:25.029545Z",
"shell.execute_reply": "2020-11-19T11:09:25.028743Z"
},
"papermill": {
"duration": 1.690168,
"end_time": "2020-11-19T11:09:25.029678",
"exception": false,
"start_time": "2020-11-19T11:09:23.339510",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"import json\n",
"import os\n",
"import numpy as np\n",
"import pandas as pd\n",
"from tqdm import tqdm\n",
"from textblob import TextBlob, Word\n",
"from string import punctuation\n",
"import re"
]
},
{
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"source": [
"data = {\"review_id\":[],\"business_id\":[],\"text\":[]}\n",
"\n",
"with open('../input/yelp-dataset/yelp_academic_dataset_review.json') as f:\n",
" for line in tqdm(f):\n",
" review = json.loads(line)\n",
" data['review_id'].append(review['review_id'])\n",
" data['business_id'].append(review['business_id'])\n",
" data['text'].append(review['text'])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:11:28.719946Z",
"iopub.status.busy": "2020-11-19T11:11:27.919518Z",
"iopub.status.idle": "2020-11-19T11:11:33.848032Z",
"shell.execute_reply": "2020-11-19T11:11:33.847359Z"
},
"papermill": {
"duration": 6.600666,
"end_time": "2020-11-19T11:11:33.848156",
"exception": false,
"start_time": "2020-11-19T11:11:27.247490",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(8021122, 3)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>review_id</th>\n",
" <th>business_id</th>\n",
" <th>text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>xQY8N_XvtGbearJ5X4QryQ</td>\n",
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" <td>I am actually horrified this place is still in...</td>\n",
" </tr>\n",
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" <th>2</th>\n",
" <td>LG2ZaYiOgpr2DK_90pYjNw</td>\n",
" <td>HQl28KMwrEKHqhFrrDqVNQ</td>\n",
" <td>I love Deagan's. I do. I really do. The atmosp...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>i6g_oA9Yf9Y31qt0wibXpw</td>\n",
" <td>5JxlZaqCnk1MnbgRirs40Q</td>\n",
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" <tr>\n",
" <th>4</th>\n",
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" <td>IS4cv902ykd8wj1TR0N3-A</td>\n",
" <td>Oh happy day, finally have a Canes near my cas...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" review_id business_id \\\n",
"0 xQY8N_XvtGbearJ5X4QryQ -MhfebM0QIsKt87iDN-FNw \n",
"1 UmFMZ8PyXZTY2QcwzsfQYA lbrU8StCq3yDfr-QMnGrmQ \n",
"2 LG2ZaYiOgpr2DK_90pYjNw HQl28KMwrEKHqhFrrDqVNQ \n",
"3 i6g_oA9Yf9Y31qt0wibXpw 5JxlZaqCnk1MnbgRirs40Q \n",
"4 6TdNDKywdbjoTkizeMce8A IS4cv902ykd8wj1TR0N3-A \n",
"\n",
" text \n",
"0 As someone who has worked with many museums, I... \n",
"1 I am actually horrified this place is still in... \n",
"2 I love Deagan's. I do. I really do. The atmosp... \n",
"3 Dismal, lukewarm, defrosted-tasting \"TexMex\" g... \n",
"4 Oh happy day, finally have a Canes near my cas... "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(data)\n",
"print(df.shape)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:11:34.831199Z",
"iopub.status.busy": "2020-11-19T11:11:34.830484Z",
"iopub.status.idle": "2020-11-19T11:11:39.351008Z",
"shell.execute_reply": "2020-11-19T11:11:39.351886Z"
},
"papermill": {
"duration": 5.014915,
"end_time": "2020-11-19T11:11:39.352137",
"exception": false,
"start_time": "2020-11-19T11:11:34.337222",
"status": "completed"
},
"tags": []
},
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"text": [
"\n"
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"source": [
"data1 = {\"name\":[],\"business_id\":[],\"city\":[],\"categories\":[]}\n",
"\n",
"with open('../input/yelp-dataset/yelp_academic_dataset_business.json') as f:\n",
" for line in tqdm(f):\n",
" business = json.loads(line)\n",
" data1['name'].append(business['name'])\n",
" data1['business_id'].append(business['business_id'])\n",
" data1['city'].append(business['city'])\n",
" data1['categories'].append(business['categories'])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:11:40.453068Z",
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},
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"end_time": "2020-11-19T11:11:40.596747",
"exception": false,
"start_time": "2020-11-19T11:11:39.893075",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(209393, 4)\n"
]
},
{
"data": {
"text/html": [
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>business_id</th>\n",
" <th>city</th>\n",
" <th>categories</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>The Range At Lake Norman</td>\n",
" <td>f9NumwFMBDn751xgFiRbNA</td>\n",
" <td>Cornelius</td>\n",
" <td>Active Life, Gun/Rifle Ranges, Guns & Ammo, Sh...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Carlos Santo, NMD</td>\n",
" <td>Yzvjg0SayhoZgCljUJRF9Q</td>\n",
" <td>Scottsdale</td>\n",
" <td>Health & Medical, Fitness & Instruction, Yoga,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Felinus</td>\n",
" <td>XNoUzKckATkOD1hP6vghZg</td>\n",
" <td>Montreal</td>\n",
" <td>Pets, Pet Services, Pet Groomers</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Nevada House of Hose</td>\n",
" <td>6OAZjbxqM5ol29BuHsil3w</td>\n",
" <td>North Las Vegas</td>\n",
" <td>Hardware Stores, Home Services, Building Suppl...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>USE MY GUY SERVICES LLC</td>\n",
" <td>51M2Kk903DFYI6gnB5I6SQ</td>\n",
" <td>Mesa</td>\n",
" <td>Home Services, Plumbing, Electricians, Handyma...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name business_id city \\\n",
"0 The Range At Lake Norman f9NumwFMBDn751xgFiRbNA Cornelius \n",
"1 Carlos Santo, NMD Yzvjg0SayhoZgCljUJRF9Q Scottsdale \n",
"2 Felinus XNoUzKckATkOD1hP6vghZg Montreal \n",
"3 Nevada House of Hose 6OAZjbxqM5ol29BuHsil3w North Las Vegas \n",
"4 USE MY GUY SERVICES LLC 51M2Kk903DFYI6gnB5I6SQ Mesa \n",
"\n",
" categories \n",
"0 Active Life, Gun/Rifle Ranges, Guns & Ammo, Sh... \n",
"1 Health & Medical, Fitness & Instruction, Yoga,... \n",
"2 Pets, Pet Services, Pet Groomers \n",
"3 Hardware Stores, Home Services, Building Suppl... \n",
"4 Home Services, Plumbing, Electricians, Handyma... "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1 = pd.DataFrame(data1)\n",
"print(df1.shape)\n",
"df1.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:11:44.595030Z",
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},
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"df.dropna(inplace=True)\n",
"df1.dropna(inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:11:46.919876Z",
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"start_time": "2020-11-19T11:11:46.382218",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"restaurants_business = df1[df1.categories.str.contains(\"Restaurants\")]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:11:48.186722Z",
"iopub.status.busy": "2020-11-19T11:11:48.185956Z",
"iopub.status.idle": "2020-11-19T11:11:48.195271Z",
"shell.execute_reply": "2020-11-19T11:11:48.194692Z"
},
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"Toronto 8679\n",
"Las Vegas 6931\n",
"Phoenix 4217\n",
"Montréal 4097\n",
"Calgary 3117\n",
" ... \n",
"Leslieville 1\n",
"St. Joseph 1\n",
"Plum Boro 1\n",
"Sutersville 1\n",
"Pont-Viau 1\n",
"Name: city, Length: 813, dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"restaurants_business['city'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:11:49.232031Z",
"iopub.status.busy": "2020-11-19T11:11:49.230999Z",
"iopub.status.idle": "2020-11-19T11:11:49.237234Z",
"shell.execute_reply": "2020-11-19T11:11:49.236521Z"
},
"papermill": {
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"exception": false,
"start_time": "2020-11-19T11:11:48.704886",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"Toronto_rest = restaurants_business[restaurants_business.city == \"Toronto\"]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:11:50.815379Z",
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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"\n",
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" }\n",
"</style>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>business_id</th>\n",
" <th>city</th>\n",
" <th>categories</th>\n",
" <th>review_id</th>\n",
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" <th>0</th>\n",
" <td>Gourmet Burger Company</td>\n",
" <td>rVBPQdeayMYht4Uv_FOLHg</td>\n",
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" <td>We've eaten here a few times now, and have bee...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Gourmet Burger Company</td>\n",
" <td>rVBPQdeayMYht4Uv_FOLHg</td>\n",
" <td>Toronto</td>\n",
" <td>Restaurants, Burgers, Food</td>\n",
" <td>0bNG5M5QxuYft_-RmpPpvg</td>\n",
" <td>Good food. Lots of burger options that do not...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Gourmet Burger Company</td>\n",
" <td>rVBPQdeayMYht4Uv_FOLHg</td>\n",
" <td>Toronto</td>\n",
" <td>Restaurants, Burgers, Food</td>\n",
" <td>qox22w34mX_Pg3iWwYHefA</td>\n",
" <td>Love this place. I come here with my family ab...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Gourmet Burger Company</td>\n",
" <td>rVBPQdeayMYht4Uv_FOLHg</td>\n",
" <td>Toronto</td>\n",
" <td>Restaurants, Burgers, Food</td>\n",
" <td>npWeJRHL79S4q4phJI47TA</td>\n",
" <td>Make no mistake: this is a fast food joint. Ho...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Gourmet Burger Company</td>\n",
" <td>rVBPQdeayMYht4Uv_FOLHg</td>\n",
" <td>Toronto</td>\n",
" <td>Restaurants, Burgers, Food</td>\n",
" <td>cV7ocrxE6BMyiIKnEbGsOg</td>\n",
" <td>What happened? Your burgers use to be the best...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name business_id city \\\n",
"0 Gourmet Burger Company rVBPQdeayMYht4Uv_FOLHg Toronto \n",
"1 Gourmet Burger Company rVBPQdeayMYht4Uv_FOLHg Toronto \n",
"2 Gourmet Burger Company rVBPQdeayMYht4Uv_FOLHg Toronto \n",
"3 Gourmet Burger Company rVBPQdeayMYht4Uv_FOLHg Toronto \n",
"4 Gourmet Burger Company rVBPQdeayMYht4Uv_FOLHg Toronto \n",
"\n",
" categories review_id \\\n",
"0 Restaurants, Burgers, Food OobdounFYjkAbIDsVUNwRw \n",
"1 Restaurants, Burgers, Food 0bNG5M5QxuYft_-RmpPpvg \n",
"2 Restaurants, Burgers, Food qox22w34mX_Pg3iWwYHefA \n",
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"\n",
" text \n",
"0 We've eaten here a few times now, and have bee... \n",
"1 Good food. Lots of burger options that do not... \n",
"2 Love this place. I come here with my family ab... \n",
"3 Make no mistake: this is a fast food joint. Ho... \n",
"4 What happened? Your burgers use to be the best... "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"finaldf = Toronto_rest.merge(df,on=\"business_id\",how=\"inner\")\n",
"finaldf.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:11:55.426302Z",
"iopub.status.busy": "2020-11-19T11:11:55.425508Z",
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},
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"Pai Northern Thai Kitchen 2834\n",
"Banh Mi Boys 1747\n",
"KINKA IZAKAYA ORIGINAL 1618\n",
"Khao San Road 1603\n",
"Seven Lives Tacos Y Mariscos 1317\n",
" ... \n",
"Telepizza 3\n",
"Super Zamani 3\n",
"YMCA Cafe at Metro Hall 3\n",
"Toranj Resto-Bar 3\n",
"Hakka Masala Restaurant 3\n",
"Name: name, Length: 7061, dtype: int64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"finaldf['name'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:11:56.535841Z",
"iopub.status.busy": "2020-11-19T11:11:56.535061Z",
"iopub.status.idle": "2020-11-19T11:11:56.689163Z",
"shell.execute_reply": "2020-11-19T11:11:56.688393Z"
},
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"start_time": "2020-11-19T11:11:55.951036",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"final_reviews = finaldf[finaldf.name==\"Pai Northern Thai Kitchen\"]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:11:57.723924Z",
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"outputs": [
{
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" <td>Toronto</td>\n",
" <td>Restaurants, Thai, Specialty Food, Food, Ethni...</td>\n",
" <td>6KdX2qVeoB5M261y8AW49Q</td>\n",
" <td>Toronto's Thai scene has really heated up to a...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>130071</th>\n",
" <td>Pai Northern Thai Kitchen</td>\n",
" <td>r_BrIgzYcwo1NAuG9dLbpg</td>\n",
" <td>Toronto</td>\n",
" <td>Restaurants, Thai, Specialty Food, Food, Ethni...</td>\n",
" <td>Wcyh_N-lXKSD2tNDxYmRPQ</td>\n",
" <td>It's a great place to go for Thai food, defini...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name business_id city \\\n",
"130067 Pai Northern Thai Kitchen r_BrIgzYcwo1NAuG9dLbpg Toronto \n",
"130068 Pai Northern Thai Kitchen r_BrIgzYcwo1NAuG9dLbpg Toronto \n",
"130069 Pai Northern Thai Kitchen r_BrIgzYcwo1NAuG9dLbpg Toronto \n",
"130070 Pai Northern Thai Kitchen r_BrIgzYcwo1NAuG9dLbpg Toronto \n",
"130071 Pai Northern Thai Kitchen r_BrIgzYcwo1NAuG9dLbpg Toronto \n",
"\n",
" categories \\\n",
"130067 Restaurants, Thai, Specialty Food, Food, Ethni... \n",
"130068 Restaurants, Thai, Specialty Food, Food, Ethni... \n",
"130069 Restaurants, Thai, Specialty Food, Food, Ethni... \n",
"130070 Restaurants, Thai, Specialty Food, Food, Ethni... \n",
"130071 Restaurants, Thai, Specialty Food, Food, Ethni... \n",
"\n",
" review_id \\\n",
"130067 rnTiV8YnySTqTVTT35_opg \n",
"130068 -NRF4XKtXAhMqoZIUEFf-Q \n",
"130069 imKA9jAQpKNCzhacXQ6NRQ \n",
"130070 6KdX2qVeoB5M261y8AW49Q \n",
"130071 Wcyh_N-lXKSD2tNDxYmRPQ \n",
"\n",
" text \n",
"130067 I've been to all of Regular's restaurants in T... \n",
"130068 I think they have the best Thai food in Toront... \n",
"130069 Great service and food. The sangria was excell... \n",
"130070 Toronto's Thai scene has really heated up to a... \n",
"130071 It's a great place to go for Thai food, defini... "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final_reviews.head()"
]
},
{
"cell_type": "code",
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},
"outputs": [
{
"data": {
"text/plain": [
"(2834, 6)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final_reviews.shape"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:11:59.827286Z",
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"\"I've been to all of Regular's restaurants in Toronto, and this is by far my favourite. I always order either their pad thai and grilled pork with sticky rice on the side. Atmosphere is nice and I enjoy its decor. That took me long enough to write a review! I've already been here at least 3 times this year alone and it's only April!\\n\\nI remember the first time I went last year. I heard a tweet as my friend poured water, and we thought we were hearing things. Eventually we figured out it's the bottle! How cute and amusing. I highly recommend this place.\""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final_reviews[\"text\"].iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:12:00.865369Z",
"iopub.status.busy": "2020-11-19T11:12:00.862600Z",
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},
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"end_time": "2020-11-19T11:12:00.868723",
"exception": false,
"start_time": "2020-11-19T11:12:00.345841",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"review = final_reviews.reset_index().text"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:12:01.920203Z",
"iopub.status.busy": "2020-11-19T11:12:01.919382Z",
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"shell.execute_reply": "2020-11-19T11:12:01.923610Z"
},
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"end_time": "2020-11-19T11:12:01.924316",
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"0 I've been to all of Regular's restaurants in T...\n",
"1 I think they have the best Thai food in Toront...\n",
"2 Great service and food. The sangria was excell...\n",
"3 Toronto's Thai scene has really heated up to a...\n",
"4 It's a great place to go for Thai food, defini...\n",
" ... \n",
"2829 I lucked into this gem unplanned and was so gl...\n",
"2830 Pad Thai and Thai iced tea was bomb, the curry...\n",
"2831 A true lover of Thai food this restaurant blew...\n",
"2832 Funky, cool, and delicious! We loved our visit...\n",
"2833 My family visited Pai on a Saturday night, the...\n",
"Name: text, Length: 2834, dtype: object"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"review"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": false,
"start_time": "2020-11-19T11:12:02.432632",
"status": "running"
},
"tags": []
},
"outputs": [],
"source": [
"def clean_sentence(sentence):\n",
" sentence = re.sub(r\"(?:\\@|https?\\://)\\S+|\\n+\", \"\", sentence.lower())\n",
" sent = TextBlob(sentence)\n",
" sent.correct()\n",
" clean = \"\"\n",
" for sentence in sent.sentences: \n",
" words = sentence.words\n",
" words = [''.join(c for c in s if c not in punctuation) for s in words]\n",
" words = [s for s in words if s]\n",
" clean += \" \".join(words)\n",
" clean += \". \"\n",
" return clean\n",
"\n",
"result = [clean_sentence(x) for x in review]\n",
"result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"sentiment_scores = []\n",
"for sentence in result:\n",
" line = TextBlob(sentence)\n",
" sentiment_scores.append(line.sentiment.polarity)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
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"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"comments = TextBlob(' '.join(result))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
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"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"cleaned = list()\n",
"for phrase in comments.noun_phrases:\n",
" count = 0\n",
" for word in phrase.split():\n",
" if len(word) <= 2 or (not Word(word).definitions):\n",
" count += 1\n",
" if count < len(phrase.split())*0.4:\n",
" cleaned.append(phrase)\n",
"len(cleaned)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
},
"papermill": {
"duration": null,
"end_time": null,
"environment_variables": {},
"exception": null,
"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2020-11-19T11:09:18.791061",
"version": "2.1.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
} | 0047/233/47233239.ipynb | s3://data-agents/kaggle-outputs/sharded/010_00047.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"execution": {
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/kaggle/input/email-spam-dataset/Email spam.csv\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load\n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the read-only \"../input/\" directory\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
"\n",
"import os\n",
"for dirname, _, filenames in os.walk('/kaggle/input'):\n",
" for filename in filenames:\n",
" print(os.path.join(dirname, filename))\n",
"\n",
"# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
"# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
"_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
"execution": {
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},
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"exception": false,
"start_time": "2020-11-19T11:10:48.802217",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import regex as re\n",
"import sqlite3"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:10:48.889979Z",
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>text</th>\n",
" <th>spam</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Subject: naturally irresistible your corporate...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Subject: the stock trading gunslinger fanny i...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Subject: unbelievable new homes made easy im ...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Subject: 4 color printing special request add...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Subject: do not have money , get software cds ...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" text spam\n",
"0 Subject: naturally irresistible your corporate... 1\n",
"1 Subject: the stock trading gunslinger fanny i... 1\n",
"2 Subject: unbelievable new homes made easy im ... 1\n",
"3 Subject: 4 color printing special request add... 1\n",
"4 Subject: do not have money , get software cds ... 1"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df=pd.read_csv(\"../input/email-spam-dataset/Email spam.csv\")\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:10:49.085239Z",
"iopub.status.busy": "2020-11-19T11:10:49.084496Z",
"iopub.status.idle": "2020-11-19T11:10:49.113519Z",
"shell.execute_reply": "2020-11-19T11:10:49.112875Z"
},
"papermill": {
"duration": 0.052192,
"end_time": "2020-11-19T11:10:49.113646",
"exception": false,
"start_time": "2020-11-19T11:10:49.061454",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(5728, 2)\n",
"(5695, 2)\n"
]
}
],
"source": [
"print(df.shape)\n",
"\n",
"df=df.drop_duplicates()\n",
"\n",
"print(df.shape)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:10:49.157475Z",
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},
"papermill": {
"duration": 0.036799,
"end_time": "2020-11-19T11:10:49.167768",
"exception": false,
"start_time": "2020-11-19T11:10:49.130969",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>text</th>\n",
" <th>spam</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Subject: naturally irresistible your corporate...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Subject: the stock trading gunslinger fanny i...</td>\n",
" <td>1</td>\n",
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" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Subject: 4 color printing special request add...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Subject: do not have money , get software cds ...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" text spam\n",
"0 Subject: naturally irresistible your corporate... 1\n",
"1 Subject: the stock trading gunslinger fanny i... 1\n",
"2 Subject: unbelievable new homes made easy im ... 1\n",
"3 Subject: 4 color printing special request add... 1\n",
"4 Subject: do not have money , get software cds ... 1"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = df.reset_index(inplace = False)[['text','spam']]\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.017647,
"end_time": "2020-11-19T11:10:49.203591",
"exception": false,
"start_time": "2020-11-19T11:10:49.185944",
"status": "completed"
},
"tags": []
},
"source": [
"# Data cleaning"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:10:49.249092Z",
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},
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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" index text spam\n",
"0 0 subject naturally irresistible your corporate ... 1\n",
"1 1 subject the stock trading gunslinger fanny is ... 1\n",
"2 2 subject unbelievable new homes made easy im wa... 1"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clean_desc = []\n",
"for w in range(len(df.text)):\n",
" desc = df['text'][w].lower()\n",
" \n",
" #remove punctuation\n",
" desc = re.sub('[^a-zA-Z]', ' ', desc)\n",
" \n",
" #remove tags\n",
" desc=re.sub(\"</?.*?>\",\" <> \",desc)\n",
" \n",
" #remove digits and special chars\n",
" desc=re.sub(\"(\\\\d|\\\\W)+\",\" \",desc)\n",
" \n",
" clean_desc.append(desc)\n",
"#assign the cleaned descriptions to the data frame\n",
"df['text'] = clean_desc\n",
"df = df.reset_index() \n",
"df.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.018285,
"end_time": "2020-11-19T11:10:52.939492",
"exception": false,
"start_time": "2020-11-19T11:10:52.921207",
"status": "completed"
},
"tags": []
},
"source": [
"# Creating word embedding"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:10:52.982740Z",
"iopub.status.busy": "2020-11-19T11:10:52.981956Z",
"iopub.status.idle": "2020-11-19T11:10:55.740891Z",
"shell.execute_reply": "2020-11-19T11:10:55.740116Z"
},
"papermill": {
"duration": 2.783002,
"end_time": "2020-11-19T11:10:55.741019",
"exception": false,
"start_time": "2020-11-19T11:10:52.958017",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<5695x33688 sparse matrix of type '<class 'numpy.int64'>'\n",
"\twith 660304 stored elements in Compressed Sparse Row format>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.feature_extraction.text import CountVectorizer\n",
"\n",
"text_vec=CountVectorizer().fit_transform(df['text'])\n",
"text_vec"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.018969,
"end_time": "2020-11-19T11:10:55.779558",
"exception": false,
"start_time": "2020-11-19T11:10:55.760589",
"status": "completed"
},
"tags": []
},
"source": [
"# Train test split"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:10:55.824612Z",
"iopub.status.busy": "2020-11-19T11:10:55.823797Z",
"iopub.status.idle": "2020-11-19T11:10:55.885028Z",
"shell.execute_reply": "2020-11-19T11:10:55.884248Z"
},
"papermill": {
"duration": 0.086245,
"end_time": "2020-11-19T11:10:55.885160",
"exception": false,
"start_time": "2020-11-19T11:10:55.798915",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"xtrain,xtest,ytrain,ytest=train_test_split(text_vec,df['spam'],test_size=0.4,random_state=44,shuffle=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.019198,
"end_time": "2020-11-19T11:10:55.924098",
"exception": false,
"start_time": "2020-11-19T11:10:55.904900",
"status": "completed"
},
"tags": []
},
"source": [
"# Gradient Boosting classifier"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:10:55.968898Z",
"iopub.status.busy": "2020-11-19T11:10:55.968119Z",
"iopub.status.idle": "2020-11-19T11:10:56.169576Z",
"shell.execute_reply": "2020-11-19T11:10:56.168753Z"
},
"papermill": {
"duration": 0.225988,
"end_time": "2020-11-19T11:10:56.169710",
"exception": false,
"start_time": "2020-11-19T11:10:55.943722",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"from sklearn import ensemble\n",
"\n",
"classifier=ensemble.GradientBoostingClassifier(n_estimators=100,learning_rate=0.5,max_depth=6)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:10:56.214710Z",
"iopub.status.busy": "2020-11-19T11:10:56.213974Z",
"iopub.status.idle": "2020-11-19T11:11:16.454723Z",
"shell.execute_reply": "2020-11-19T11:11:16.454110Z"
},
"papermill": {
"duration": 20.265176,
"end_time": "2020-11-19T11:11:16.454849",
"exception": false,
"start_time": "2020-11-19T11:10:56.189673",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"GradientBoostingClassifier(learning_rate=0.5, max_depth=6)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"classifier.fit(xtrain,ytrain)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:11:16.501410Z",
"iopub.status.busy": "2020-11-19T11:11:16.500622Z",
"iopub.status.idle": "2020-11-19T11:11:16.531995Z",
"shell.execute_reply": "2020-11-19T11:11:16.531185Z"
},
"papermill": {
"duration": 0.05687,
"end_time": "2020-11-19T11:11:16.532121",
"exception": false,
"start_time": "2020-11-19T11:11:16.475251",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"97.41000877963126"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import accuracy_score\n",
"\n",
"ypred=classifier.predict(xtest)\n",
"\n",
"acc=accuracy_score(ypred,ytest)*100\n",
"acc"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:11:16.579948Z",
"iopub.status.busy": "2020-11-19T11:11:16.579183Z",
"iopub.status.idle": "2020-11-19T11:11:16.996760Z",
"shell.execute_reply": "2020-11-19T11:11:16.995918Z"
},
"papermill": {
"duration": 0.443772,
"end_time": "2020-11-19T11:11:16.996898",
"exception": false,
"start_time": "2020-11-19T11:11:16.553126",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f9d6a09e690>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from sklearn.metrics import confusion_matrix\n",
"import seaborn as sns\n",
"\n",
"sns.heatmap(confusion_matrix(ypred,ytest),annot=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.024731,
"end_time": "2020-11-19T11:11:17.048190",
"exception": false,
"start_time": "2020-11-19T11:11:17.023459",
"status": "completed"
},
"tags": []
},
"source": [
"# Sentiment analysis"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:11:17.106169Z",
"iopub.status.busy": "2020-11-19T11:11:17.105461Z",
"iopub.status.idle": "2020-11-19T11:11:28.986871Z",
"shell.execute_reply": "2020-11-19T11:11:28.986076Z"
},
"papermill": {
"duration": 11.913335,
"end_time": "2020-11-19T11:11:28.986995",
"exception": false,
"start_time": "2020-11-19T11:11:17.073660",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>index</th>\n",
" <th>text</th>\n",
" <th>spam</th>\n",
" <th>tb_Pol</th>\n",
" <th>tb_Subj</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>subject naturally irresistible your corporate ...</td>\n",
" <td>1</td>\n",
" <td>0.296607</td>\n",
" <td>0.546905</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>subject the stock trading gunslinger fanny is ...</td>\n",
" <td>1</td>\n",
" <td>0.160317</td>\n",
" <td>0.562698</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>subject unbelievable new homes made easy im wa...</td>\n",
" <td>1</td>\n",
" <td>0.040229</td>\n",
" <td>0.480581</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" index text spam tb_Pol \\\n",
"0 0 subject naturally irresistible your corporate ... 1 0.296607 \n",
"1 1 subject the stock trading gunslinger fanny is ... 1 0.160317 \n",
"2 2 subject unbelievable new homes made easy im wa... 1 0.040229 \n",
"\n",
" tb_Subj \n",
"0 0.546905 \n",
"1 0.562698 \n",
"2 0.480581 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from textblob import TextBlob\n",
"\n",
"#load the descriptions into textblob\n",
"email_blob = [TextBlob(text) for text in df['text']]\n",
"#add the sentiment metrics to the dataframe\n",
"df['tb_Pol'] = [b.sentiment.polarity for b in email_blob]\n",
"df['tb_Subj'] = [b.sentiment.subjectivity for b in email_blob]\n",
"#show dataframe\n",
"df.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.023326,
"end_time": "2020-11-19T11:11:29.033953",
"exception": false,
"start_time": "2020-11-19T11:11:29.010627",
"status": "completed"
},
"tags": []
},
"source": [
"**tb_pol indicatin the polarity,tells it is positive if it is >0**"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.022847,
"end_time": "2020-11-19T11:11:29.080108",
"exception": false,
"start_time": "2020-11-19T11:11:29.057261",
"status": "completed"
},
"tags": []
},
"source": [
"### Here the positive class is spam"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"papermill": {
"duration": 0.022667,
"end_time": "2020-11-19T11:11:29.125909",
"exception": false,
"start_time": "2020-11-19T11:11:29.103242",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
},
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"end_time": "2020-11-19T11:11:29.257878",
"environment_variables": {},
"exception": null,
"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2020-11-19T11:10:43.652416",
"version": "2.1.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| 0047/233/47233307.ipynb | s3://data-agents/kaggle-outputs/sharded/010_00047.jsonl.gz |
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.059897,
"end_time": "2020-11-19T11:15:18.180386",
"exception": false,
"start_time": "2020-11-19T11:15:18.120489",
"status": "completed"
},
"tags": []
},
"source": [
"# Introduction :An interpretation of the data with respect to the Movie.\n",
" \n",
" \n",
"In this notebook , the titanic dataset is explored with relevance to the perspectives that were been projected in the famous James Cameron movie Titanic based on the tragic story of the RMS Titanic , in the cold Atlantic Ocean. Now , lets not get deep in the story , which might lead us nowhere near to the solution. But if one may question , how the story has thrown light on this dataset , my answer is , the certain facts that were shown in the movie , which will be supported by the graphs as we go through the notebook ,helped me in Feature selection. \n",
"\n",
"***Bon Voyage !!***\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.058388,
"end_time": "2020-11-19T11:15:18.295734",
"exception": false,
"start_time": "2020-11-19T11:15:18.237346",
"status": "completed"
},
"tags": []
},
"source": [
"# Playing with data :\n",
"\n",
"As the title suggests , I have no experience of how things are in Kaggle , thus the \"playing\" is sort of a , playing with borrowed toys."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"execution": {
"iopub.execute_input": "2020-11-19T11:15:18.417156Z",
"iopub.status.busy": "2020-11-19T11:15:18.416022Z",
"iopub.status.idle": "2020-11-19T11:15:18.421633Z",
"shell.execute_reply": "2020-11-19T11:15:18.420974Z"
},
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"end_time": "2020-11-19T11:15:18.421759",
"exception": false,
"start_time": "2020-11-19T11:15:18.352535",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/kaggle/input/titanic/train.csv\n",
"/kaggle/input/titanic/gender_submission.csv\n",
"/kaggle/input/titanic/test.csv\n"
]
}
],
"source": [
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"import os\n",
"for dirname, _, filenames in os.walk('/kaggle/input'):\n",
" for filename in filenames:\n",
" print(os.path.join(dirname, filename))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
"_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
"execution": {
"iopub.execute_input": "2020-11-19T11:15:18.544289Z",
"iopub.status.busy": "2020-11-19T11:15:18.543475Z",
"iopub.status.idle": "2020-11-19T11:15:19.667042Z",
"shell.execute_reply": "2020-11-19T11:15:19.665398Z"
},
"papermill": {
"duration": 1.187595,
"end_time": "2020-11-19T11:15:19.667254",
"exception": false,
"start_time": "2020-11-19T11:15:18.479659",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import sklearn\n",
"import seaborn as sb"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.069411,
"end_time": "2020-11-19T11:15:19.795695",
"exception": false,
"start_time": "2020-11-19T11:15:19.726284",
"status": "completed"
},
"tags": []
},
"source": [
"* Loading the datasets"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:19.927404Z",
"iopub.status.busy": "2020-11-19T11:15:19.925918Z",
"iopub.status.idle": "2020-11-19T11:15:19.974173Z",
"shell.execute_reply": "2020-11-19T11:15:19.973416Z"
},
"papermill": {
"duration": 0.11935,
"end_time": "2020-11-19T11:15:19.974314",
"exception": false,
"start_time": "2020-11-19T11:15:19.854964",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"0 0 A/5 21171 7.2500 NaN S \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S \n",
"3 0 113803 53.1000 C123 S \n",
"4 0 373450 8.0500 NaN S "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train = pd.read_csv('/kaggle/input/titanic/train.csv')\n",
"test = pd.read_csv('/kaggle/input/titanic/test.csv')\n",
"train.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.066307,
"end_time": "2020-11-19T11:15:20.111187",
"exception": false,
"start_time": "2020-11-19T11:15:20.044880",
"status": "completed"
},
"tags": []
},
"source": [
"* Combining the train and test datasets."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:20.251727Z",
"iopub.status.busy": "2020-11-19T11:15:20.250693Z",
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},
"papermill": {
"duration": 0.074362,
"end_time": "2020-11-19T11:15:20.253992",
"exception": false,
"start_time": "2020-11-19T11:15:20.179630",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"combine = [train,test]"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.058162,
"end_time": "2020-11-19T11:15:20.372246",
"exception": false,
"start_time": "2020-11-19T11:15:20.314084",
"status": "completed"
},
"tags": []
},
"source": [
"* Checking the error or missing value percentages of each feature."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:20.500649Z",
"iopub.status.busy": "2020-11-19T11:15:20.498797Z",
"iopub.status.idle": "2020-11-19T11:15:20.510634Z",
"shell.execute_reply": "2020-11-19T11:15:20.509878Z"
},
"papermill": {
"duration": 0.08011,
"end_time": "2020-11-19T11:15:20.510759",
"exception": false,
"start_time": "2020-11-19T11:15:20.430649",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PassengerId 0.0\n",
"Survived 0.0\n",
"Pclass 0.0\n",
"Name 0.0\n",
"Sex 0.0\n",
"Age 19.865319865319865\n",
"SibSp 0.0\n",
"Parch 0.0\n",
"Ticket 0.0\n",
"Fare 0.0\n",
"Cabin 77.10437710437711\n",
"Embarked 0.2244668911335578\n"
]
}
],
"source": [
"for c in train.columns:\n",
" print(c, str(100*train[c].isnull().sum()/len(train)))\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.058619,
"end_time": "2020-11-19T11:15:20.628746",
"exception": false,
"start_time": "2020-11-19T11:15:20.570127",
"status": "completed"
},
"tags": []
},
"source": [
"We see there are missing values in *Age* , *Cabin* and *Embarked* data. As the values of Cabin are in string , we are going to disregard it. According to the movie and common human sentiments , we will use the age values."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:20.755070Z",
"iopub.status.busy": "2020-11-19T11:15:20.754082Z",
"iopub.status.idle": "2020-11-19T11:15:20.757490Z",
"shell.execute_reply": "2020-11-19T11:15:20.756729Z"
},
"papermill": {
"duration": 0.070007,
"end_time": "2020-11-19T11:15:20.757619",
"exception": false,
"start_time": "2020-11-19T11:15:20.687612",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"train['Age'] = train['Age'].fillna(train['Age'].mean())"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.058842,
"end_time": "2020-11-19T11:15:20.876337",
"exception": false,
"start_time": "2020-11-19T11:15:20.817495",
"status": "completed"
},
"tags": []
},
"source": [
"Here , I have replaced the missing values of age with the average of all the ages of the people who boarded the Titanic.\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:21.027002Z",
"iopub.status.busy": "2020-11-19T11:15:21.023283Z",
"iopub.status.idle": "2020-11-19T11:15:21.031964Z",
"shell.execute_reply": "2020-11-19T11:15:21.033386Z"
},
"papermill": {
"duration": 0.090592,
"end_time": "2020-11-19T11:15:21.033607",
"exception": false,
"start_time": "2020-11-19T11:15:20.943015",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PassengerId 0.0\n",
"Survived 0.0\n",
"Pclass 0.0\n",
"Name 0.0\n",
"Sex 0.0\n",
"Age 0.0\n",
"SibSp 0.0\n",
"Parch 0.0\n",
"Ticket 0.0\n",
"Fare 0.0\n",
"Cabin 77.10437710437711\n",
"Embarked 0.2244668911335578\n"
]
}
],
"source": [
"for c in train.columns:\n",
" print(c, str(100*train[c].isnull().sum()/len(train)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.060633,
"end_time": "2020-11-19T11:15:21.161017",
"exception": false,
"start_time": "2020-11-19T11:15:21.100384",
"status": "completed"
},
"tags": []
},
"source": [
"Now we can see that there are no missing values in Age column."
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.072005,
"end_time": "2020-11-19T11:15:21.294021",
"exception": false,
"start_time": "2020-11-19T11:15:21.222016",
"status": "completed"
},
"tags": []
},
"source": [
"* Checking how many people from differnt class and sex had survived."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:21.450915Z",
"iopub.status.busy": "2020-11-19T11:15:21.445997Z",
"iopub.status.idle": "2020-11-19T11:15:21.459198Z",
"shell.execute_reply": "2020-11-19T11:15:21.458446Z"
},
"papermill": {
"duration": 0.098777,
"end_time": "2020-11-19T11:15:21.459341",
"exception": false,
"start_time": "2020-11-19T11:15:21.360564",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Pclass Survived\n",
"0 1 0.629630\n",
"1 2 0.472826\n",
"2 3 0.242363\n",
" Sex Survived\n",
"0 female 0.742038\n",
"1 male 0.188908\n"
]
}
],
"source": [
"dependencies_sex = train[['Sex', 'Survived']].groupby(['Sex'],as_index=False).mean()\n",
"dependencies_Pclass = train[['Pclass', 'Survived']].groupby(['Pclass'],as_index=False).mean()\n",
"\n",
"print(dependencies_Pclass)\n",
"print(dependencies_sex)"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.060159,
"end_time": "2020-11-19T11:15:21.586306",
"exception": false,
"start_time": "2020-11-19T11:15:21.526147",
"status": "completed"
},
"tags": []
},
"source": [
"The large difference in the numbers shows that these two features - *Pclass* and *Sex* , have influence on the survival of the boarded passengers. "
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.059939,
"end_time": "2020-11-19T11:15:21.706513",
"exception": false,
"start_time": "2020-11-19T11:15:21.646574",
"status": "completed"
},
"tags": []
},
"source": [
"# Graphs that support my feature selection based on the movie."
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.060392,
"end_time": "2020-11-19T11:15:21.827292",
"exception": false,
"start_time": "2020-11-19T11:15:21.766900",
"status": "completed"
},
"tags": []
},
"source": [
"* Starting off with the survivor count\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:21.965280Z",
"iopub.status.busy": "2020-11-19T11:15:21.958951Z",
"iopub.status.idle": "2020-11-19T11:15:22.177600Z",
"shell.execute_reply": "2020-11-19T11:15:22.176729Z"
},
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"duration": 0.289009,
"end_time": "2020-11-19T11:15:22.177737",
"exception": false,
"start_time": "2020-11-19T11:15:21.888728",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fde3304d150>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sb.set_style('whitegrid')\n",
"sb.countplot(x='Survived',data=train,palette='YlOrBr')\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.070209,
"end_time": "2020-11-19T11:15:22.317294",
"exception": false,
"start_time": "2020-11-19T11:15:22.247085",
"status": "completed"
},
"tags": []
},
"source": [
"* Survivor Count based on Sex "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:22.495938Z",
"iopub.status.busy": "2020-11-19T11:15:22.475052Z",
"iopub.status.idle": "2020-11-19T11:15:22.688165Z",
"shell.execute_reply": "2020-11-19T11:15:22.688726Z"
},
"papermill": {
"duration": 0.297341,
"end_time": "2020-11-19T11:15:22.688919",
"exception": false,
"start_time": "2020-11-19T11:15:22.391578",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fde32e9a350>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sb.set_style('whitegrid')\n",
"sb.countplot(x='Survived',hue='Sex',data=train,palette='rocket')\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.064402,
"end_time": "2020-11-19T11:15:22.819277",
"exception": false,
"start_time": "2020-11-19T11:15:22.754875",
"status": "completed"
},
"tags": []
},
"source": [
"* Survivor Count based on Class of Passenger"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:22.986513Z",
"iopub.status.busy": "2020-11-19T11:15:22.976316Z",
"iopub.status.idle": "2020-11-19T11:15:23.244278Z",
"shell.execute_reply": "2020-11-19T11:15:23.243438Z"
},
"papermill": {
"duration": 0.358485,
"end_time": "2020-11-19T11:15:23.244448",
"exception": false,
"start_time": "2020-11-19T11:15:22.885963",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fde32e034d0>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sb.set_style('whitegrid')\n",
"sb.countplot(x='Survived',hue='Pclass',data=train,palette='icefire')\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.079806,
"end_time": "2020-11-19T11:15:23.413600",
"exception": false,
"start_time": "2020-11-19T11:15:23.333794",
"status": "completed"
},
"tags": []
},
"source": [
"* Survival Count based on Age"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:23.572806Z",
"iopub.status.busy": "2020-11-19T11:15:23.566395Z",
"iopub.status.idle": "2020-11-19T11:15:24.594369Z",
"shell.execute_reply": "2020-11-19T11:15:24.593525Z"
},
"papermill": {
"duration": 1.106432,
"end_time": "2020-11-19T11:15:24.594503",
"exception": false,
"start_time": "2020-11-19T11:15:23.488071",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x7fde32da34d0>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x216 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"g = sb.FacetGrid(train, col='Survived')\n",
"g.map(plt.hist, 'Age', bins=20)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.065697,
"end_time": "2020-11-19T11:15:24.726504",
"exception": false,
"start_time": "2020-11-19T11:15:24.660807",
"status": "completed"
},
"tags": []
},
"source": [
"* Splitting Titles from Names "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:24.874357Z",
"iopub.status.busy": "2020-11-19T11:15:24.873460Z",
"iopub.status.idle": "2020-11-19T11:15:24.877312Z",
"shell.execute_reply": "2020-11-19T11:15:24.876564Z"
},
"papermill": {
"duration": 0.084675,
"end_time": "2020-11-19T11:15:24.877445",
"exception": false,
"start_time": "2020-11-19T11:15:24.792770",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"for dats in combine:\n",
" dats['Title'] = dats.Name.str.extract('([A-Za-z]+)\\.',expand=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.076625,
"end_time": "2020-11-19T11:15:25.021860",
"exception": false,
"start_time": "2020-11-19T11:15:24.945235",
"status": "completed"
},
"tags": []
},
"source": [
"* Grouping various Titles under Rare Category."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:25.181522Z",
"iopub.status.busy": "2020-11-19T11:15:25.180694Z",
"iopub.status.idle": "2020-11-19T11:15:25.184519Z",
"shell.execute_reply": "2020-11-19T11:15:25.183774Z"
},
"papermill": {
"duration": 0.089428,
"end_time": "2020-11-19T11:15:25.184661",
"exception": false,
"start_time": "2020-11-19T11:15:25.095233",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"for dataset in combine:\n",
" dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',\n",
" \t'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
"\n",
" dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
" dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
" dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:25.342922Z",
"iopub.status.busy": "2020-11-19T11:15:25.341823Z",
"iopub.status.idle": "2020-11-19T11:15:25.347330Z",
"shell.execute_reply": "2020-11-19T11:15:25.346595Z"
},
"papermill": {
"duration": 0.09394,
"end_time": "2020-11-19T11:15:25.347454",
"exception": false,
"start_time": "2020-11-19T11:15:25.253514",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" <th>Title</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>Mr</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" <td>Mrs</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>Miss</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" <td>Mrs</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>Mr</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked Title \n",
"0 0 A/5 21171 7.2500 NaN S Mr \n",
"1 0 PC 17599 71.2833 C85 C Mrs \n",
"2 0 STON/O2. 3101282 7.9250 NaN S Miss \n",
"3 0 113803 53.1000 C123 S Mrs \n",
"4 0 373450 8.0500 NaN S Mr "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.067282,
"end_time": "2020-11-19T11:15:25.481959",
"exception": false,
"start_time": "2020-11-19T11:15:25.414677",
"status": "completed"
},
"tags": []
},
"source": [
"* Label Encoding Titles. "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:25.631208Z",
"iopub.status.busy": "2020-11-19T11:15:25.630064Z",
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"end_time": "2020-11-19T11:15:25.633987",
"exception": false,
"start_time": "2020-11-19T11:15:25.550888",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n",
"for dataset in combine:\n",
" dataset['Title'] = dataset['Title'].map(title_mapping)\n",
" dataset['Title'] = dataset['Title'].fillna(0)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:25.807002Z",
"iopub.status.busy": "2020-11-19T11:15:25.805875Z",
"iopub.status.idle": "2020-11-19T11:15:25.811130Z",
"shell.execute_reply": "2020-11-19T11:15:25.810353Z"
},
"papermill": {
"duration": 0.102333,
"end_time": "2020-11-19T11:15:25.811273",
"exception": false,
"start_time": "2020-11-19T11:15:25.708940",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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"<style scoped>\n",
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" vertical-align: middle;\n",
" }\n",
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" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" <th>Title</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked Title \n",
"0 0 A/5 21171 7.2500 NaN S 1 \n",
"1 0 PC 17599 71.2833 C85 C 3 \n",
"2 0 STON/O2. 3101282 7.9250 NaN S 2 \n",
"3 0 113803 53.1000 C123 S 3 \n",
"4 0 373450 8.0500 NaN S 1 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.069026,
"end_time": "2020-11-19T11:15:25.948704",
"exception": false,
"start_time": "2020-11-19T11:15:25.879678",
"status": "completed"
},
"tags": []
},
"source": [
"Now we can see that under the Column Title , instead of *Mr.* , *Mrs.* , *Miss* , etc. , there are numbers 1-5 , representing the 5 different Titles."
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.120282,
"end_time": "2020-11-19T11:15:26.140476",
"exception": false,
"start_time": "2020-11-19T11:15:26.020194",
"status": "completed"
},
"tags": []
},
"source": [
"* Here we see how the survival of the passengers depend on the *Sex* and *Title*. ( There are 2 rows with Title as 5 , which represents the Rare category , and it implies that females of high classes were favoured over all )"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:26.288349Z",
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"shell.execute_reply": "2020-11-19T11:15:26.302913Z"
},
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"end_time": "2020-11-19T11:15:26.303851",
"exception": false,
"start_time": "2020-11-19T11:15:26.209449",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Title</th>\n",
" <th>Sex</th>\n",
" <th>Survived</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>0.156673</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>female</td>\n",
" <td>0.702703</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>0.793651</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>male</td>\n",
" <td>0.575000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>female</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5</td>\n",
" <td>male</td>\n",
" <td>0.250000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Title Sex Survived\n",
"0 1 male 0.156673\n",
"1 2 female 0.702703\n",
"2 3 female 0.793651\n",
"3 4 male 0.575000\n",
"4 5 female 1.000000\n",
"5 5 male 0.250000"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"title_dependencies=train[['Title','Survived','Sex']].groupby(['Title','Sex'],as_index=False).mean()\n",
"\n",
"title_dependencies"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.069823,
"end_time": "2020-11-19T11:15:26.443455",
"exception": false,
"start_time": "2020-11-19T11:15:26.373632",
"status": "completed"
},
"tags": []
},
"source": [
"# Selecting the final features !!"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.068778,
"end_time": "2020-11-19T11:15:26.581335",
"exception": false,
"start_time": "2020-11-19T11:15:26.512557",
"status": "completed"
},
"tags": []
},
"source": [
"According to the movie , the Children and the Women , obviously in the decrasing order of their class , were allowed to board the life boats first. Remember the scene were the crew were shouthing , *\" Women and Children First !!\"*. Thus the selection of the *Sex* , *Age* and the *Pclass* as a valid feature for the prediction. As we have clearly seen that people of high social stature had a cent percent chance of survival , thus the *Title* column is also a valid feature. \n",
"\n",
"Now , the *Parch* and *SibSp* feature , which represents parents and children, and siblings and spouse , is also a valid feature , because parents and children were not to be separated , and siblings and spouse too. Thus , those passengers who were travelling alone were left behind. \n",
"\n",
"The reason behind dropping , *Cabin* , *Embarked* and *Ticket* feature is , that in the data *Ticket* , there are too many mixed data type , and that doesnt help us figure out the survival of the passengers ; in the *Cabin* data , there were too many missing values , although it might have been useful , because the cabin position had an inportant role in the survival as the lower cabins were filled with water first ; the *Embarked* data has nothing to do with the survival , because the port of embarkation did not determine the fate of the passengers, although , upon speculations one may find patterns in the data. \n",
"\n",
"What we can see is , that the prejudice of people of certain social backgrounds determined the survival of the passengers. "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:26.839718Z",
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"end_time": "2020-11-19T11:15:26.845101",
"exception": false,
"start_time": "2020-11-19T11:15:26.652368",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Survived Pclass Sex Age SibSp Parch Fare Title\n",
"0 0 3 male 22.0 1 0 7.2500 1\n",
"1 1 1 female 38.0 1 0 71.2833 3\n",
"2 1 3 female 26.0 0 0 7.9250 2\n",
"3 1 1 female 35.0 1 0 53.1000 3\n",
"4 0 3 male 35.0 0 0 8.0500 1\n",
" Survived Pclass Sex Age SibSp Parch Fare Title\n",
"0 0 3 0 22.0 1 0 7.2500 1\n",
"1 1 1 1 38.0 1 0 71.2833 3\n",
"2 1 3 1 26.0 0 0 7.9250 2\n",
"3 1 1 1 35.0 1 0 53.1000 3\n",
"4 0 3 0 35.0 0 0 8.0500 1\n",
" Pclass Sex Age SibSp Parch Fare Title\n",
"0 3 0 34.5 0 0 7.8292 1\n",
"1 3 1 47.0 1 0 7.0000 3\n",
"2 2 0 62.0 0 0 9.6875 1\n",
"3 3 0 27.0 0 0 8.6625 1\n",
"4 3 1 22.0 1 1 12.2875 3\n"
]
}
],
"source": [
"train = train.drop(['Name', 'PassengerId', 'Cabin', 'Embarked','Ticket'], axis=1)\n",
"test = test.drop(['Name', 'PassengerId', 'Cabin', 'Embarked','Ticket'], axis=1)\n",
"\n",
"combine=[train,test]\n",
"\n",
"print(train.head())\n",
"\n",
"for dataset in combine:\n",
" dataset['Sex'] = dataset['Sex'].map( {'female': 1, 'male': 0}).astype(int)\n",
"\n",
"print(train.head())\n",
"print (test.head())\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.082141,
"end_time": "2020-11-19T11:15:27.005587",
"exception": false,
"start_time": "2020-11-19T11:15:26.923446",
"status": "completed"
},
"tags": []
},
"source": [
"# Splitting Training data "
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:27.171736Z",
"iopub.status.busy": "2020-11-19T11:15:27.170865Z",
"iopub.status.idle": "2020-11-19T11:15:27.299307Z",
"shell.execute_reply": "2020-11-19T11:15:27.298488Z"
},
"papermill": {
"duration": 0.213867,
"end_time": "2020-11-19T11:15:27.299457",
"exception": false,
"start_time": "2020-11-19T11:15:27.085590",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test , Y_train, Y_test = train_test_split(train.drop(['Survived'],axis=1),train['Survived'],test_size=0.10,random_state=None)"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.072675,
"end_time": "2020-11-19T11:15:27.445622",
"exception": false,
"start_time": "2020-11-19T11:15:27.372947",
"status": "completed"
},
"tags": []
},
"source": [
"# Applying Logistic Regression :"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:27.599608Z",
"iopub.status.busy": "2020-11-19T11:15:27.598631Z",
"iopub.status.idle": "2020-11-19T11:15:27.707588Z",
"shell.execute_reply": "2020-11-19T11:15:27.706693Z"
},
"papermill": {
"duration": 0.189339,
"end_time": "2020-11-19T11:15:27.707732",
"exception": false,
"start_time": "2020-11-19T11:15:27.518393",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn import metrics"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:27.858486Z",
"iopub.status.busy": "2020-11-19T11:15:27.857577Z",
"iopub.status.idle": "2020-11-19T11:15:27.924754Z",
"shell.execute_reply": "2020-11-19T11:15:27.923038Z"
},
"papermill": {
"duration": 0.1465,
"end_time": "2020-11-19T11:15:27.924936",
"exception": false,
"start_time": "2020-11-19T11:15:27.778436",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
"\n",
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
"Please also refer to the documentation for alternative solver options:\n",
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
" extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
]
},
{
"data": {
"text/plain": [
"LogisticRegression()"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"modelLR= LogisticRegression()\n",
"modelLR.fit(X_train,Y_train)\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:28.096341Z",
"iopub.status.busy": "2020-11-19T11:15:28.095414Z",
"iopub.status.idle": "2020-11-19T11:15:28.100786Z",
"shell.execute_reply": "2020-11-19T11:15:28.100025Z"
},
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"end_time": "2020-11-19T11:15:28.100968",
"exception": false,
"start_time": "2020-11-19T11:15:28.006884",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"Y_pred_log=modelLR.predict(X_test)\n",
"acc_LR = metrics.accuracy_score(Y_test, Y_pred_log)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:28.263750Z",
"iopub.status.busy": "2020-11-19T11:15:28.262922Z",
"iopub.status.idle": "2020-11-19T11:15:28.267279Z",
"shell.execute_reply": "2020-11-19T11:15:28.266327Z"
},
"papermill": {
"duration": 0.086107,
"end_time": "2020-11-19T11:15:28.267422",
"exception": false,
"start_time": "2020-11-19T11:15:28.181315",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 1 0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 1 0 0\n",
" 1 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0\n",
" 1 0 1 0 0 0 0 1 1 0 0 1 1 0 0 0]\n",
"We see that Logistic regression gives an Accuracy of 88.88888888888889 % on the traing set.\n"
]
}
],
"source": [
"print(Y_pred_log)\n",
"print(\"We see that Logistic regression gives an Accuracy of \",acc_LR*100,\"% on the traing set.\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.075105,
"end_time": "2020-11-19T11:15:28.415355",
"exception": false,
"start_time": "2020-11-19T11:15:28.340250",
"status": "completed"
},
"tags": []
},
"source": [
"# Applying Decision Tree:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:28.571697Z",
"iopub.status.busy": "2020-11-19T11:15:28.570929Z",
"iopub.status.idle": "2020-11-19T11:15:28.710356Z",
"shell.execute_reply": "2020-11-19T11:15:28.711028Z"
},
"papermill": {
"duration": 0.219931,
"end_time": "2020-11-19T11:15:28.711202",
"exception": false,
"start_time": "2020-11-19T11:15:28.491271",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"from sklearn.tree import DecisionTreeClassifier"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:28.869873Z",
"iopub.status.busy": "2020-11-19T11:15:28.868970Z",
"iopub.status.idle": "2020-11-19T11:15:28.879409Z",
"shell.execute_reply": "2020-11-19T11:15:28.878778Z"
},
"papermill": {
"duration": 0.095899,
"end_time": "2020-11-19T11:15:28.879549",
"exception": false,
"start_time": "2020-11-19T11:15:28.783650",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"dtree = DecisionTreeClassifier()\n",
"dtree.fit(X_train, Y_train)\n",
"y_pred_tree = dtree.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:29.045512Z",
"iopub.status.busy": "2020-11-19T11:15:29.044443Z",
"iopub.status.idle": "2020-11-19T11:15:29.048209Z",
"shell.execute_reply": "2020-11-19T11:15:29.048805Z"
},
"papermill": {
"duration": 0.088457,
"end_time": "2020-11-19T11:15:29.048994",
"exception": false,
"start_time": "2020-11-19T11:15:28.960537",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 1 0 1 1 0 0 0 0 0 0 1 1 0 0\n",
" 1 0 0 1 0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0\n",
" 1 0 0 0 0 0 0 1 1 1 0 0 1 0 1 0]\n",
"We see that Decision Tree gives an Accuracy of 85.55555555555556 % on the traing set.\n"
]
}
],
"source": [
"acc_DT = metrics.accuracy_score(y_pred_tree, Y_test)\n",
"print(y_pred_tree)\n",
"print(\"We see that Decision Tree gives an Accuracy of \",acc_DT*100,\"% on the traing set.\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.07333,
"end_time": "2020-11-19T11:15:29.197185",
"exception": false,
"start_time": "2020-11-19T11:15:29.123855",
"status": "completed"
},
"tags": []
},
"source": [
"# Applying Ramndom Forest :"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:29.350928Z",
"iopub.status.busy": "2020-11-19T11:15:29.350079Z",
"iopub.status.idle": "2020-11-19T11:15:29.387716Z",
"shell.execute_reply": "2020-11-19T11:15:29.387065Z"
},
"papermill": {
"duration": 0.116357,
"end_time": "2020-11-19T11:15:29.387909",
"exception": false,
"start_time": "2020-11-19T11:15:29.271552",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestClassifier"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:29.545470Z",
"iopub.status.busy": "2020-11-19T11:15:29.544653Z",
"iopub.status.idle": "2020-11-19T11:15:29.838215Z",
"shell.execute_reply": "2020-11-19T11:15:29.837325Z"
},
"papermill": {
"duration": 0.376271,
"end_time": "2020-11-19T11:15:29.838354",
"exception": false,
"start_time": "2020-11-19T11:15:29.462083",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"rforest = RandomForestClassifier()\n",
"rforest.fit(X_train, Y_train)\n",
"y_pred_forest = rforest.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:29.996329Z",
"iopub.status.busy": "2020-11-19T11:15:29.995430Z",
"iopub.status.idle": "2020-11-19T11:15:30.001346Z",
"shell.execute_reply": "2020-11-19T11:15:30.000680Z"
},
"papermill": {
"duration": 0.088442,
"end_time": "2020-11-19T11:15:30.001483",
"exception": false,
"start_time": "2020-11-19T11:15:29.913041",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 0 0 1 0 1 1 0 0 0 0 0 0 0 1 0 0\n",
" 1 0 0 1 0 0 1 1 0 1 0 0 0 0 0 1 0 1 0 1 0 1 1 0 0 0 1 0 0 1 0 0 0 0 1 0 0\n",
" 1 0 0 0 0 0 0 1 1 1 0 1 1 0 1 0]\n",
"We see that Random Forest classifier gives an Accuracy of 87.77777777777777 % on the traing set.\n"
]
}
],
"source": [
"acc_RF = metrics.accuracy_score(y_pred_forest, Y_test)\n",
"print(y_pred_forest)\n",
"print(\"We see that Random Forest classifier gives an Accuracy of \",acc_RF*100,\"% on the traing set.\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.079416,
"end_time": "2020-11-19T11:15:30.163234",
"exception": false,
"start_time": "2020-11-19T11:15:30.083818",
"status": "completed"
},
"tags": []
},
"source": [
"# Applying SVC :\n"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:30.326295Z",
"iopub.status.busy": "2020-11-19T11:15:30.325090Z",
"iopub.status.idle": "2020-11-19T11:15:30.328929Z",
"shell.execute_reply": "2020-11-19T11:15:30.329532Z"
},
"papermill": {
"duration": 0.08638,
"end_time": "2020-11-19T11:15:30.329713",
"exception": false,
"start_time": "2020-11-19T11:15:30.243333",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"from sklearn.svm import SVC"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:30.487466Z",
"iopub.status.busy": "2020-11-19T11:15:30.486675Z",
"iopub.status.idle": "2020-11-19T11:15:30.518218Z",
"shell.execute_reply": "2020-11-19T11:15:30.517550Z"
},
"papermill": {
"duration": 0.113943,
"end_time": "2020-11-19T11:15:30.518362",
"exception": false,
"start_time": "2020-11-19T11:15:30.404419",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"svc = SVC()\n",
"svc.fit(X_train, Y_train)\n",
"y_pred_svc = svc.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:30.675336Z",
"iopub.status.busy": "2020-11-19T11:15:30.674485Z",
"iopub.status.idle": "2020-11-19T11:15:30.677691Z",
"shell.execute_reply": "2020-11-19T11:15:30.679144Z"
},
"papermill": {
"duration": 0.086666,
"end_time": "2020-11-19T11:15:30.679331",
"exception": false,
"start_time": "2020-11-19T11:15:30.592665",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1\n",
" 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0\n",
" 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0]\n",
"We see that SVC classifier gives an Accuracy of 78.88888888888889 % on the traing set.\n"
]
}
],
"source": [
"acc_SVC = metrics.accuracy_score(y_pred_svc, Y_test)\n",
"print(y_pred_svc)\n",
"print(\"We see that SVC classifier gives an Accuracy of \",acc_SVC*100,\"% on the traing set.\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.074173,
"end_time": "2020-11-19T11:15:30.830082",
"exception": false,
"start_time": "2020-11-19T11:15:30.755909",
"status": "completed"
},
"tags": []
},
"source": [
"# Applying Gradient Boosting:"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:30.994452Z",
"iopub.status.busy": "2020-11-19T11:15:30.993510Z",
"iopub.status.idle": "2020-11-19T11:15:31.924042Z",
"shell.execute_reply": "2020-11-19T11:15:31.923157Z"
},
"papermill": {
"duration": 1.013574,
"end_time": "2020-11-19T11:15:31.924187",
"exception": false,
"start_time": "2020-11-19T11:15:30.910613",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"from xgboost import XGBClassifier"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:32.085290Z",
"iopub.status.busy": "2020-11-19T11:15:32.084020Z",
"iopub.status.idle": "2020-11-19T11:15:32.158278Z",
"shell.execute_reply": "2020-11-19T11:15:32.158977Z"
},
"papermill": {
"duration": 0.158285,
"end_time": "2020-11-19T11:15:32.159196",
"exception": false,
"start_time": "2020-11-19T11:15:32.000911",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"xgb=XGBClassifier()\n",
"xgb.fit(X_train,Y_train)\n",
"y_pred_xgb=xgb.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:32.392195Z",
"iopub.status.busy": "2020-11-19T11:15:32.391319Z",
"iopub.status.idle": "2020-11-19T11:15:32.398547Z",
"shell.execute_reply": "2020-11-19T11:15:32.399368Z"
},
"papermill": {
"duration": 0.12323,
"end_time": "2020-11-19T11:15:32.399563",
"exception": false,
"start_time": "2020-11-19T11:15:32.276333",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 0 0 1 0 1 1 0 0 0 0 0 0 0 1 1 0\n",
" 1 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 1 0 0 1 0 0 0 0 1 0 0\n",
" 1 0 0 0 0 0 0 1 1 0 0 0 1 0 1 0]\n",
"We see that XGB classifier gives an Accuracy of 87.77777777777777 % on the traing set.\n"
]
}
],
"source": [
"acc_XGB= metrics.accuracy_score(y_pred_xgb,Y_test)\n",
"print(y_pred_xgb)\n",
"print(\"We see that XGB classifier gives an Accuracy of \",acc_XGB*100,\"% on the traing set.\") "
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.092936,
"end_time": "2020-11-19T11:15:32.589946",
"exception": false,
"start_time": "2020-11-19T11:15:32.497010",
"status": "completed"
},
"tags": []
},
"source": [
"# Applying Artificial Neural Network:"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:32.757880Z",
"iopub.status.busy": "2020-11-19T11:15:32.756936Z",
"iopub.status.idle": "2020-11-19T11:15:39.032545Z",
"shell.execute_reply": "2020-11-19T11:15:39.031803Z"
},
"papermill": {
"duration": 6.360205,
"end_time": "2020-11-19T11:15:39.032690",
"exception": false,
"start_time": "2020-11-19T11:15:32.672485",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"import keras\n",
"from keras.layers import Dense\n",
"from keras.models import Sequential\n",
"from sklearn.metrics import classification_report"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.076036,
"end_time": "2020-11-19T11:15:39.186271",
"exception": false,
"start_time": "2020-11-19T11:15:39.110235",
"status": "completed"
},
"tags": []
},
"source": [
"Using sequential model to stack two ***32*** unit hidden layer , with ***1*** output layer."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:39.353223Z",
"iopub.status.busy": "2020-11-19T11:15:39.352362Z",
"iopub.status.idle": "2020-11-19T11:15:39.485727Z",
"shell.execute_reply": "2020-11-19T11:15:39.485017Z"
},
"papermill": {
"duration": 0.223096,
"end_time": "2020-11-19T11:15:39.485869",
"exception": false,
"start_time": "2020-11-19T11:15:39.262773",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"nn = Sequential()\n",
"nn.add(Dense(units= 32, activation = 'relu', input_dim=7, kernel_initializer=\"uniform\"))\n",
"nn.add(Dense(units= 32, activation = 'relu',kernel_initializer=\"uniform\"))\n",
"nn.add(Dense(units= 1, activation = 'sigmoid',kernel_initializer=\"uniform\"))\n",
"nn.compile(optimizer='adam',\n",
" loss='binary_crossentropy',\n",
" metrics=['accuracy'])\n"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:39.647959Z",
"iopub.status.busy": "2020-11-19T11:15:39.647143Z",
"iopub.status.idle": "2020-11-19T11:15:56.003462Z",
"shell.execute_reply": "2020-11-19T11:15:56.002106Z"
},
"papermill": {
"duration": 16.44136,
"end_time": "2020-11-19T11:15:56.003659",
"exception": false,
"start_time": "2020-11-19T11:15:39.562299",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0]\n"
]
}
],
"source": [
"nn.fit(X_train,Y_train, batch_size=32,epochs=500,verbose= 0)\n",
"\n",
"nn_pred = nn.predict(X_test)\n",
"nn_pred = [ 1 if y>=0.5 else 0 for y in nn_pred]\n",
"print(nn_pred)\n",
"\n",
"acc_NN = metrics.accuracy_score(Y_test, nn_pred)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:56.166590Z",
"iopub.status.busy": "2020-11-19T11:15:56.165563Z",
"iopub.status.idle": "2020-11-19T11:15:56.170162Z",
"shell.execute_reply": "2020-11-19T11:15:56.169076Z"
},
"papermill": {
"duration": 0.087431,
"end_time": "2020-11-19T11:15:56.170339",
"exception": false,
"start_time": "2020-11-19T11:15:56.082908",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"We can see that the neural network gives an Accuracy of 92.22222222222223 % on the training set.\n"
]
}
],
"source": [
"print(\"We can see that the neural network gives an Accuracy of \",acc_NN*100 , \"% on the training set.\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.077555,
"end_time": "2020-11-19T11:15:56.328272",
"exception": false,
"start_time": "2020-11-19T11:15:56.250717",
"status": "completed"
},
"tags": []
},
"source": [
"# Getting the best Classifier:"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:56.498772Z",
"iopub.status.busy": "2020-11-19T11:15:56.497656Z",
"iopub.status.idle": "2020-11-19T11:15:56.502679Z",
"shell.execute_reply": "2020-11-19T11:15:56.501714Z"
},
"papermill": {
"duration": 0.096035,
"end_time": "2020-11-19T11:15:56.502809",
"exception": false,
"start_time": "2020-11-19T11:15:56.406774",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Model Score\n",
"5 Artificial Neural Network 0.922222\n",
"0 Logistic Regression 0.888889\n",
"2 Random Forest 0.877778\n",
"4 Gradient Boosting Classifier 0.877778\n",
"1 Decision Tree 0.855556\n",
"3 Support Vector Machines 0.788889\n"
]
}
],
"source": [
"models = pd.DataFrame({\n",
" 'Model': ['Logistic Regression','Decision Tree','Random Forest','Support Vector Machines',\n",
" 'Gradient Boosting Classifier','Artificial Neural Network'],\n",
" 'Score': [acc_LR, acc_DT, acc_RF, \n",
" acc_SVC, acc_XGB, acc_NN]})\n",
"best_model=models.sort_values(by='Score', ascending=False)\n",
"print(best_model)"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.078024,
"end_time": "2020-11-19T11:15:56.661723",
"exception": false,
"start_time": "2020-11-19T11:15:56.583699",
"status": "completed"
},
"tags": []
},
"source": [
"* Fetching the best Model"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:56.825881Z",
"iopub.status.busy": "2020-11-19T11:15:56.824868Z",
"iopub.status.idle": "2020-11-19T11:15:56.829653Z",
"shell.execute_reply": "2020-11-19T11:15:56.828561Z"
},
"papermill": {
"duration": 0.089694,
"end_time": "2020-11-19T11:15:56.829788",
"exception": false,
"start_time": "2020-11-19T11:15:56.740094",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Artificial Neural Network\n"
]
}
],
"source": [
"best=best_model['Model'].iloc[0]\n",
"\n",
"print(str(best))"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.078922,
"end_time": "2020-11-19T11:15:56.988097",
"exception": false,
"start_time": "2020-11-19T11:15:56.909175",
"status": "completed"
},
"tags": []
},
"source": [
"# Fitting the test set on the Best Classifier:"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:57.167138Z",
"iopub.status.busy": "2020-11-19T11:15:57.165878Z",
"iopub.status.idle": "2020-11-19T11:15:57.176359Z",
"shell.execute_reply": "2020-11-19T11:15:57.175329Z"
},
"papermill": {
"duration": 0.107505,
"end_time": "2020-11-19T11:15:57.176547",
"exception": false,
"start_time": "2020-11-19T11:15:57.069042",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pclass 0.0\n",
"Sex 0.0\n",
"Age 20.574162679425836\n",
"SibSp 0.0\n",
"Parch 0.0\n",
"Fare 0.23923444976076555\n",
"Title 0.0\n",
"..............Before\n",
"\n",
"\n",
"Pclass 0.0\n",
"Sex 0.0\n",
"Age 0.0\n",
"SibSp 0.0\n",
"Parch 0.0\n",
"Fare 0.0\n",
"Title 0.0\n",
"..............After\n"
]
}
],
"source": [
"test.head()\n",
"for c in test.columns:\n",
" print(c, str(100*test[c].isnull().sum()/len(test)))\n",
"print(\"..............Before\") \n",
"\n",
"test['Age'] = test['Age'].fillna(test['Age'].mean())\n",
"test['Fare'] = test['Fare'].fillna(test['Fare'].mean())\n",
"print(\"\\n\")\n",
"for c in test.columns:\n",
" print(c, str(100*test[c].isnull().sum()/len(test)))\n",
"print(\"..............After\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.078833,
"end_time": "2020-11-19T11:15:57.336058",
"exception": false,
"start_time": "2020-11-19T11:15:57.257225",
"status": "completed"
},
"tags": []
},
"source": [
"***Selecting the Best Model and fitting it to the Test data :***"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:57.503366Z",
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"shell.execute_reply": "2020-11-19T11:16:14.475794Z"
},
"papermill": {
"duration": 17.060496,
"end_time": "2020-11-19T11:16:14.476039",
"exception": false,
"start_time": "2020-11-19T11:15:57.415543",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0]\n",
"Artificial Neural Network\n"
]
}
],
"source": [
"\n",
"if best == 'Logistic Regression':\n",
" modelLR.fit(train.drop(['Survived'],axis=1),train['Survived'])\n",
" test_pred=modelLR.predict(test)\n",
" print(test_pred)\n",
" print('Logistic Regression')\n",
" \n",
"if best == 'Decision Tree':\n",
" dtree.fit(train.drop(['Survived'],axis=1),train['Survived'])\n",
" test_pred=modelLR.predict(test)\n",
" print(test_pred)\n",
" print('Decision Tree')\n",
" \n",
"if best =='Random Forest':\n",
" rforest.fit(train.drop(['Survived'],axis=1),train['Survived'])\n",
" test_pred=rforest.predict(test)\n",
" print(test_pred)\n",
" print('Random Forest')\n",
" \n",
"if best == 'Support Vector Machine':\n",
" svc.fit(train.drop(['Survived'],axis=1),train['Survived'])\n",
" test_pred=svc.predict(test)\n",
" print(test_pred)\n",
" print('Support Vector Machines')\n",
"\n",
"if best == ' Gradient Boosting Classifier':\n",
" xgb.fit(train.drop(['Survived'],axis=1),train['Survived'])\n",
" test_pred=xgb.predict(test)\n",
" print(test_pred)\n",
" print('Gradient Boosting Classifier')\n",
" \n",
"if best == 'Artificial Neural Network':\n",
" nn.fit(train.drop(['Survived'],axis=1),train['Survived'], epochs=500 , verbose=0)\n",
" test_pred=nn.predict(test)\n",
" test_pred = [ 1 if y>=0.5 else 0 for y in test_pred]\n",
" print(test_pred)\n",
" print('Artificial Neural Network')\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.07905,
"end_time": "2020-11-19T11:16:14.645427",
"exception": false,
"start_time": "2020-11-19T11:16:14.566377",
"status": "completed"
},
"tags": []
},
"source": [
"**Fetching the Survival Data of the test set from the *gender_submission.csv* file .**"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:16:14.811578Z",
"iopub.status.busy": "2020-11-19T11:16:14.810742Z",
"iopub.status.idle": "2020-11-19T11:16:14.823395Z",
"shell.execute_reply": "2020-11-19T11:16:14.822634Z"
},
"papermill": {
"duration": 0.098537,
"end_time": "2020-11-19T11:16:14.823529",
"exception": false,
"start_time": "2020-11-19T11:16:14.724992",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Survived</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Survived\n",
"0 0\n",
"1 1\n",
"2 0\n",
"3 0\n",
"4 1"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_data=pd.read_csv('/kaggle/input/titanic/gender_submission.csv')\n",
"test_data = test_data.drop(['PassengerId'], axis=1)\n",
"\n",
"test_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:16:14.991693Z",
"iopub.status.busy": "2020-11-19T11:16:14.990707Z",
"iopub.status.idle": "2020-11-19T11:16:14.996311Z",
"shell.execute_reply": "2020-11-19T11:16:14.996989Z"
},
"papermill": {
"duration": 0.093908,
"end_time": "2020-11-19T11:16:14.997150",
"exception": false,
"start_time": "2020-11-19T11:16:14.903242",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Here we see that the test data has an Accuracy of 88.27751196172248 % \n"
]
}
],
"source": [
"test_data.values.tolist()\n",
"test_acc = metrics.accuracy_score(test_data, test_pred)\n",
"\n",
"print(\"Here we see that the test data has an Accuracy of \",test_acc*100,\"% \")"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.081419,
"end_time": "2020-11-19T11:16:15.160809",
"exception": false,
"start_time": "2020-11-19T11:16:15.079390",
"status": "completed"
},
"tags": []
},
"source": [
"* Adding the predictions to the Test set"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:16:15.337749Z",
"iopub.status.busy": "2020-11-19T11:16:15.336933Z",
"iopub.status.idle": "2020-11-19T11:16:15.346908Z",
"shell.execute_reply": "2020-11-19T11:16:15.347472Z"
},
"papermill": {
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"end_time": "2020-11-19T11:16:15.347651",
"exception": false,
"start_time": "2020-11-19T11:16:15.248580",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"test_predict = pd.DataFrame(test_pred, columns= ['Survived'])\n",
"test_new= pd.read_csv('/kaggle/input/titanic/test.csv')\n",
"new_test = pd.concat([test_new, test_predict], axis=1, join='inner')"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:16:15.532861Z",
"iopub.status.busy": "2020-11-19T11:16:15.531734Z",
"iopub.status.idle": "2020-11-19T11:16:15.536445Z",
"shell.execute_reply": "2020-11-19T11:16:15.535763Z"
},
"papermill": {
"duration": 0.107067,
"end_time": "2020-11-19T11:16:15.536567",
"exception": false,
"start_time": "2020-11-19T11:16:15.429500",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" <th>Survived</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>892</td>\n",
" <td>3</td>\n",
" <td>Kelly, Mr. James</td>\n",
" <td>male</td>\n",
" <td>34.5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>330911</td>\n",
" <td>7.8292</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>893</td>\n",
" <td>3</td>\n",
" <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
" <td>female</td>\n",
" <td>47.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>363272</td>\n",
" <td>7.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>894</td>\n",
" <td>2</td>\n",
" <td>Myles, Mr. Thomas Francis</td>\n",
" <td>male</td>\n",
" <td>62.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>240276</td>\n",
" <td>9.6875</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>895</td>\n",
" <td>3</td>\n",
" <td>Wirz, Mr. Albert</td>\n",
" <td>male</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>315154</td>\n",
" <td>8.6625</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>896</td>\n",
" <td>3</td>\n",
" <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
" <td>female</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3101298</td>\n",
" <td>12.2875</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Pclass Name Sex \\\n",
"0 892 3 Kelly, Mr. James male \n",
"1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n",
"2 894 2 Myles, Mr. Thomas Francis male \n",
"3 895 3 Wirz, Mr. Albert male \n",
"4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n",
"\n",
" Age SibSp Parch Ticket Fare Cabin Embarked Survived \n",
"0 34.5 0 0 330911 7.8292 NaN Q 0 \n",
"1 47.0 1 0 363272 7.0000 NaN S 0 \n",
"2 62.0 0 0 240276 9.6875 NaN Q 0 \n",
"3 27.0 0 0 315154 8.6625 NaN S 0 \n",
"4 22.0 1 1 3101298 12.2875 NaN S 0 "
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_test.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.081306,
"end_time": "2020-11-19T11:16:15.700970",
"exception": false,
"start_time": "2020-11-19T11:16:15.619664",
"status": "completed"
},
"tags": []
},
"source": [
"The new Test set with Survived Column"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.081571,
"end_time": "2020-11-19T11:16:15.865553",
"exception": false,
"start_time": "2020-11-19T11:16:15.783982",
"status": "completed"
},
"tags": []
},
"source": [
"* Submitting the predictions"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:16:16.039522Z",
"iopub.status.busy": "2020-11-19T11:16:16.038475Z",
"iopub.status.idle": "2020-11-19T11:16:16.058451Z",
"shell.execute_reply": "2020-11-19T11:16:16.057568Z"
},
"papermill": {
"duration": 0.111273,
"end_time": "2020-11-19T11:16:16.058588",
"exception": false,
"start_time": "2020-11-19T11:16:15.947315",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"submit=new_test[['PassengerId','Survived']]\n",
"submit.to_csv('predictions.csv',index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.08272,
"end_time": "2020-11-19T11:16:16.225423",
"exception": false,
"start_time": "2020-11-19T11:16:16.142703",
"status": "completed"
},
"tags": []
},
"source": [
"# *The End* "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
},
"papermill": {
"duration": 63.451561,
"end_time": "2020-11-19T11:16:16.418010",
"environment_variables": {},
"exception": null,
"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2020-11-19T11:15:12.966449",
"version": "2.1.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| 0047/233/47233545.ipynb | s3://data-agents/kaggle-outputs/sharded/010_00047.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"execution": {
"iopub.execute_input": "2020-11-19T11:15:27.694293Z",
"iopub.status.busy": "2020-11-19T11:15:27.693440Z",
"iopub.status.idle": "2020-11-19T11:15:27.696868Z",
"shell.execute_reply": "2020-11-19T11:15:27.696133Z"
},
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"duration": 0.032112,
"end_time": "2020-11-19T11:15:27.697005",
"exception": false,
"start_time": "2020-11-19T11:15:27.664893",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load\n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the read-only \"../input/\" directory\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
"\n",
"import os\n",
"\n",
"# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
"# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
"_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
"execution": {
"iopub.execute_input": "2020-11-19T11:15:27.739144Z",
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},
"papermill": {
"duration": 0.227598,
"end_time": "2020-11-19T11:15:27.942847",
"exception": false,
"start_time": "2020-11-19T11:15:27.715249",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"import os\n",
"files = os.listdir(\"/kaggle/input/alif-ner/alif/drive/My Drive/NER/Alif\")\n",
"txt_files = [I for I in files if I.endswith(\".txt\")]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:27.987524Z",
"iopub.status.busy": "2020-11-19T11:15:27.986419Z",
"iopub.status.idle": "2020-11-19T11:15:27.990000Z",
"shell.execute_reply": "2020-11-19T11:15:27.989286Z"
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"duration": 0.029196,
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"def is_good_label(label):\n",
" valued_labels = [\"ART\" , \"Event\" , \"Language\" , \"Location\" , \"Miscellaneous\" , \"NHFC\" , \n",
" \"NORP\" , 'Organization' , \"Person\" , \"Product\" , \"Time\" ]\n",
" for I in valued_labels:\n",
" if I in label:\n",
" return True\n",
" return label == 'O'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:28.032040Z",
"iopub.status.busy": "2020-11-19T11:15:28.031246Z",
"iopub.status.idle": "2020-11-19T11:15:28.034971Z",
"shell.execute_reply": "2020-11-19T11:15:28.034222Z"
},
"papermill": {
"duration": 0.026573,
"end_time": "2020-11-19T11:15:28.035099",
"exception": false,
"start_time": "2020-11-19T11:15:28.008526",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"from random import shuffle"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:28.090162Z",
"iopub.status.busy": "2020-11-19T11:15:28.089392Z",
"iopub.status.idle": "2020-11-19T11:15:58.794238Z",
"shell.execute_reply": "2020-11-19T11:15:58.793506Z"
},
"papermill": {
"duration": 30.740762,
"end_time": "2020-11-19T11:15:58.794375",
"exception": false,
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"X = []\n",
"y = []\n",
"error = 0\n",
"\n",
"for file in txt_files:\n",
" file = open(\"/kaggle/input/alif-ner/alif/drive/My Drive/NER/Alif/%s\" % (file))\n",
" rows = [I.strip() for I in file.readlines()]\n",
" flag = True\n",
" X.append([])\n",
" y.append([])\n",
"\n",
" for row in rows:\n",
" tokens = row.strip().split(\" \")\n",
" if len(row) == 0:\n",
" X.append([])\n",
" y.append([])\n",
" flag = True\n",
" else:\n",
" if flag:\n",
" if len(tokens) == 1 or not is_good_label(tokens[-1]):\n",
" flag = False\n",
" error += 1\n",
" X.pop(-1)\n",
" y.pop(-1)\n",
" else:\n",
" for I in range(len(tokens) - 1):\n",
" X[-1].append(tokens[I])\n",
" y[-1].append(tokens[-1].split(\"-\")[-1])\n",
"\n",
" assert len(X) == len(y)\n",
"\n",
" for M in range(len(X)):\n",
" assert len(X[M]) == len(y[M])\n",
"\n",
"out_lists = []\n",
"\n",
"for row in range(len(X)):\n",
" out_lists.append([X[row] , y[row]])\n",
" \n",
"shuffle(out_lists)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:15:58.837256Z",
"iopub.status.busy": "2020-11-19T11:15:58.836503Z",
"iopub.status.idle": "2020-11-19T11:16:00.917568Z",
"shell.execute_reply": "2020-11-19T11:16:00.916893Z"
},
"papermill": {
"duration": 2.104812,
"end_time": "2020-11-19T11:16:00.917709",
"exception": false,
"start_time": "2020-11-19T11:15:58.812897",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cloning into 'NCRFpp'...\r\n",
"remote: Enumerating objects: 118, done.\u001b[K\r\n",
"remote: Counting objects: 100% (118/118), done.\u001b[K\r\n",
"remote: Compressing objects: 100% (85/85), done.\u001b[K\r\n",
"remote: Total 776 (delta 77), reused 62 (delta 31), pack-reused 658\u001b[K\r\n",
"Receiving objects: 100% (776/776), 6.91 MiB | 17.43 MiB/s, done.\r\n",
"Resolving deltas: 100% (477/477), done.\r\n"
]
}
],
"source": [
"!git clone https://github.com/KerenzaDoxolodeo/NCRFpp/"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:16:00.971860Z",
"iopub.status.busy": "2020-11-19T11:16:00.970767Z",
"iopub.status.idle": "2020-11-19T11:16:00.977030Z",
"shell.execute_reply": "2020-11-19T11:16:00.976182Z"
},
"papermill": {
"duration": 0.035898,
"end_time": "2020-11-19T11:16:00.977161",
"exception": false,
"start_time": "2020-11-19T11:16:00.941263",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/kaggle/working/NCRFpp\n"
]
}
],
"source": [
"%cd NCRFpp"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:16:01.030905Z",
"iopub.status.busy": "2020-11-19T11:16:01.029814Z",
"iopub.status.idle": "2020-11-19T11:16:01.775467Z",
"shell.execute_reply": "2020-11-19T11:16:01.774645Z"
},
"papermill": {
"duration": 0.774767,
"end_time": "2020-11-19T11:16:01.775600",
"exception": false,
"start_time": "2020-11-19T11:16:01.000833",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LICENCE\t\t demo.decode.config main_parse.py sample_data\r\n",
"README.md\t demo.train.config model\t utils\r\n",
"demo.clf.config main.py\t readme\r\n"
]
}
],
"source": [
"!ls"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:16:01.831019Z",
"iopub.status.busy": "2020-11-19T11:16:01.829902Z",
"iopub.status.idle": "2020-11-19T11:16:02.577773Z",
"shell.execute_reply": "2020-11-19T11:16:02.577122Z"
},
"papermill": {
"duration": 0.778159,
"end_time": "2020-11-19T11:16:02.577905",
"exception": false,
"start_time": "2020-11-19T11:16:01.799746",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"!mkdir deo"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:16:02.649482Z",
"iopub.status.busy": "2020-11-19T11:16:02.639909Z",
"iopub.status.idle": "2020-11-19T11:16:03.436563Z",
"shell.execute_reply": "2020-11-19T11:16:03.435724Z"
},
"papermill": {
"duration": 0.834493,
"end_time": "2020-11-19T11:16:03.436727",
"exception": false,
"start_time": "2020-11-19T11:16:02.602234",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"file = open(\"deo/train.bmes\" , \"w\")\n",
"\n",
"for M in out_lists[:len(out_lists) * 4 // 5]:\n",
" last = None\n",
" for I in range(len(M[0])):\n",
" if M[1][I] == 'O':\n",
" file.write(\"%s O\\n\" % (M[0][I]))\n",
" elif M[1][I] == last:\n",
" file.write(\"%s I-%s\\n\" % (M[0][I] , M[1][I]))\n",
" else:\n",
" file.write(\"%s B-%s\\n\" % (M[0][I] , M[1][I]))\n",
" last = M[1][I]\n",
" file.write(\"\\n\")\n",
"file.close()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:16:03.500902Z",
"iopub.status.busy": "2020-11-19T11:16:03.497984Z",
"iopub.status.idle": "2020-11-19T11:16:03.688282Z",
"shell.execute_reply": "2020-11-19T11:16:03.687555Z"
},
"papermill": {
"duration": 0.226785,
"end_time": "2020-11-19T11:16:03.688423",
"exception": false,
"start_time": "2020-11-19T11:16:03.461638",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"file = open(\"deo/test.bmes\" , \"w\")\n",
"\n",
"for M in out_lists[len(out_lists) * 4 // 5:]:\n",
" last = None\n",
" for I in range(len(M[0])):\n",
" if M[1][I] == 'O':\n",
" file.write(\"%s O\\n\" % (M[0][I]))\n",
" elif M[1][I] == last:\n",
" file.write(\"%s I-%s\\n\" % (M[0][I] , M[1][I]))\n",
" else:\n",
" file.write(\"%s B-%s\\n\" % (M[0][I] , M[1][I]))\n",
" last = M[1][I]\n",
" file.write(\"\\n\")\n",
"file.close()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:16:03.745123Z",
"iopub.status.busy": "2020-11-19T11:16:03.744288Z",
"iopub.status.idle": "2020-11-19T11:16:03.748390Z",
"shell.execute_reply": "2020-11-19T11:16:03.747648Z"
},
"papermill": {
"duration": 0.0347,
"end_time": "2020-11-19T11:16:03.748520",
"exception": false,
"start_time": "2020-11-19T11:16:03.713820",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"abc\n",
"def\n",
"\n"
]
}
],
"source": [
"a = \"\"\"\n",
"abc\n",
"def\n",
"\"\"\"\n",
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:16:03.807356Z",
"iopub.status.busy": "2020-11-19T11:16:03.806329Z",
"iopub.status.idle": "2020-11-19T11:16:03.809898Z",
"shell.execute_reply": "2020-11-19T11:16:03.809134Z"
},
"papermill": {
"duration": 0.035832,
"end_time": "2020-11-19T11:16:03.810021",
"exception": false,
"start_time": "2020-11-19T11:16:03.774189",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"config_string = \"\"\"\n",
"train_dir=deo/train.bmes\n",
"dev_dir=deo/test.bmes\n",
"test_dir=deo/test.bmes\n",
"model_dir=deo/lstmcrf\n",
"\n",
"norm_word_emb=False\n",
"norm_char_emb=False\n",
"number_normalized=True\n",
"seg=True\n",
"word_emb_dim=50\n",
"char_emb_dim=30\n",
"\n",
"use_crf=True\n",
"use_char=True\n",
"word_seq_feature=LSTM\n",
"char_seq_feature=LSTM\n",
"\n",
"status=train\n",
"optimizer=SGD\n",
"iteration=5\n",
"batch_size=10\n",
"ave_batch_loss=False\n",
"\n",
"cnn_layer=4\n",
"char_hidden_dim=50\n",
"hidden_dim=200\n",
"dropout=0.5\n",
"lstm_layer=1\n",
"bilstm=True\n",
"learning_rate=0.015\n",
"lr_decay=0.05\n",
"momentum=0\n",
"l2=1e-8\n",
"\"\"\"\n",
"file = open(\"deo.train.config\" , \"w\")\n",
"file.write(config_string)\n",
"file.close()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:16:03.868942Z",
"iopub.status.busy": "2020-11-19T11:16:03.868102Z",
"iopub.status.idle": "2020-11-19T11:46:37.272723Z",
"shell.execute_reply": "2020-11-19T11:46:37.271908Z"
},
"papermill": {
"duration": 1833.436112,
"end_time": "2020-11-19T11:46:37.272855",
"exception": false,
"start_time": "2020-11-19T11:16:03.836743",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Seed num: 42\r\n",
"MODEL: train\r\n",
"Training model...\r\n",
"++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n",
"DATA SUMMARY START:\r\n",
" I/O:\r\n",
" Start Sequence Laebling task...\r\n",
" Tag scheme: BIO\r\n",
" Split token: ||| \r\n",
" MAX SENTENCE LENGTH: 250\r\n",
" MAX WORD LENGTH: -1\r\n",
" Number normalized: True\r\n",
" Word alphabet size: 24301\r\n",
" Char alphabet size: 207\r\n",
" Label alphabet size: 24\r\n",
" Word embedding dir: None\r\n",
" Char embedding dir: None\r\n",
" Word embedding size: 50\r\n",
" Char embedding size: 30\r\n",
" Norm word emb: False\r\n",
" Norm char emb: False\r\n",
" Train file directory: deo/train.bmes\r\n",
" Dev file directory: deo/test.bmes\r\n",
" Test file directory: deo/test.bmes\r\n",
" Raw file directory: None\r\n",
" Dset file directory: None\r\n",
" Model file directory: deo/lstmcrf\r\n",
" Loadmodel directory: None\r\n",
" Decode file directory: None\r\n",
" Train instance number: 25492\r\n",
" Dev instance number: 6363\r\n",
" Test instance number: 6363\r\n",
" Raw instance number: 0\r\n",
" FEATURE num: 0\r\n",
" ++++++++++++++++++++++++++++++++++++++++\r\n",
" Model Network:\r\n",
" Model use_crf: True\r\n",
" Model word extractor: LSTM\r\n",
" Model use_char: True\r\n",
" Model char extractor: LSTM\r\n",
" Model char_hidden_dim: 50\r\n",
" ++++++++++++++++++++++++++++++++++++++++\r\n",
" Training:\r\n",
" Optimizer: SGD\r\n",
" Iteration: 5\r\n",
" BatchSize: 10\r\n",
" Average batch loss: False\r\n",
" ++++++++++++++++++++++++++++++++++++++++\r\n",
" Hyperparameters:\r\n",
" Hyper lr: 0.015\r\n",
" Hyper lr_decay: 0.05\r\n",
" Hyper HP_clip: None\r\n",
" Hyper momentum: 0.0\r\n",
" Hyper l2: 1e-08\r\n",
" Hyper hidden_dim: 200\r\n",
" Hyper dropout: 0.5\r\n",
" Hyper lstm_layer: 1\r\n",
" Hyper bilstm: True\r\n",
" Hyper GPU: False\r\n",
"DATA SUMMARY END.\r\n",
"++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n",
"build sequence labeling network...\r\n",
"use_char: True\r\n",
"char feature extractor: LSTM\r\n",
"word feature extractor: LSTM\r\n",
"use crf: True\r\n",
"build word sequence feature extractor: LSTM...\r\n",
"build word representation...\r\n",
"build char sequence feature extractor: LSTM ...\r\n",
"build CRF...\r\n",
"Epoch: 0/5\r\n",
" Learning rate is set as: 0.015\r\n",
"Shuffle: first input word list: [9226, 50, 9227, 9228, 22, 4544, 710, 1676, 1824, 27, 31, 206, 9224, 3927, 39]\r\n",
" Instance: 500; Time: 5.86s; loss: 11467.5466; acc: 8805/10943=0.8046\r\n",
" Instance: 1000; Time: 6.54s; loss: 9018.0428; acc: 17637/21723=0.8119\r\n",
" Instance: 1500; Time: 5.93s; loss: 9521.5752; acc: 26282/32951=0.7976\r\n",
" Instance: 2000; Time: 5.90s; loss: 7228.6469; acc: 35205/43869=0.8025\r\n",
" Instance: 2500; Time: 6.75s; loss: 8085.7132; acc: 44175/55002=0.8032\r\n",
" Instance: 3000; Time: 5.84s; loss: 5276.5567; acc: 53600/66252=0.8090\r\n",
" Instance: 3500; Time: 6.28s; loss: 4135.9960; acc: 63132/77495=0.8147\r\n",
" Instance: 4000; Time: 5.90s; loss: 3853.3784; acc: 72417/88489=0.8184\r\n",
" Instance: 4500; Time: 6.32s; loss: 4280.6140; acc: 81774/99765=0.8197\r\n",
" Instance: 5000; Time: 6.34s; loss: 3548.4854; acc: 91583/111180=0.8237\r\n",
" Instance: 5500; Time: 5.99s; loss: 3455.1662; acc: 101116/122273=0.8270\r\n",
" Instance: 6000; Time: 6.29s; loss: 4394.3219; acc: 110342/133297=0.8278\r\n",
" Instance: 6500; Time: 6.23s; loss: 3717.7032; acc: 119876/144595=0.8290\r\n",
" Instance: 7000; Time: 5.45s; loss: 2873.9897; acc: 128952/155001=0.8319\r\n",
" Instance: 7500; Time: 6.21s; loss: 2776.3391; acc: 138536/165962=0.8347\r\n",
" Instance: 8000; Time: 6.52s; loss: 3239.7299; acc: 148031/177010=0.8363\r\n",
" Instance: 8500; Time: 6.25s; loss: 2915.1886; acc: 158139/188492=0.8390\r\n",
" Instance: 9000; Time: 6.54s; loss: 3102.8379; acc: 168049/199901=0.8407\r\n",
" Instance: 9500; Time: 5.57s; loss: 2747.8085; acc: 177376/210643=0.8421\r\n",
" Instance: 10000; Time: 6.04s; loss: 2749.7340; acc: 187038/221648=0.8439\r\n",
" Instance: 10500; Time: 5.90s; loss: 2867.2719; acc: 196552/232578=0.8451\r\n",
" Instance: 11000; Time: 5.59s; loss: 2784.1947; acc: 205848/243235=0.8463\r\n",
" Instance: 11500; Time: 6.07s; loss: 2740.2966; acc: 215314/254080=0.8474\r\n",
" Instance: 12000; Time: 5.86s; loss: 2775.6904; acc: 225095/265280=0.8485\r\n",
" Instance: 12500; Time: 6.44s; loss: 2652.2225; acc: 234628/276108=0.8498\r\n",
" Instance: 13000; Time: 5.71s; loss: 2050.9076; acc: 244627/287090=0.8521\r\n",
" Instance: 13500; Time: 6.26s; loss: 2708.4365; acc: 254417/298206=0.8532\r\n",
" Instance: 14000; Time: 6.02s; loss: 2395.0806; acc: 264181/309186=0.8544\r\n",
" Instance: 14500; Time: 6.64s; loss: 2916.7267; acc: 274178/320643=0.8551\r\n",
" Instance: 15000; Time: 5.53s; loss: 2391.7073; acc: 283815/331491=0.8562\r\n",
" Instance: 15500; Time: 5.90s; loss: 2536.5865; acc: 293743/342687=0.8572\r\n",
" Instance: 16000; Time: 5.64s; loss: 2253.5037; acc: 303465/353490=0.8585\r\n",
" Instance: 16500; Time: 5.68s; loss: 2375.4270; acc: 313185/364392=0.8595\r\n",
" Instance: 17000; Time: 5.75s; loss: 2585.6658; acc: 322453/374950=0.8600\r\n",
" Instance: 17500; Time: 6.33s; loss: 2287.8177; acc: 332198/385729=0.8612\r\n",
" Instance: 18000; Time: 6.38s; loss: 2255.5432; acc: 342028/396659=0.8623\r\n",
" Instance: 18500; Time: 5.67s; loss: 2424.5189; acc: 351848/407735=0.8629\r\n",
" Instance: 19000; Time: 6.53s; loss: 2321.5856; acc: 362204/419270=0.8639\r\n",
" Instance: 19500; Time: 6.67s; loss: 2430.9487; acc: 372338/430618=0.8647\r\n",
" Instance: 20000; Time: 5.75s; loss: 2236.1490; acc: 382240/441610=0.8656\r\n",
" Instance: 20500; Time: 5.98s; loss: 2115.0308; acc: 392503/452873=0.8667\r\n",
" Instance: 21000; Time: 5.64s; loss: 2135.8223; acc: 402747/464138=0.8677\r\n",
" Instance: 21500; Time: 5.27s; loss: 2002.2705; acc: 412302/474683=0.8686\r\n",
" Instance: 22000; Time: 6.17s; loss: 2135.2474; acc: 422632/486021=0.8696\r\n",
" Instance: 22500; Time: 6.72s; loss: 2101.0378; acc: 432809/497276=0.8704\r\n",
" Instance: 23000; Time: 6.36s; loss: 1998.7151; acc: 443034/508449=0.8713\r\n",
" Instance: 23500; Time: 6.06s; loss: 2041.7292; acc: 453056/519504=0.8721\r\n",
" Instance: 24000; Time: 6.25s; loss: 1934.0773; acc: 462889/530274=0.8729\r\n",
" Instance: 24500; Time: 6.63s; loss: 2085.5032; acc: 473449/541790=0.8739\r\n",
" Instance: 25000; Time: 6.21s; loss: 2106.8956; acc: 483837/553294=0.8745\r\n",
" Instance: 25492; Time: 6.32s; loss: 2093.8816; acc: 494107/564619=0.8751\r\n",
"Epoch: 0 training finished. Time: 310.67s, speed: 82.06st/s, total loss: 171129.86590576172\r\n",
"totalloss: 171129.86590576172\r\n",
"Right token = 129760 All token = 139693 acc = 0.9288940748641664\r\n",
"Dev: time: 24.73s, speed: 260.51st/s; acc: 0.9289, p: 0.7243, r: 0.6313, f: 0.6746\r\n",
"Exceed previous best f score: -10\r\n",
"Save current best model in file: deo/lstmcrf.0.model\r\n",
"Right token = 129760 All token = 139693 acc = 0.9288940748641664\r\n",
"Test: time: 24.62s, speed: 261.88st/s; acc: 0.9289, p: 0.7243, r: 0.6313, f: 0.6746\r\n",
"Epoch: 1/5\r\n",
" Learning rate is set as: 0.014285714285714285\r\n",
"Shuffle: first input word list: [5184, 4371, 145, 120, 556, 425, 5239, 160, 3963, 2841, 627, 399, 628, 3, 605, 17404, 5184, 1982, 231, 120, 65, 330, 331, 34, 9736, 45, 26, 93, 1570, 1487, 2341, 108, 143, 39]\r\n",
" Instance: 500; Time: 6.11s; loss: 1660.6455; acc: 10400/11255=0.9240\r\n",
" Instance: 1000; Time: 5.97s; loss: 1776.8972; acc: 20563/22343=0.9203\r\n",
" Instance: 1500; Time: 5.90s; loss: 1681.3650; acc: 30902/33500=0.9224\r\n",
" Instance: 2000; Time: 5.83s; loss: 1643.1586; acc: 41072/44524=0.9225\r\n",
" Instance: 2500; Time: 5.89s; loss: 1667.3973; acc: 51168/55487=0.9222\r\n",
" Instance: 3000; Time: 5.93s; loss: 1576.0062; acc: 61229/66317=0.9233\r\n",
" Instance: 3500; Time: 5.93s; loss: 1866.0762; acc: 71847/77915=0.9221\r\n",
" Instance: 4000; Time: 6.32s; loss: 1833.4409; acc: 81818/88771=0.9217\r\n",
" Instance: 4500; Time: 6.16s; loss: 1718.2008; acc: 92181/99991=0.9219\r\n",
" Instance: 5000; Time: 6.37s; loss: 1782.8713; acc: 102404/111142=0.9214\r\n",
" Instance: 5500; Time: 5.90s; loss: 1750.3334; acc: 112413/122010=0.9213\r\n",
" Instance: 6000; Time: 5.74s; loss: 1595.3087; acc: 122565/132960=0.9218\r\n",
" Instance: 6500; Time: 6.60s; loss: 3571.5574; acc: 132517/144310=0.9183\r\n",
" Instance: 7000; Time: 6.21s; loss: 1864.6843; acc: 142681/155456=0.9178\r\n",
" Instance: 7500; Time: 6.11s; loss: 1811.4705; acc: 152345/165939=0.9181\r\n",
" Instance: 8000; Time: 6.58s; loss: 2006.8934; acc: 162071/176600=0.9177\r\n",
" Instance: 8500; Time: 6.05s; loss: 1683.4283; acc: 172553/187923=0.9182\r\n",
" Instance: 9000; Time: 5.98s; loss: 1720.7343; acc: 183091/199269=0.9188\r\n",
" Instance: 9500; Time: 6.10s; loss: 1888.2816; acc: 193549/210659=0.9188\r\n",
" Instance: 10000; Time: 5.94s; loss: 1805.8206; acc: 203894/221913=0.9188\r\n",
" Instance: 10500; Time: 5.81s; loss: 1555.8290; acc: 213926/232711=0.9193\r\n",
" Instance: 11000; Time: 5.84s; loss: 1626.5405; acc: 224030/243567=0.9198\r\n",
" Instance: 11500; Time: 6.10s; loss: 1732.8615; acc: 234746/255113=0.9202\r\n",
" Instance: 12000; Time: 5.38s; loss: 1483.8192; acc: 244338/265481=0.9204\r\n",
" Instance: 12500; Time: 6.81s; loss: 1542.6008; acc: 255297/277265=0.9208\r\n",
" Instance: 13000; Time: 6.08s; loss: 1533.9236; acc: 265172/288010=0.9207\r\n",
" Instance: 13500; Time: 6.72s; loss: 1773.9761; acc: 275447/299216=0.9206\r\n",
" Instance: 14000; Time: 5.69s; loss: 1560.6853; acc: 286049/310574=0.9210\r\n",
" Instance: 14500; Time: 6.02s; loss: 1429.2494; acc: 296840/322069=0.9217\r\n",
" Instance: 15000; Time: 6.39s; loss: 1585.5450; acc: 307147/333198=0.9218\r\n",
" Instance: 15500; Time: 7.03s; loss: 1769.0262; acc: 317573/344597=0.9216\r\n",
" Instance: 16000; Time: 5.75s; loss: 1547.6821; acc: 327728/355529=0.9218\r\n",
" Instance: 16500; Time: 6.01s; loss: 1403.5238; acc: 337984/366562=0.9220\r\n",
" Instance: 17000; Time: 5.48s; loss: 1375.0182; acc: 347751/377002=0.9224\r\n",
" Instance: 17500; Time: 6.75s; loss: 1434.2316; acc: 357915/387974=0.9225\r\n",
" Instance: 18000; Time: 5.88s; loss: 1539.0273; acc: 368030/398881=0.9227\r\n",
" Instance: 18500; Time: 6.78s; loss: 1500.8455; acc: 378476/410121=0.9228\r\n",
" Instance: 19000; Time: 6.54s; loss: 1435.6569; acc: 388723/421181=0.9229\r\n",
" Instance: 19500; Time: 6.75s; loss: 1485.3093; acc: 399416/432650=0.9232\r\n",
" Instance: 20000; Time: 5.73s; loss: 1588.8606; acc: 409214/443274=0.9232\r\n",
" Instance: 20500; Time: 6.26s; loss: 1574.6174; acc: 419672/454593=0.9232\r\n",
" Instance: 21000; Time: 6.39s; loss: 1231.7192; acc: 430142/465710=0.9236\r\n",
" Instance: 21500; Time: 6.27s; loss: 1412.1769; acc: 440887/477117=0.9241\r\n",
" Instance: 22000; Time: 5.88s; loss: 1405.6757; acc: 451015/487953=0.9243\r\n",
" Instance: 22500; Time: 5.93s; loss: 1395.8198; acc: 461330/498962=0.9246\r\n",
" Instance: 23000; Time: 5.81s; loss: 1414.0807; acc: 471537/509879=0.9248\r\n",
" Instance: 23500; Time: 5.68s; loss: 1600.6844; acc: 481593/520662=0.9250\r\n",
" Instance: 24000; Time: 6.40s; loss: 1477.9640; acc: 492384/532248=0.9251\r\n",
" Instance: 24500; Time: 5.71s; loss: 1331.5858; acc: 502523/543045=0.9254\r\n",
" Instance: 25000; Time: 5.98s; loss: 1312.9333; acc: 512753/553948=0.9256\r\n",
" Instance: 25492; Time: 5.42s; loss: 1229.3799; acc: 522838/564619=0.9260\r\n",
"Epoch: 1 training finished. Time: 310.90s, speed: 81.99st/s, total loss: 83195.4206237793\r\n",
"totalloss: 83195.4206237793\r\n",
"Right token = 132514 All token = 139693 acc = 0.9486087348686048\r\n",
"Dev: time: 25.19s, speed: 255.85st/s; acc: 0.9486, p: 0.7634, r: 0.7774, f: 0.7704\r\n",
"Exceed previous best f score: 0.6746206609852421\r\n",
"Save current best model in file: deo/lstmcrf.1.model\r\n",
"Right token = 132514 All token = 139693 acc = 0.9486087348686048\r\n",
"Test: time: 24.53s, speed: 262.90st/s; acc: 0.9486, p: 0.7634, r: 0.7774, f: 0.7704\r\n",
"Epoch: 2/5\r\n",
" Learning rate is set as: 0.013636363636363634\r\n",
"Shuffle: first input word list: [7849, 7850, 3, 7813, 125, 7851, 3, 557, 4072, 160, 3808, 7852, 39]\r\n",
" Instance: 500; Time: 6.27s; loss: 1280.9294; acc: 10663/11314=0.9425\r\n",
" Instance: 1000; Time: 6.06s; loss: 1285.6218; acc: 21068/22303=0.9446\r\n",
" Instance: 1500; Time: 6.13s; loss: 1326.2072; acc: 31548/33480=0.9423\r\n",
" Instance: 2000; Time: 5.58s; loss: 1214.4330; acc: 41702/44222=0.9430\r\n",
" Instance: 2500; Time: 5.72s; loss: 1101.7487; acc: 51894/54984=0.9438\r\n",
" Instance: 3000; Time: 6.45s; loss: 1309.4054; acc: 62490/66282=0.9428\r\n",
" Instance: 3500; Time: 6.20s; loss: 1190.8353; acc: 73359/77820=0.9427\r\n",
" Instance: 4000; Time: 5.93s; loss: 1284.1819; acc: 83610/88721=0.9424\r\n",
" Instance: 4500; Time: 6.18s; loss: 1478.1969; acc: 93669/99565=0.9408\r\n",
" Instance: 5000; Time: 6.36s; loss: 1319.6150; acc: 104481/111063=0.9407\r\n",
" Instance: 5500; Time: 5.53s; loss: 1322.8800; acc: 114779/122026=0.9406\r\n",
" Instance: 6000; Time: 6.03s; loss: 1133.0632; acc: 125679/133523=0.9413\r\n",
" Instance: 6500; Time: 5.80s; loss: 1274.8450; acc: 136040/144572=0.9410\r\n",
" Instance: 7000; Time: 6.60s; loss: 1243.4021; acc: 146664/155859=0.9410\r\n",
" Instance: 7500; Time: 6.62s; loss: 1109.9163; acc: 156839/166670=0.9410\r\n",
" Instance: 8000; Time: 5.78s; loss: 1141.1495; acc: 167070/177497=0.9413\r\n",
" Instance: 8500; Time: 6.25s; loss: 1345.2468; acc: 177705/188852=0.9410\r\n",
" Instance: 9000; Time: 6.25s; loss: 1284.2751; acc: 188589/200424=0.9410\r\n",
" Instance: 9500; Time: 6.08s; loss: 1189.3280; acc: 199116/211545=0.9412\r\n",
" Instance: 10000; Time: 5.84s; loss: 1127.5089; acc: 209400/222482=0.9412\r\n",
" Instance: 10500; Time: 6.03s; loss: 1217.0042; acc: 219730/233482=0.9411\r\n",
" Instance: 11000; Time: 5.78s; loss: 1072.2196; acc: 230517/244809=0.9416\r\n",
" Instance: 11500; Time: 5.96s; loss: 1273.5804; acc: 240833/255764=0.9416\r\n",
" Instance: 12000; Time: 6.26s; loss: 1203.7413; acc: 251405/266951=0.9418\r\n",
" Instance: 12500; Time: 6.10s; loss: 1107.0691; acc: 261671/277826=0.9419\r\n",
" Instance: 13000; Time: 6.03s; loss: 1241.8059; acc: 271868/288606=0.9420\r\n",
" Instance: 13500; Time: 5.77s; loss: 1202.5405; acc: 282386/299698=0.9422\r\n",
" Instance: 14000; Time: 6.32s; loss: 1086.8290; acc: 293188/311092=0.9424\r\n",
" Instance: 14500; Time: 6.08s; loss: 1144.3563; acc: 303720/322197=0.9427\r\n",
" Instance: 15000; Time: 6.07s; loss: 1118.2712; acc: 313614/332657=0.9428\r\n",
" Instance: 15500; Time: 6.23s; loss: 1110.2034; acc: 324023/343668=0.9428\r\n",
" Instance: 16000; Time: 5.96s; loss: 1002.4485; acc: 334274/354440=0.9431\r\n",
" Instance: 16500; Time: 6.04s; loss: 1060.2374; acc: 344890/365621=0.9433\r\n",
" Instance: 17000; Time: 6.01s; loss: 1116.2095; acc: 355387/376700=0.9434\r\n",
" Instance: 17500; Time: 6.85s; loss: 1230.8478; acc: 366002/387933=0.9435\r\n",
" Instance: 18000; Time: 6.11s; loss: 1147.5312; acc: 376815/399384=0.9435\r\n",
" Instance: 18500; Time: 6.01s; loss: 1148.4175; acc: 387328/410464=0.9436\r\n",
" Instance: 19000; Time: 5.50s; loss: 980.0217; acc: 397249/420913=0.9438\r\n",
" Instance: 19500; Time: 6.17s; loss: 956.7783; acc: 407763/431925=0.9441\r\n",
" Instance: 20000; Time: 6.41s; loss: 1499.5128; acc: 417532/442430=0.9437\r\n",
" Instance: 20500; Time: 6.48s; loss: 1302.1766; acc: 428130/453644=0.9438\r\n",
" Instance: 21000; Time: 6.19s; loss: 1181.6963; acc: 438606/464713=0.9438\r\n",
" Instance: 21500; Time: 5.56s; loss: 1168.8792; acc: 449021/475746=0.9438\r\n",
" Instance: 22000; Time: 6.13s; loss: 1254.5463; acc: 459795/487168=0.9438\r\n",
" Instance: 22500; Time: 5.78s; loss: 1207.0382; acc: 469798/497830=0.9437\r\n",
" Instance: 23000; Time: 6.28s; loss: 1063.8285; acc: 480324/508899=0.9438\r\n",
" Instance: 23500; Time: 5.41s; loss: 1086.1536; acc: 490731/519892=0.9439\r\n",
" Instance: 24000; Time: 6.59s; loss: 1173.0015; acc: 501539/531341=0.9439\r\n",
" Instance: 24500; Time: 5.73s; loss: 1021.1012; acc: 511973/542305=0.9441\r\n",
" Instance: 25000; Time: 6.14s; loss: 1099.7375; acc: 522540/553447=0.9442\r\n",
" Instance: 25492; Time: 6.40s; loss: 1045.8465; acc: 533181/564619=0.9443\r\n",
"Epoch: 2 training finished. Time: 310.04s, speed: 82.22st/s, total loss: 60486.4206237793\r\n",
"totalloss: 60486.4206237793\r\n",
"Right token = 133922 All token = 139693 acc = 0.9586879800705833\r\n",
"Dev: time: 24.95s, speed: 258.25st/s; acc: 0.9587, p: 0.8274, r: 0.8045, f: 0.8158\r\n",
"Exceed previous best f score: 0.7703635172121353\r\n",
"Save current best model in file: deo/lstmcrf.2.model\r\n",
"Right token = 133922 All token = 139693 acc = 0.9586879800705833\r\n",
"Test: time: 24.59s, speed: 262.23st/s; acc: 0.9587, p: 0.8274, r: 0.8045, f: 0.8158\r\n",
"Epoch: 3/5\r\n",
" Learning rate is set as: 0.013043478260869566\r\n",
"Shuffle: first input word list: [23187, 343, 97, 8919, 2841, 23, 481, 26, 699, 1706, 65, 23188, 97, 7686, 1767, 2841, 45, 26, 699, 26, 39]\r\n",
" Instance: 500; Time: 5.92s; loss: 940.0109; acc: 10319/10843=0.9517\r\n",
" Instance: 1000; Time: 5.84s; loss: 962.2094; acc: 20711/21788=0.9506\r\n",
" Instance: 1500; Time: 5.86s; loss: 894.6539; acc: 30990/32562=0.9517\r\n",
" Instance: 2000; Time: 6.39s; loss: 962.2877; acc: 42058/44117=0.9533\r\n",
" Instance: 2500; Time: 6.03s; loss: 911.1019; acc: 52460/54982=0.9541\r\n",
" Instance: 3000; Time: 6.23s; loss: 1043.8060; acc: 63238/66285=0.9540\r\n",
" Instance: 3500; Time: 6.24s; loss: 934.1678; acc: 73968/77454=0.9550\r\n",
" Instance: 4000; Time: 5.89s; loss: 1012.4240; acc: 84804/88810=0.9549\r\n",
" Instance: 4500; Time: 6.53s; loss: 1162.8699; acc: 95347/99995=0.9535\r\n",
" Instance: 5000; Time: 5.76s; loss: 1025.0381; acc: 105639/110844=0.9530\r\n",
" Instance: 5500; Time: 5.70s; loss: 1057.6375; acc: 116062/121793=0.9529\r\n",
" Instance: 6000; Time: 6.73s; loss: 1088.8983; acc: 126787/133119=0.9524\r\n",
" Instance: 6500; Time: 5.71s; loss: 933.4320; acc: 137306/144124=0.9527\r\n",
" Instance: 7000; Time: 5.89s; loss: 1014.9440; acc: 148137/155532=0.9525\r\n",
" Instance: 7500; Time: 6.24s; loss: 1026.7166; acc: 158919/166863=0.9524\r\n",
" Instance: 8000; Time: 6.07s; loss: 1711.4380; acc: 168662/177530=0.9500\r\n",
" Instance: 8500; Time: 6.76s; loss: 1117.1985; acc: 179520/188948=0.9501\r\n",
" Instance: 9000; Time: 6.03s; loss: 994.3868; acc: 189963/199911=0.9502\r\n",
" Instance: 9500; Time: 6.00s; loss: 1027.4473; acc: 200759/211262=0.9503\r\n",
" Instance: 10000; Time: 6.40s; loss: 1234.2314; acc: 211552/222705=0.9499\r\n",
" Instance: 10500; Time: 5.89s; loss: 1033.4143; acc: 222301/233958=0.9502\r\n",
" Instance: 11000; Time: 6.24s; loss: 951.8639; acc: 233025/245178=0.9504\r\n",
" Instance: 11500; Time: 6.89s; loss: 928.2294; acc: 243565/256183=0.9507\r\n",
" Instance: 12000; Time: 5.29s; loss: 1015.0317; acc: 253265/266399=0.9507\r\n",
" Instance: 12500; Time: 6.65s; loss: 1103.1348; acc: 264113/277783=0.9508\r\n",
" Instance: 13000; Time: 6.45s; loss: 962.6361; acc: 275217/289363=0.9511\r\n",
" Instance: 13500; Time: 6.07s; loss: 1020.8254; acc: 285977/300631=0.9513\r\n",
" Instance: 14000; Time: 6.10s; loss: 781.8086; acc: 296629/311724=0.9516\r\n",
" Instance: 14500; Time: 5.84s; loss: 937.1790; acc: 307283/322901=0.9516\r\n",
" Instance: 15000; Time: 5.69s; loss: 767.0591; acc: 317595/333638=0.9519\r\n",
" Instance: 15500; Time: 6.13s; loss: 989.7808; acc: 328539/345090=0.9520\r\n",
" Instance: 16000; Time: 6.06s; loss: 1008.0867; acc: 339054/356145=0.9520\r\n",
" Instance: 16500; Time: 6.83s; loss: 977.7954; acc: 349747/367355=0.9521\r\n",
" Instance: 17000; Time: 6.14s; loss: 1031.8037; acc: 360128/378258=0.9521\r\n",
" Instance: 17500; Time: 6.64s; loss: 943.5344; acc: 370775/389410=0.9521\r\n",
" Instance: 18000; Time: 6.01s; loss: 1059.6608; acc: 381516/400726=0.9521\r\n",
" Instance: 18500; Time: 5.84s; loss: 975.2371; acc: 391859/411607=0.9520\r\n",
" Instance: 19000; Time: 5.62s; loss: 839.3114; acc: 402256/422448=0.9522\r\n",
" Instance: 19500; Time: 5.56s; loss: 889.2471; acc: 412514/433171=0.9523\r\n",
" Instance: 20000; Time: 6.06s; loss: 1135.0752; acc: 423000/444279=0.9521\r\n",
" Instance: 20500; Time: 5.78s; loss: 990.3540; acc: 433401/455164=0.9522\r\n",
" Instance: 21000; Time: 5.90s; loss: 965.2799; acc: 444097/466375=0.9522\r\n",
" Instance: 21500; Time: 6.32s; loss: 933.4833; acc: 454390/477186=0.9522\r\n",
" Instance: 22000; Time: 6.31s; loss: 969.0947; acc: 465201/488490=0.9523\r\n",
" Instance: 22500; Time: 5.97s; loss: 886.4409; acc: 475563/499327=0.9524\r\n",
" Instance: 23000; Time: 5.79s; loss: 902.2258; acc: 485830/510057=0.9525\r\n",
" Instance: 23500; Time: 5.49s; loss: 868.9147; acc: 496307/520979=0.9526\r\n",
" Instance: 24000; Time: 5.69s; loss: 956.2675; acc: 506433/531580=0.9527\r\n",
" Instance: 24500; Time: 6.38s; loss: 889.5598; acc: 516987/542625=0.9528\r\n",
" Instance: 25000; Time: 5.87s; loss: 1013.0883; acc: 527600/553760=0.9528\r\n",
" Instance: 25492; Time: 5.83s; loss: 934.0947; acc: 537961/564619=0.9528\r\n",
"Epoch: 3 training finished. Time: 309.55s, speed: 82.35st/s, total loss: 50714.418212890625\r\n",
"totalloss: 50714.418212890625\r\n",
"Right token = 134607 All token = 139693 acc = 0.9635915901297846\r\n",
"Dev: time: 25.31s, speed: 254.99st/s; acc: 0.9636, p: 0.8352, r: 0.8358, f: 0.8355\r\n",
"Exceed previous best f score: 0.8157790927021696\r\n",
"Save current best model in file: deo/lstmcrf.3.model\r\n",
"Right token = 134607 All token = 139693 acc = 0.9635915901297846\r\n",
"Test: time: 25.42s, speed: 253.53st/s; acc: 0.9636, p: 0.8352, r: 0.8358, f: 0.8355\r\n",
"Epoch: 4/5\r\n",
" Learning rate is set as: 0.0125\r\n",
"Shuffle: first input word list: [7337, 7338, 7, 2733, 34, 1659, 2797, 206, 3210, 108, 39]\r\n",
" Instance: 500; Time: 5.86s; loss: 877.9946; acc: 10778/11292=0.9545\r\n",
" Instance: 1000; Time: 6.11s; loss: 857.3245; acc: 21487/22435=0.9577\r\n",
" Instance: 1500; Time: 6.18s; loss: 858.3844; acc: 32151/33539=0.9586\r\n",
" Instance: 2000; Time: 5.89s; loss: 842.4369; acc: 42556/44391=0.9587\r\n",
" Instance: 2500; Time: 6.84s; loss: 817.6665; acc: 53454/55716=0.9594\r\n",
" Instance: 3000; Time: 6.15s; loss: 741.5051; acc: 63889/66564=0.9598\r\n",
" Instance: 3500; Time: 5.55s; loss: 905.2797; acc: 74139/77306=0.9590\r\n",
" Instance: 4000; Time: 5.81s; loss: 787.7540; acc: 84661/88260=0.9592\r\n",
" Instance: 4500; Time: 6.14s; loss: 881.2434; acc: 95370/99420=0.9593\r\n",
" Instance: 5000; Time: 6.54s; loss: 903.9222; acc: 106333/110852=0.9592\r\n",
" Instance: 5500; Time: 6.21s; loss: 845.3932; acc: 117336/122301=0.9594\r\n",
" Instance: 6000; Time: 6.05s; loss: 948.8181; acc: 127952/133378=0.9593\r\n",
" Instance: 6500; Time: 6.17s; loss: 902.6189; acc: 138838/144754=0.9591\r\n",
" Instance: 7000; Time: 6.61s; loss: 828.2267; acc: 149363/155721=0.9592\r\n",
" Instance: 7500; Time: 6.66s; loss: 948.4744; acc: 160001/166898=0.9587\r\n",
" Instance: 8000; Time: 6.43s; loss: 803.2566; acc: 170572/177927=0.9587\r\n",
" Instance: 8500; Time: 5.83s; loss: 784.4020; acc: 181380/189129=0.9590\r\n",
" Instance: 9000; Time: 6.42s; loss: 834.9692; acc: 192077/200270=0.9591\r\n",
" Instance: 9500; Time: 6.09s; loss: 825.7832; acc: 202599/211206=0.9592\r\n",
" Instance: 10000; Time: 6.22s; loss: 799.9487; acc: 212891/221930=0.9593\r\n",
" Instance: 10500; Time: 6.24s; loss: 806.8264; acc: 223692/233165=0.9594\r\n",
" Instance: 11000; Time: 5.77s; loss: 864.5057; acc: 234107/244041=0.9593\r\n",
" Instance: 11500; Time: 6.12s; loss: 800.9823; acc: 244684/255016=0.9595\r\n",
" Instance: 12000; Time: 6.28s; loss: 786.8318; acc: 255315/266064=0.9596\r\n",
" Instance: 12500; Time: 6.12s; loss: 788.4788; acc: 265873/277054=0.9596\r\n",
" Instance: 13000; Time: 5.77s; loss: 854.1526; acc: 276000/287600=0.9597\r\n",
" Instance: 13500; Time: 6.25s; loss: 878.9348; acc: 286882/299012=0.9594\r\n",
" Instance: 14000; Time: 6.10s; loss: 870.1184; acc: 297590/310165=0.9595\r\n",
" Instance: 14500; Time: 5.51s; loss: 741.8000; acc: 307808/320797=0.9595\r\n",
" Instance: 15000; Time: 6.50s; loss: 920.5197; acc: 319023/332527=0.9594\r\n",
" Instance: 15500; Time: 6.13s; loss: 761.0104; acc: 329428/343336=0.9595\r\n",
" Instance: 16000; Time: 6.38s; loss: 848.5674; acc: 340057/354444=0.9594\r\n",
" Instance: 16500; Time: 5.64s; loss: 930.0559; acc: 350421/365299=0.9593\r\n",
" Instance: 17000; Time: 6.11s; loss: 841.1011; acc: 361035/376362=0.9593\r\n",
" Instance: 17500; Time: 6.16s; loss: 868.1248; acc: 371664/387468=0.9592\r\n",
" Instance: 18000; Time: 6.48s; loss: 797.3402; acc: 382249/398475=0.9593\r\n",
" Instance: 18500; Time: 6.44s; loss: 899.2206; acc: 392775/409488=0.9592\r\n",
" Instance: 19000; Time: 5.63s; loss: 898.8475; acc: 403286/420425=0.9592\r\n",
" Instance: 19500; Time: 5.95s; loss: 774.3936; acc: 413867/431417=0.9593\r\n",
" Instance: 20000; Time: 5.87s; loss: 752.1008; acc: 424443/442412=0.9594\r\n",
" Instance: 20500; Time: 6.61s; loss: 992.5529; acc: 435364/453870=0.9592\r\n",
" Instance: 21000; Time: 6.77s; loss: 977.9945; acc: 446541/465583=0.9591\r\n",
" Instance: 21500; Time: 6.28s; loss: 1086.4163; acc: 457255/476816=0.9590\r\n",
" Instance: 22000; Time: 6.47s; loss: 816.3641; acc: 467557/487592=0.9589\r\n",
" Instance: 22500; Time: 5.65s; loss: 823.9532; acc: 478216/498655=0.9590\r\n",
" Instance: 23000; Time: 5.66s; loss: 825.9805; acc: 488786/509705=0.9590\r\n",
" Instance: 23500; Time: 6.30s; loss: 1589.7987; acc: 499143/520894=0.9582\r\n",
" Instance: 24000; Time: 5.84s; loss: 932.5292; acc: 509251/531524=0.9581\r\n",
" Instance: 24500; Time: 6.04s; loss: 1018.7532; acc: 519811/542617=0.9580\r\n",
" Instance: 25000; Time: 5.85s; loss: 723.8646; acc: 530132/553332=0.9581\r\n",
" Instance: 25492; Time: 6.26s; loss: 901.2269; acc: 540961/564619=0.9581\r\n",
"Epoch: 4 training finished. Time: 312.96s, speed: 81.45st/s, total loss: 44368.74914550781\r\n",
"totalloss: 44368.74914550781\r\n",
"Right token = 134911 All token = 139693 acc = 0.9657677907983936\r\n",
"Dev: time: 25.41s, speed: 253.52st/s; acc: 0.9658, p: 0.8454, r: 0.8483, f: 0.8468\r\n",
"Exceed previous best f score: 0.8355109659356044\r\n",
"Save current best model in file: deo/lstmcrf.4.model\r\n",
"Right token = 134911 All token = 139693 acc = 0.9657677907983936\r\n",
"Test: time: 24.93s, speed: 258.62st/s; acc: 0.9658, p: 0.8454, r: 0.8483, f: 0.8468\r\n"
]
}
],
"source": [
"!python main.py --config deo.train.config"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:46:37.551094Z",
"iopub.status.busy": "2020-11-19T11:46:37.550172Z",
"iopub.status.idle": "2020-11-19T11:46:37.553673Z",
"shell.execute_reply": "2020-11-19T11:46:37.552873Z"
},
"papermill": {
"duration": 0.145864,
"end_time": "2020-11-19T11:46:37.553805",
"exception": false,
"start_time": "2020-11-19T11:46:37.407941",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"config_string = \"\"\"\n",
"status=decode\n",
"raw_dir=deo/test.bmes\n",
"nbest=1\n",
"decode_dir=deo/hasil.out\n",
"dset_dir=deo/lstmcrf.dset\n",
"load_model_dir=deo/lstmcrf.0.model\n",
"\"\"\"\n",
"file = open(\"deo.test.config\" , \"w\")\n",
"file.write(config_string)\n",
"file.close()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:46:37.834717Z",
"iopub.status.busy": "2020-11-19T11:46:37.831300Z",
"iopub.status.idle": "2020-11-19T11:47:54.499387Z",
"shell.execute_reply": "2020-11-19T11:47:54.498707Z"
},
"papermill": {
"duration": 76.810275,
"end_time": "2020-11-19T11:47:54.499537",
"exception": false,
"start_time": "2020-11-19T11:46:37.689262",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Seed num: 42\r\n",
"MODEL: decode\r\n",
"deo/test.bmes\r\n",
"++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n",
"DATA SUMMARY START:\r\n",
" I/O:\r\n",
" Start Sequence Laebling task...\r\n",
" Tag scheme: BIO\r\n",
" Split token: ||| \r\n",
" MAX SENTENCE LENGTH: 250\r\n",
" MAX WORD LENGTH: -1\r\n",
" Number normalized: True\r\n",
" Word alphabet size: 24301\r\n",
" Char alphabet size: 207\r\n",
" Label alphabet size: 24\r\n",
" Word embedding dir: None\r\n",
" Char embedding dir: None\r\n",
" Word embedding size: 50\r\n",
" Char embedding size: 30\r\n",
" Norm word emb: False\r\n",
" Norm char emb: False\r\n",
" Train file directory: deo/train.bmes\r\n",
" Dev file directory: deo/test.bmes\r\n",
" Test file directory: deo/test.bmes\r\n",
" Raw file directory: deo/test.bmes\r\n",
" Dset file directory: deo/lstmcrf.dset\r\n",
" Model file directory: deo/lstmcrf\r\n",
" Loadmodel directory: deo/lstmcrf.0.model\r\n",
" Decode file directory: deo/hasil.out\r\n",
" Train instance number: 25492\r\n",
" Dev instance number: 6363\r\n",
" Test instance number: 6363\r\n",
" Raw instance number: 0\r\n",
" FEATURE num: 0\r\n",
" ++++++++++++++++++++++++++++++++++++++++\r\n",
" Model Network:\r\n",
" Model use_crf: True\r\n",
" Model word extractor: LSTM\r\n",
" Model use_char: True\r\n",
" Model char extractor: LSTM\r\n",
" Model char_hidden_dim: 50\r\n",
" ++++++++++++++++++++++++++++++++++++++++\r\n",
" Training:\r\n",
" Optimizer: SGD\r\n",
" Iteration: 5\r\n",
" BatchSize: 10\r\n",
" Average batch loss: False\r\n",
" ++++++++++++++++++++++++++++++++++++++++\r\n",
" Hyperparameters:\r\n",
" Hyper lr: 0.015\r\n",
" Hyper lr_decay: 0.05\r\n",
" Hyper HP_clip: None\r\n",
" Hyper momentum: 0.0\r\n",
" Hyper l2: 1e-08\r\n",
" Hyper hidden_dim: 200\r\n",
" Hyper dropout: 0.5\r\n",
" Hyper lstm_layer: 1\r\n",
" Hyper bilstm: True\r\n",
" Hyper GPU: False\r\n",
"DATA SUMMARY END.\r\n",
"++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n",
"nbest: 1\r\n",
"Load Model from file: deo/lstmcrf\r\n",
"build sequence labeling network...\r\n",
"use_char: True\r\n",
"char feature extractor: LSTM\r\n",
"word feature extractor: LSTM\r\n",
"use crf: True\r\n",
"build word sequence feature extractor: LSTM...\r\n",
"build word representation...\r\n",
"build char sequence feature extractor: LSTM ...\r\n",
"build CRF...\r\n",
"Decode raw data, nbest: 1 ...\r\n",
"Right token = 129760 All token = 139693 acc = 0.9288940748641664\r\n",
"raw: time:64.63s, speed:99.02st/s; acc: 0.9289, p: 0.7243, r: 0.6313, f: 0.6746\r\n",
"Predict raw 1-best result has been written into file. deo/hasil.out\r\n"
]
}
],
"source": [
"!python main.py --config deo.test.config"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:47:54.783498Z",
"iopub.status.busy": "2020-11-19T11:47:54.782738Z",
"iopub.status.idle": "2020-11-19T11:47:55.524524Z",
"shell.execute_reply": "2020-11-19T11:47:55.523678Z"
},
"papermill": {
"duration": 0.886172,
"end_time": "2020-11-19T11:47:55.524658",
"exception": false,
"start_time": "2020-11-19T11:47:54.638486",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pemerintahan O\r\n",
"baru O\r\n",
"ingin O\r\n",
"memperkuat O\r\n",
"kebebasan O\r\n",
"sipil O\r\n",
"dan O\r\n",
"politik O\r\n",
", O\r\n",
"tetapi O\r\n"
]
}
],
"source": [
"!head deo/test.bmes"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:47:55.818165Z",
"iopub.status.busy": "2020-11-19T11:47:55.817440Z",
"iopub.status.idle": "2020-11-19T11:47:56.607221Z",
"shell.execute_reply": "2020-11-19T11:47:56.606513Z"
},
"papermill": {
"duration": 0.935059,
"end_time": "2020-11-19T11:47:56.607400",
"exception": false,
"start_time": "2020-11-19T11:47:55.672341",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# 1.0000\r\n",
"Pemerintahan O\r\n",
"baru O\r\n",
"ingin O\r\n",
"memperkuat O\r\n",
"kebebasan O\r\n",
"sipil O\r\n",
"dan O\r\n",
"politik O\r\n",
", O\r\n",
"tetapi O\r\n",
"ditentang O\r\n",
"oleh O\r\n",
"Chogyal B-Organization\r\n",
"Palden I-Organization\r\n",
"Thondup I-Organization\r\n",
"Namgyal I-Organization\r\n",
". O\r\n",
"\r\n",
"# 1.0000\r\n",
"Jadi O\r\n",
"relevansi O\r\n",
"untuk O\r\n",
"status O\r\n",
"bencana O\r\n",
"nasional O\r\n",
"tidak O\r\n",
"relevan O\r\n",
", O\r\n",
"\" O\r\n"
]
}
],
"source": [
"!head -n 30 deo/hasil.out"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:47:56.935520Z",
"iopub.status.busy": "2020-11-19T11:47:56.934673Z",
"iopub.status.idle": "2020-11-19T11:47:56.950465Z",
"shell.execute_reply": "2020-11-19T11:47:56.951062Z"
},
"papermill": {
"duration": 0.18718,
"end_time": "2020-11-19T11:47:56.951263",
"exception": false,
"start_time": "2020-11-19T11:47:56.764083",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"truth = []\n",
"\n",
"for row in out_lists[len(out_lists) * 4 // 5:]:\n",
" truth.extend(row[1])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:47:57.286431Z",
"iopub.status.busy": "2020-11-19T11:47:57.285228Z",
"iopub.status.idle": "2020-11-19T11:47:58.654336Z",
"shell.execute_reply": "2020-11-19T11:47:58.654982Z"
},
"papermill": {
"duration": 1.550192,
"end_time": "2020-11-19T11:47:58.655158",
"exception": false,
"start_time": "2020-11-19T11:47:57.104966",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"from sklearn.metrics import classification_report"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:47:58.995628Z",
"iopub.status.busy": "2020-11-19T11:47:58.965974Z",
"iopub.status.idle": "2020-11-19T11:47:59.138731Z",
"shell.execute_reply": "2020-11-19T11:47:59.137881Z"
},
"papermill": {
"duration": 0.34133,
"end_time": "2020-11-19T11:47:59.138859",
"exception": false,
"start_time": "2020-11-19T11:47:58.797529",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"prediction = []\n",
"\n",
"file = open(\"deo/hasil.out\")\n",
"\n",
"data = file.readlines()\n",
"\n",
"for row in data:\n",
" result = row.strip().split(\" \")\n",
" if len(result) == 2 and result[1] != '1.0000':\n",
" prediction.append(result[1])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:47:59.552562Z",
"iopub.status.busy": "2020-11-19T11:47:59.510591Z",
"iopub.status.idle": "2020-11-19T11:48:01.726526Z",
"shell.execute_reply": "2020-11-19T11:48:01.725444Z"
},
"papermill": {
"duration": 2.4448,
"end_time": "2020-11-19T11:48:01.726707",
"exception": false,
"start_time": "2020-11-19T11:47:59.281907",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/opt/conda/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" ART 0.00 0.00 0.00 1518\n",
" B-ART 0.00 0.00 0.00 0\n",
" B-Event 0.00 0.00 0.00 0\n",
" B-Location 0.00 0.00 0.00 0\n",
"B-Miscellaneous 0.00 0.00 0.00 0\n",
" B-NHFC 0.00 0.00 0.00 0\n",
" B-NORP 0.00 0.00 0.00 0\n",
" B-Organization 0.00 0.00 0.00 0\n",
" B-Person 0.00 0.00 0.00 0\n",
" B-Product 0.00 0.00 0.00 0\n",
" B-Time 0.00 0.00 0.00 0\n",
" Event 0.00 0.00 0.00 1803\n",
" I-ART 0.00 0.00 0.00 0\n",
" I-Event 0.00 0.00 0.00 0\n",
" I-Location 0.00 0.00 0.00 0\n",
"I-Miscellaneous 0.00 0.00 0.00 0\n",
" I-Organization 0.00 0.00 0.00 0\n",
" I-Person 0.00 0.00 0.00 0\n",
" I-Product 0.00 0.00 0.00 0\n",
" I-Time 0.00 0.00 0.00 0\n",
" Language 0.00 0.00 0.00 37\n",
" Location 0.00 0.00 0.00 4357\n",
" Miscellaneous 0.00 0.00 0.00 1422\n",
" NHFC 0.00 0.00 0.00 365\n",
" NORP 0.00 0.00 0.00 250\n",
" O 0.96 0.99 0.98 116161\n",
" Organization 0.00 0.00 0.00 4755\n",
" Person 0.00 0.00 0.00 4439\n",
" Product 0.00 0.00 0.00 1179\n",
" Time 0.00 0.00 0.00 3407\n",
"\n",
" accuracy 0.82 139693\n",
" macro avg 0.03 0.03 0.03 139693\n",
" weighted avg 0.80 0.82 0.81 139693\n",
"\n"
]
}
],
"source": [
"print(classification_report(truth , prediction))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
},
"papermill": {
"duration": 1959.414764,
"end_time": "2020-11-19T11:48:01.990886",
"environment_variables": {},
"exception": null,
"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2020-11-19T11:15:22.576122",
"version": "2.1.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| 0047/233/47233566.ipynb | s3://data-agents/kaggle-outputs/sharded/010_00047.jsonl.gz |
{
"cells": [
{
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"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"execution": {
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"exception": false,
"start_time": "2020-11-19T11:18:11.529644",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import time"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:18:11.585052Z",
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},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"0 32\n",
"1 7\n",
"2 10\n",
"3 81\n",
"4 67\n",
" ... \n",
"393651 171\n",
"393652 514\n",
"393653 38\n",
"393654 172\n",
"393655 15\n",
"Name: questions_attempted, Length: 393656, dtype: int32"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results_c = pd.read_parquet('../input/riiid-data-prparation/results_question.parq')\n",
"results_u = pd.read_parquet('../input/riiid-data-prparation/results_user.parq')\n",
"results_u.questions_attempted.astype('int32')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:18:11.787601Z",
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"exception": false,
"start_time": "2020-11-19T11:18:11.772082",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"import gc\n",
"import riiideducation"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:18:11.824773Z",
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"start_time": "2020-11-19T11:18:11.808833",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"import lightgbm as lgbm"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:18:13.115028Z",
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"env = riiideducation.make_env()\n",
"iter_tester = env.iter_test()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:18:13.143425Z",
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"start_time": "2020-11-19T11:18:13.127596",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"model = lgbm.Booster(model_file='../input/riiid-training/booster1.txt')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:18:38.529983Z",
"iopub.status.busy": "2020-11-19T11:18:38.529003Z",
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"start_time": "2020-11-19T11:18:38.512786",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"label = 'answered_correctly'\n",
"categorical = ['content_id','user_id','prior_question_had_explanation']\n",
"continuous = ['prior_question_elapsed_time','answered_correctly_user','questions_attempted','answered_correctly_content']"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:18:38.561765Z",
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"params = {'num_leaves':15,'objective':'binary','boosting':'goss','device_type':'cpu','learning_rate':0.2,\n",
" 'random_seed':0}\n",
"params['metric'] = ['auc','binary_logloss']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:18:38.591741Z",
"iopub.status.busy": "2020-11-19T11:18:38.590970Z",
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"used_cols = categorical+continuous"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:18:38.628576Z",
"iopub.status.busy": "2020-11-19T11:18:38.627588Z",
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"exception": false,
"start_time": "2020-11-19T11:18:38.605591",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"for test_df, sample_prediction_df in iter_tester:\n",
" test_df.prior_question_had_explanation = test_df.prior_question_had_explanation.fillna(False)\n",
" test_df['prior_question_had_explanation'] %= 2\n",
" test_df.prior_question_had_explanation= test_df.prior_question_had_explanation.astype('int8')\n",
" test_df = test_df.merge(results_c,on='content_id').reset_index()\n",
" temp = test_df.merge(results_u,on='user_id')\n",
" test_df['answered_correctly'] = 0.65\n",
" test_df.loc[temp['index'].to_list(),'answered_correctly'] = model.predict(temp[used_cols])\n",
" env.predict(test_df.loc[test_df['content_type_id']==0,['row_id','answered_correctly']])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
},
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"duration": 33.897379,
"end_time": "2020-11-19T11:18:39.810085",
"environment_variables": {},
"exception": null,
"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2020-11-19T11:18:05.912706",
"version": "2.1.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| 0047/233/47233722.ipynb | s3://data-agents/kaggle-outputs/sharded/010_00047.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"execution": {
"iopub.execute_input": "2020-11-19T11:20:19.245233Z",
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},
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m W&B installed but not logged in. Run `wandb login` or set the WANDB_API_KEY env variable.\n"
]
}
],
"source": [
"import pandas as pd \n",
"import numpy as np\n",
"import tensorflow as tf\n",
"import tensorflow.keras.backend as K\n",
"from sklearn.model_selection import StratifiedKFold\n",
"from keras.layers import Dense, Flatten, Conv1D, Dropout, Input\n",
"from keras.models import Model, load_model\n",
"from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
"from transformers import *\n",
"import tokenizers"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
"_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
"execution": {
"iopub.execute_input": "2020-11-19T11:20:27.772510Z",
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},
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"start_time": "2020-11-19T11:20:27.757020",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"data_path = '../input/tweet-sentiment-extraction'\n",
"path = '../input/roberta-base'\n",
"path2 = '../input/tf-roberta'\n",
"max_len = 128\n",
"vocab_file = path + '/vocab.json'\n",
"merges_file = path + '/merges.txt'\n",
"config_file = path2 + '/config-roberta-base.json'\n",
"pretrained_file = path2 + '/pretrained-roberta-base.h5'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:20:27.917061Z",
"iopub.status.busy": "2020-11-19T11:20:27.909596Z",
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},
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"exception": false,
"start_time": "2020-11-19T11:20:27.782580",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"#tokenizer for roberta\n",
"tokenizer = tokenizers.ByteLevelBPETokenizer(\n",
" vocab_file, \n",
" merges_file, \n",
" lowercase=True,\n",
" add_prefix_space=True\n",
")\n",
"sentiment_id = {'positive':tokenizer.encode('positive').ids[0], \n",
" 'negative':tokenizer.encode('negative').ids[0], \n",
" 'neutral':tokenizer.encode('neutral').ids[0]}"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:20:27.946923Z",
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},
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"end_time": "2020-11-19T11:20:28.097912",
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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"<div>\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>textID</th>\n",
" <th>text</th>\n",
" <th>selected_text</th>\n",
" <th>sentiment</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>cb774db0d1</td>\n",
" <td>I`d have responded, if I were going</td>\n",
" <td>I`d have responded, if I were going</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>549e992a42</td>\n",
" <td>Sooo SAD I will miss you here in San Diego!!!</td>\n",
" <td>Sooo SAD</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>088c60f138</td>\n",
" <td>my boss is bullying me...</td>\n",
" <td>bullying me</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>9642c003ef</td>\n",
" <td>what interview! leave me alone</td>\n",
" <td>leave me alone</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>358bd9e861</td>\n",
" <td>Sons of ****, why couldn`t they put them on t...</td>\n",
" <td>Sons of ****,</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" textID text \\\n",
"0 cb774db0d1 I`d have responded, if I were going \n",
"1 549e992a42 Sooo SAD I will miss you here in San Diego!!! \n",
"2 088c60f138 my boss is bullying me... \n",
"3 9642c003ef what interview! leave me alone \n",
"4 358bd9e861 Sons of ****, why couldn`t they put them on t... \n",
"\n",
" selected_text sentiment \n",
"0 I`d have responded, if I were going neutral \n",
"1 Sooo SAD negative \n",
"2 bullying me negative \n",
"3 leave me alone negative \n",
"4 Sons of ****, negative "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_data = pd.read_csv(data_path + '/train.csv')\n",
"train_data.dropna(axis = 0,inplace=True)\n",
"train_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>textID</th>\n",
" <th>text</th>\n",
" <th>sentiment</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>f87dea47db</td>\n",
" <td>Last session of the day http://twitpic.com/67ezh</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>96d74cb729</td>\n",
" <td>Shanghai is also really exciting (precisely -...</td>\n",
" <td>positive</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>eee518ae67</td>\n",
" <td>Recession hit Veronique Branquinho, she has to...</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>01082688c6</td>\n",
" <td>happy bday!</td>\n",
" <td>positive</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>33987a8ee5</td>\n",
" <td>http://twitpic.com/4w75p - I like it!!</td>\n",
" <td>positive</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" textID text sentiment\n",
"0 f87dea47db Last session of the day http://twitpic.com/67ezh neutral\n",
"1 96d74cb729 Shanghai is also really exciting (precisely -... positive\n",
"2 eee518ae67 Recession hit Veronique Branquinho, she has to... negative\n",
"3 01082688c6 happy bday! positive\n",
"4 33987a8ee5 http://twitpic.com/4w75p - I like it!! positive"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_data = pd.read_csv(data_path + '/test.csv')\n",
"test_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:20:28.191000Z",
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"#Tokenizing the training data (input data formating for training)\n",
"\n",
"train_data.reset_index(inplace=True)\n",
"tot_tw = train_data.shape[0]\n",
"\n",
"input_ids = np.ones((tot_tw, max_len), dtype='int32')\n",
"attention_mask = np.zeros((tot_tw, max_len), dtype='int32')\n",
"token_type_ids = np.zeros((tot_tw, max_len), dtype='int32')\n",
"start_mask = np.zeros((tot_tw, max_len), dtype='int32')\n",
"end_mask = np.zeros((tot_tw, max_len), dtype='int32')\n",
"\n",
"for i in range(tot_tw):\n",
" set1 = \" \"+\" \".join(train_data.loc[i,'text'].split())\n",
" set2 = \" \".join(train_data.loc[i,'selected_text'].split())\n",
" idx = set1.find(set2)\n",
" set2_loc = np.zeros((len(set1)))\n",
" set2_loc[idx:idx+len(set2)]=1\n",
" if set1[idx-1]==\" \":\n",
" set2_loc[idx-1]=1\n",
" \n",
" enc_set1 = tokenizer.encode(set1)\n",
"\n",
" selected_text_token_idx=[]\n",
" for k,(a,b) in enumerate(enc_set1.offsets):\n",
" sm = np.sum(set2_loc[a:b]) \n",
" if sm > 0:\n",
" selected_text_token_idx.append(k)\n",
"\n",
" senti_token = sentiment_id[train_data.loc[i,'sentiment']]\n",
" input_ids[i,:len(enc_set1.ids)+5] = [0]+enc_set1.ids+[2,2]+[senti_token]+[2] \n",
" attention_mask[i,:len(enc_set1.ids)+5]=1\n",
"\n",
" if len(selected_text_token_idx) > 0:\n",
" start_mask[i,selected_text_token_idx[0]+1]=1\n",
" end_mask[i, selected_text_token_idx[-1]+1]=1"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:20:35.856124Z",
"iopub.status.busy": "2020-11-19T11:20:35.855230Z",
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"exception": false,
"start_time": "2020-11-19T11:20:35.836173",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"#Tokenizing the test data exactly the same way as the training data\n",
"\n",
"tot_test_tw = test_data.shape[0]\n",
"\n",
"input_ids_t = np.ones((tot_test_tw,max_len), dtype='int32')\n",
"attention_mask_t = np.zeros((tot_test_tw,max_len), dtype='int32')\n",
"token_type_ids_t = np.zeros((tot_test_tw,max_len), dtype='int32')\n",
"\n",
"for i in range(tot_test_tw):\n",
" set1 = \" \"+\" \".join(test_data.loc[i,'text'].split())\n",
" enc_set1 = tokenizer.encode(set1)\n",
"\n",
" s_token = sentiment_id[test_data.loc[i,'sentiment']]\n",
" input_ids_t[i,:len(enc_set1.ids)+5]=[0]+enc_set1.ids+[2,2]+[s_token]+[2]\n",
" attention_mask_t[i,:len(enc_set1.ids)+5]=1"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:20:36.238258Z",
"iopub.status.busy": "2020-11-19T11:20:36.237640Z",
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"#Metric Function\n",
"# Categorical Cross Entropy with Label Smoothing\n",
"# Label Smoothing is done to enhance accuracy\n",
"def custom_loss(y_true, y_pred):\n",
" loss = tf.keras.losses.categorical_crossentropy(y_true, y_pred, from_logits = False, label_smoothing = 0.20)\n",
" loss = tf.reduce_mean(loss)\n",
" return loss"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:20:36.280018Z",
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"#Function for building the model\n",
"def build_model():\n",
" ids = tf.keras.layers.Input((max_len,), dtype=tf.int32)\n",
" att = tf.keras.layers.Input((max_len,), dtype=tf.int32)\n",
" tok = tf.keras.layers.Input((max_len,), dtype=tf.int32) \n",
"\n",
" config_path = RobertaConfig.from_pretrained(config_file)\n",
" # I used a pre-trained model here\n",
" roberta_model = TFRobertaModel.from_pretrained(pretrained_file, config=config_path)\n",
" x = roberta_model(ids, attention_mask = att, token_type_ids=tok)\n",
" \n",
" x1 = tf.keras.layers.Dropout(0.05)(x[0])\n",
" x1 = tf.keras.layers.Conv1D(128, 2,padding='same')(x1) #128 is the no. of filters and 2 is the kernel size of each filter\n",
" x1 = tf.keras.layers.LeakyReLU()(x1)\n",
" x1 = tf.keras.layers.Conv1D(64, 2,padding='same')(x1)\n",
" x1 = tf.keras.layers.Dense(1)(x1)\n",
" x1 = tf.keras.layers.Flatten()(x1)\n",
" x1 = tf.keras.layers.Activation('softmax')(x1)\n",
" \n",
" x2 = tf.keras.layers.Dropout(0.05)(x[0]) \n",
" x2 = tf.keras.layers.Conv1D(128, 2,padding='same')(x2)\n",
" x2 = tf.keras.layers.LeakyReLU()(x2)\n",
" x2 = tf.keras.layers.Conv1D(64, 2, padding='same')(x2)\n",
" x2 = tf.keras.layers.Dense(1)(x2)\n",
" x2 = tf.keras.layers.Flatten()(x2)\n",
" x2 = tf.keras.layers.Activation('softmax')(x2)\n",
"\n",
"\n",
" model = tf.keras.models.Model(inputs=[ids, att, tok], outputs=[x1,x2])\n",
" optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5) \n",
" model.compile(loss=custom_loss, optimizer=optimizer)\n",
"\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:20:36.356961Z",
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------------------------------------\n",
"-- FOLD 1 --\n",
"----------------------------------------\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"All model checkpoint layers were used when initializing TFRobertaModel.\n",
"\n",
"All the layers of TFRobertaModel were initialized from the model checkpoint at ../input/tf-roberta/pretrained-roberta-base.h5.\n",
"If your task is similar to the task the model of the checkpoint was trained on, you can already use TFRobertaModel for predictions without further training.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"111/111 [==============================] - 19s 167ms/step\n",
"----------------------------------------\n",
"-- FOLD 2 --\n",
"----------------------------------------\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"All model checkpoint layers were used when initializing TFRobertaModel.\n",
"\n",
"All the layers of TFRobertaModel were initialized from the model checkpoint at ../input/tf-roberta/pretrained-roberta-base.h5.\n",
"If your task is similar to the task the model of the checkpoint was trained on, you can already use TFRobertaModel for predictions without further training.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"111/111 [==============================] - 19s 170ms/step\n",
"----------------------------------------\n",
"-- FOLD 3 --\n",
"----------------------------------------\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"All model checkpoint layers were used when initializing TFRobertaModel.\n",
"\n",
"All the layers of TFRobertaModel were initialized from the model checkpoint at ../input/tf-roberta/pretrained-roberta-base.h5.\n",
"If your task is similar to the task the model of the checkpoint was trained on, you can already use TFRobertaModel for predictions without further training.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"111/111 [==============================] - 19s 168ms/step\n",
"----------------------------------------\n",
"-- FOLD 4 --\n",
"----------------------------------------\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"All model checkpoint layers were used when initializing TFRobertaModel.\n",
"\n",
"All the layers of TFRobertaModel were initialized from the model checkpoint at ../input/tf-roberta/pretrained-roberta-base.h5.\n",
"If your task is similar to the task the model of the checkpoint was trained on, you can already use TFRobertaModel for predictions without further training.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"111/111 [==============================] - 19s 168ms/step\n",
"----------------------------------------\n",
"-- FOLD 5 --\n",
"----------------------------------------\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"All model checkpoint layers were used when initializing TFRobertaModel.\n",
"\n",
"All the layers of TFRobertaModel were initialized from the model checkpoint at ../input/tf-roberta/pretrained-roberta-base.h5.\n",
"If your task is similar to the task the model of the checkpoint was trained on, you can already use TFRobertaModel for predictions without further training.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"111/111 [==============================] - 19s 170ms/step\n"
]
}
],
"source": [
"#Training the model with 5 stratified kFolds\n",
"\n",
"pred_start= np.zeros((input_ids_t.shape[0],max_len))\n",
"pred_end= np.zeros((input_ids_t.shape[0],max_len))\n",
"\n",
"skf = StratifiedKFold(n_splits=5,shuffle=True,random_state=777)\n",
"\n",
"for i,(idxT,idxV) in enumerate(skf.split(input_ids,train_data.sentiment.values)):\n",
" print('--'*20)\n",
" print('-- FOLD %i --'%(i+1))\n",
" print('--'*20)\n",
" K.clear_session()\n",
" model = build_model()\n",
" '''\n",
" sv = tf.keras.callbacks.ModelCheckpoint(\n",
" 'roberta-%i.h5'%(i), monitor='val_loss', verbose=1, save_best_only=True,\n",
" save_weights_only=True, mode='auto', save_freq='epoch')\n",
" \n",
" model.fit([input_ids[idxT,], attention_mask[idxT,], token_type_ids[idxT,]], [start_tokens[idxT,], end_tokens[idxT,]], \n",
" epochs=3, batch_size=32, verbose=DISPLAY, callbacks=[sv],\n",
" validation_data=([input_ids[idxV,],attention_mask[idxV,],token_type_ids[idxV,]], \n",
" [start_tokens[idxV,], end_tokens[idxV,]]))\n",
" '''\n",
" model.load_weights('../input/tweet-model-data/roberta-%i.h5'%(i))\n",
" pred = model.predict([input_ids_t,attention_mask_t,token_type_ids_t],verbose=1)\n",
" pred_start = pred_start + (pred[0]/5)\n",
" pred_end = pred_end + (pred[1]/5) # 5 is the total no. of splits here"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:23:03.531836Z",
"iopub.status.busy": "2020-11-19T11:23:03.292325Z",
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"all = []\n",
"for k in range(input_ids_t.shape[0]):\n",
" a = np.argmax(pred_start[k,])\n",
" b = np.argmax(pred_end[k,])\n",
" if a>b: \n",
" st = test_data.loc[k,'text'] \n",
" else:\n",
" text1 = \" \"+\" \".join(test_data.loc[k,'text'].split())\n",
" enc = tokenizer.encode(text1)\n",
" st = tokenizer.decode(enc.ids[a-1:b])\n",
" all.append(st)\n",
"test_data['selected_text']=all\n",
"test_data.head(20)\n",
"test_data[['textID','selected_text']].to_csv('submission.csv', index=False)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
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"nbconvert_exporter": "python",
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},
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"environment_variables": {},
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"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2020-11-19T11:20:15.335793",
"version": "2.1.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| 0047/233/47233862.ipynb | s3://data-agents/kaggle-outputs/sharded/010_00047.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:21:52.449365Z",
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['steam-200k.csv']\n"
]
}
],
"source": [
"import os\n",
"print(os.listdir(\"../input/steam-video-games\"))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "1ac4a83c-5e68-4b25-b94d-36f25daa7a48",
"_uuid": "f18f123e-51e2-49c5-8c27-e6c4286dfd7a",
"execution": {
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/kaggle/input/steam-video-games/steam-200k.csv\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Benutzer</th>\n",
" <th>Spiel</th>\n",
" <th>Aktion</th>\n",
" <th>Stunden</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>151603712</td>\n",
" <td>The Elder Scrolls V Skyrim</td>\n",
" <td>purchase</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>151603712</td>\n",
" <td>The Elder Scrolls V Skyrim</td>\n",
" <td>play</td>\n",
" <td>273.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>151603712</td>\n",
" <td>Fallout 4</td>\n",
" <td>purchase</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>151603712</td>\n",
" <td>Fallout 4</td>\n",
" <td>play</td>\n",
" <td>87.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>151603712</td>\n",
" <td>Spore</td>\n",
" <td>purchase</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Benutzer Spiel Aktion Stunden\n",
"0 151603712 The Elder Scrolls V Skyrim purchase 1.0\n",
"1 151603712 The Elder Scrolls V Skyrim play 273.0\n",
"2 151603712 Fallout 4 purchase 1.0\n",
"3 151603712 Fallout 4 play 87.0\n",
"4 151603712 Spore purchase 1.0"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load\n",
"\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib import ticker as plt_ticker\n",
"import numpy as np\n",
"import pandas as pd\n",
"import re\n",
"import seaborn as sns\n",
"from sklearn.cluster import KMeans\n",
"\n",
"# Input data files are available in the read-only \"../input/\" directory\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
"\n",
"import os\n",
"for dirname, _, filenames in os.walk('/kaggle/input'):\n",
" for filename in filenames:\n",
" print(os.path.join(dirname, filename))\n",
"\n",
"# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
"# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session\n",
"\n",
"\n",
"df = pd.read_csv(\"../input/steam-video-games/steam-200k.csv\", header=None, index_col=None, usecols = ['Benutzer', 'Spiel', 'Aktion', 'Stunden'], names=['Benutzer', 'Spiel', 'Aktion', 'Stunden', 'dummy'])\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
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},
"outputs": [
{
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\n",
"text/plain": [
"<Figure size 1440x720 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"nb_games = 10\n",
"df_purchase = df.loc[df['Aktion'] == 'purchase']\n",
"purchased_times = df_purchase.groupby('Spiel')['Spiel'].agg('count').sort_values(ascending=False)\n",
"purchased_times = pd.DataFrame({'Spiel': purchased_times.index, 'Erworben (Gesamt)': purchased_times.values})[0:nb_games]\n",
"\n",
"df_play = df.loc[df['Aktion'] == 'play']\n",
"hours_played = df_play.groupby('Spiel')['Stunden'].agg(np.sum).sort_values(ascending=False)\n",
"hours_played = pd.DataFrame({'Spiel': hours_played.index, 'Spielzeit in Stunden': hours_played.values})[0:nb_games]\n",
"\n",
"df_avg_played = df.loc[df['Aktion'] == 'play']\n",
"avg_played = df_avg_played.groupby('Spiel')['Stunden'].agg(np.average).sort_values(ascending=False)\n",
"avg_played = pd.DataFrame({'Spiel': avg_played.index, 'Durchschnittliche Spielzeit in Stunden': avg_played.values})[0:nb_games]\n",
"\n",
"\n",
"fig, ax = plt.subplots(1,2,figsize=(20,nb_games))\n",
"\n",
"sns.barplot(y = 'Spiel', x = 'Erworben (Gesamt)', data = purchased_times, ax=ax[0])\n",
"sns.barplot(y = 'Spiel', x = 'Spielzeit in Stunden', data = hours_played, ax=ax[1])\n",
"#sns.barplot(y = 'Spiel', x = 'Durchschnittliche Spielzeit in Stunden', data = avg_played, ax=ax[2])\n",
"plt.tight_layout()\n",
"#loc = plt_ticker.LinearLocator(numticks=10) # this locator puts ticks at regular intervals\n",
"#formatter = \n",
"ax[1].xaxis.set_major_locator(plt_ticker.MultipleLocator(100000))\n",
"ax[1].xaxis.set_major_formatter(plt_ticker.ScalarFormatter())\n",
"ax[1].xaxis.get_major_formatter().set_scientific(False)\n",
"#ax[1].ticklabel_format(useOffset=False)\n",
"ax[1].yaxis.tick_right()\n",
"ax[0].yaxis.set_label_position(\"right\")\n",
"ax[0].set_ylabel('Top 10 Meisterworben')\n",
"ax[1].yaxis.set_label_position(\"left\")\n",
"ax[1].set_ylabel('Top 10 Meistgespielt')\n",
"for i in range(0,2):\n",
" ax[i].tick_params(axis='y', labelsize=18)\n",
" ax[i].xaxis.label.set_size(20)\n",
" ax[i].yaxis.label.set_size(15)\n",
" ax[i].grid(b=True, axis='x')\n",
" \n",
"plt.savefig(fname='figures.png', bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:21:55.869522Z",
"iopub.status.busy": "2020-11-19T11:21:55.868234Z",
"iopub.status.idle": "2020-11-19T11:21:55.902453Z",
"shell.execute_reply": "2020-11-19T11:21:55.901762Z"
},
"papermill": {
"duration": 0.084431,
"end_time": "2020-11-19T11:21:55.902629",
"exception": false,
"start_time": "2020-11-19T11:21:55.818198",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Benutzer</th>\n",
" <th>Stunden</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>7.048900e+04</td>\n",
" <td>70489.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1.058812e+08</td>\n",
" <td>48.878063</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>7.150365e+07</td>\n",
" <td>229.335236</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>5.250000e+03</td>\n",
" <td>0.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>4.934256e+07</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>9.169067e+07</td>\n",
" <td>4.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.556738e+08</td>\n",
" <td>19.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>3.099031e+08</td>\n",
" <td>11754.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Benutzer Stunden\n",
"count 7.048900e+04 70489.000000\n",
"mean 1.058812e+08 48.878063\n",
"std 7.150365e+07 229.335236\n",
"min 5.250000e+03 0.100000\n",
"25% 4.934256e+07 1.000000\n",
"50% 9.169067e+07 4.500000\n",
"75% 1.556738e+08 19.100000\n",
"max 3.099031e+08 11754.000000"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_play2 = df.loc[df['Aktion'] == 'play']\n",
"df_play2.describe()\n",
"#df_filtered = df_play2.loc[df_play2['Stunden'] > 0.0]\n",
"#df_filtered.describe()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:21:56.026021Z",
"iopub.status.busy": "2020-11-19T11:21:56.020195Z",
"iopub.status.idle": "2020-11-19T11:21:56.891518Z",
"shell.execute_reply": "2020-11-19T11:21:56.892182Z"
},
"papermill": {
"duration": 0.978235,
"end_time": "2020-11-19T11:21:56.892354",
"exception": false,
"start_time": "2020-11-19T11:21:55.914119",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"top_games = 10\n",
"sum_hours_played = df_play.groupby('Spiel')['Stunden'].agg(np.sum).sort_values(ascending=False)\n",
"top_played = pd.DataFrame({'Spiel': sum_hours_played.index, 'Spielzeit in Stunden': sum_hours_played.values})[0:top_games]\n",
"#top_played.sort_values(ascending=False, by='Spielzeit in Stunden')\n",
"\n",
"df_only_played = df.loc[df['Aktion'] == 'play']\n",
"\n",
"#mask = df['Spiel'].isin(top_played['Spiel'])\n",
"mask = df_only_played['Spiel'].isin(top_played['Spiel'])\n",
"\n",
"df_for_boxplot_pre = df.loc[df['Aktion'] == 'play'].loc[mask][['Stunden', 'Spiel']]\n",
"#df_for_boxplot_pre = df.loc[mask][['Stunden', 'Spiel']]\n",
"#df_for_boxplot = df_for_boxplot_pre['Stunden', 'Spiel']\n",
"sns.set(rc={\"axes.grid\":False,\n",
" 'axes.labelsize':17,\n",
" 'figure.figsize':(20.0, 10.0),\n",
" 'xtick.labelsize':20,\n",
" 'ytick.labelsize':20})\n",
"\n",
"sns.set_style(\"darkgrid\")\n",
"boxplot = sns.boxplot(width=.4, x=\"Stunden\", y=\"Spiel\", data=df_for_boxplot_pre, palette=\"Set3\", showfliers=False, order=['Dota 2', 'Counter-Strike Global Offensive', 'Team Fortress 2', 'Counter-Strike', \"Sid Meier's Civilization V\", 'Counter-Strike Source', 'The Elder Scrolls V Skyrim', \"Garry's Mod\", 'Call of Duty Modern Warfare 2 - Multiplayer', 'Left 4 Dead 2'])\n",
"#custom_ax2 = sns.boxplot(width=.4, x=\"Stunden\", y=\"Spiel\", data=df_for_boxplot_pre, palette=\"Set3\", showfliers=True)#, order=['Dota 2', 'Counter-Strike Global Offensive', 'Team Fortress 2', 'Counter-Strike', \"Sid Meier's Civilization V\", 'Counter-Strike Source', 'The Elder Scrolls V Skyrim', \"Garry's Mod\", 'Call of Duty Modern Warfare 2 - Multiplayer', 'Left 4 Dead 2'])\n",
"plt.ylabel('Top 10 Meistgespielt')\n",
"plt.xlabel('Spielzeiten in Stunden')\n",
"#plt.savefig(fname='boxplot_no_fliers.png', bbox_inches='tight')\n",
"fig = boxplot.get_figure()\n",
"fig.savefig('boxplot_no_fliers.png') "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
},
"papermill": {
"duration": 9.982553,
"end_time": "2020-11-19T11:21:57.014084",
"environment_variables": {},
"exception": null,
"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2020-11-19T11:21:47.031531",
"version": "2.1.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| 0047/233/47233975.ipynb | s3://data-agents/kaggle-outputs/sharded/010_00047.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.025357,
"end_time": "2020-11-19T11:26:23.111615",
"exception": false,
"start_time": "2020-11-19T11:26:23.086258",
"status": "completed"
},
"tags": []
},
"source": [
"<h1 style = \"background-color: #00005F; color:white; font:Arial\" > Analise utilizando regressão linear </h1>\n",
"<h2> Prevendo a qualidade do vinho utilizando regressão linear. </h2>\n",
"\n",
"<h3> Verificando as melhores variáveis para qualificar o melhor vinho. </h3>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"execution": {
"iopub.execute_input": "2020-11-19T11:26:23.162875Z",
"iopub.status.busy": "2020-11-19T11:26:23.162026Z",
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"name": "stdout",
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"text": [
"/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load\n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Input data files are available in the read-only \"../input/\" directory\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
"\n",
"import os\n",
"for dirname, _, filenames in os.walk('/kaggle/input'):\n",
" for filename in filenames:\n",
" print(os.path.join(dirname, filename))\n",
" df = pd.read_csv(os.path.join(dirname, filename))\n",
"\n",
"# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
"# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
]
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{
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{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1599 entries, 0 to 1598\n",
"Data columns (total 12 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 fixed acidity 1599 non-null float64\n",
" 1 volatile acidity 1599 non-null float64\n",
" 2 citric acid 1599 non-null float64\n",
" 3 residual sugar 1599 non-null float64\n",
" 4 chlorides 1599 non-null float64\n",
" 5 free sulfur dioxide 1599 non-null float64\n",
" 6 total sulfur dioxide 1599 non-null float64\n",
" 7 density 1599 non-null float64\n",
" 8 pH 1599 non-null float64\n",
" 9 sulphates 1599 non-null float64\n",
" 10 alcohol 1599 non-null float64\n",
" 11 quality 1599 non-null int64 \n",
"dtypes: float64(11), int64(1)\n",
"memory usage: 150.0 KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
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"metadata": {
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{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>fixed acidity</th>\n",
" <th>volatile acidity</th>\n",
" <th>citric acid</th>\n",
" <th>residual sugar</th>\n",
" <th>chlorides</th>\n",
" <th>free sulfur dioxide</th>\n",
" <th>total sulfur dioxide</th>\n",
" <th>density</th>\n",
" <th>pH</th>\n",
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" <th>alcohol</th>\n",
" <th>quality</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>8.319637</td>\n",
" <td>0.527821</td>\n",
" <td>0.270976</td>\n",
" <td>2.538806</td>\n",
" <td>0.087467</td>\n",
" <td>15.874922</td>\n",
" <td>46.467792</td>\n",
" <td>0.996747</td>\n",
" <td>3.311113</td>\n",
" <td>0.658149</td>\n",
" <td>10.422983</td>\n",
" <td>5.636023</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.741096</td>\n",
" <td>0.179060</td>\n",
" <td>0.194801</td>\n",
" <td>1.409928</td>\n",
" <td>0.047065</td>\n",
" <td>10.460157</td>\n",
" <td>32.895324</td>\n",
" <td>0.001887</td>\n",
" <td>0.154386</td>\n",
" <td>0.169507</td>\n",
" <td>1.065668</td>\n",
" <td>0.807569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>4.600000</td>\n",
" <td>0.120000</td>\n",
" <td>0.000000</td>\n",
" <td>0.900000</td>\n",
" <td>0.012000</td>\n",
" <td>1.000000</td>\n",
" <td>6.000000</td>\n",
" <td>0.990070</td>\n",
" <td>2.740000</td>\n",
" <td>0.330000</td>\n",
" <td>8.400000</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>7.100000</td>\n",
" <td>0.390000</td>\n",
" <td>0.090000</td>\n",
" <td>1.900000</td>\n",
" <td>0.070000</td>\n",
" <td>7.000000</td>\n",
" <td>22.000000</td>\n",
" <td>0.995600</td>\n",
" <td>3.210000</td>\n",
" <td>0.550000</td>\n",
" <td>9.500000</td>\n",
" <td>5.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>7.900000</td>\n",
" <td>0.520000</td>\n",
" <td>0.260000</td>\n",
" <td>2.200000</td>\n",
" <td>0.079000</td>\n",
" <td>14.000000</td>\n",
" <td>38.000000</td>\n",
" <td>0.996750</td>\n",
" <td>3.310000</td>\n",
" <td>0.620000</td>\n",
" <td>10.200000</td>\n",
" <td>6.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>9.200000</td>\n",
" <td>0.640000</td>\n",
" <td>0.420000</td>\n",
" <td>2.600000</td>\n",
" <td>0.090000</td>\n",
" <td>21.000000</td>\n",
" <td>62.000000</td>\n",
" <td>0.997835</td>\n",
" <td>3.400000</td>\n",
" <td>0.730000</td>\n",
" <td>11.100000</td>\n",
" <td>6.000000</td>\n",
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" <tr>\n",
" <th>max</th>\n",
" <td>15.900000</td>\n",
" <td>1.580000</td>\n",
" <td>1.000000</td>\n",
" <td>15.500000</td>\n",
" <td>0.611000</td>\n",
" <td>72.000000</td>\n",
" <td>289.000000</td>\n",
" <td>1.003690</td>\n",
" <td>4.010000</td>\n",
" <td>2.000000</td>\n",
" <td>14.900000</td>\n",
" <td>8.000000</td>\n",
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],
"text/plain": [
" fixed acidity volatile acidity citric acid residual sugar \\\n",
"count 1599.000000 1599.000000 1599.000000 1599.000000 \n",
"mean 8.319637 0.527821 0.270976 2.538806 \n",
"std 1.741096 0.179060 0.194801 1.409928 \n",
"min 4.600000 0.120000 0.000000 0.900000 \n",
"25% 7.100000 0.390000 0.090000 1.900000 \n",
"50% 7.900000 0.520000 0.260000 2.200000 \n",
"75% 9.200000 0.640000 0.420000 2.600000 \n",
"max 15.900000 1.580000 1.000000 15.500000 \n",
"\n",
" chlorides free sulfur dioxide total sulfur dioxide density \\\n",
"count 1599.000000 1599.000000 1599.000000 1599.000000 \n",
"mean 0.087467 15.874922 46.467792 0.996747 \n",
"std 0.047065 10.460157 32.895324 0.001887 \n",
"min 0.012000 1.000000 6.000000 0.990070 \n",
"25% 0.070000 7.000000 22.000000 0.995600 \n",
"50% 0.079000 14.000000 38.000000 0.996750 \n",
"75% 0.090000 21.000000 62.000000 0.997835 \n",
"max 0.611000 72.000000 289.000000 1.003690 \n",
"\n",
" pH sulphates alcohol quality \n",
"count 1599.000000 1599.000000 1599.000000 1599.000000 \n",
"mean 3.311113 0.658149 10.422983 5.636023 \n",
"std 0.154386 0.169507 1.065668 0.807569 \n",
"min 2.740000 0.330000 8.400000 3.000000 \n",
"25% 3.210000 0.550000 9.500000 5.000000 \n",
"50% 3.310000 0.620000 10.200000 6.000000 \n",
"75% 3.400000 0.730000 11.100000 6.000000 \n",
"max 4.010000 2.000000 14.900000 8.000000 "
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P/NdW1SPbvw8AJHkocBTwsPY5b0yyx8gilaR1YLcJITPxkiRJksatqi4Cbh5w9SOBd1TVbVV1NXAVcNiaBSdJ69Agdxk7HXgD8NYF819bVa/pnbEgE38g8E9JHuztgiVJkiStkRcleT7wcWBzVX0NOAi4uGedbe28XSQ5HjgeYGZmhrm5uWUHMLMXbN64c5f5K9lWV+3YsWOiyrPQpJcPJr+Mlm/5dpsQqqqLkmwYcHt3ZOKBq5PMZ+I/suIIJUmSJKm/NwF/DFT7/1TgV4H0Wbf6baCqtgBbADZt2lSzs7PLDuL1Z57LqZftemi19ejlb6ur5ubmWMlrs15Mevlg8sto+ZZvkB5Cixl7Jr5LRpmN7Hf2AYZzBmJSsqqWo1sshyRJWgtVdcP84yR/A7yvndwGHNKz6sHA9SMMTZI6b6UJoU5k4rtklNnIY098f9/5wzgDMSlZVcvRLZZDkiSthSQHVNX2dvKXgPlxT88D3p7kz2mGsjgU+NgYQpSkzlpRQshMvCRJkqRRSnIWMAvsn2Qb8ApgNskjaU5CbwV+HaCqrkhyNvAZYCfwQsc1laQ7W1FCyEy8JEmSpFGqqmf3mf2WJdY/GTh57SKSpPVttwkhM/GSJK0PG3ouKd68cecdlxhvPeXJY4tDkiRJ3TTIXcbMxEuSJEmSJE2Qu4w7AEmSJEmSJI2WCSFJkiRJkqQps9LbzkuSJK3aYuMNjXrcI0mSpGljQkiSJO1Wv8SNSRtJkqT1y0vGJElrLslpSW5McnnPvP2SXJDk8+3/e/csOynJVUk+l+QJ44lakiRJmlwmhCRJo3A6cMSCeScCF1bVocCF7TRJHgocBTysfc4bk+wxulAlSZKkyWdCSJK05qrqIuDmBbOPBM5oH58BPL1n/juq6raquhq4CjhsJIFKkiRJU8IxhCRJ4zJTVdsBqmp7kvu18w8CLu5Zb1s7bxdJjgeOB5iZmWFubm7tol3Ejh07xrLffjZv3HnH45m9vjf9+jPP3WXdjQfda8XbnrdYufut26s3tsWM6zXt0vvZq6txQXdj62pc0O3YJEnTw4SQJKlr0mde9VuxqrYAWwA2bdpUs7OzaxhWf3Nzc4xjv/3vzvW9Zn3zxp2cetnizfzWo2eXtb9j+w0qvcg2+q3ba3exLbXttTau93N3uhoXdDe2rsYF3Y5NkjQ9vGRMkjQuNyQ5AKD9f2M7fxtwSM96BwPXjzg2SZIkaaKZEJIkjct5wDHt42OAc3vmH5XkbkkeCBwKfGwM8UmSJEkTy0vGJElrLslZwCywf5JtwCuAU4CzkxwHXAM8E6CqrkhyNvAZYCfwwqq6fSyBS5IkSRPKhJAkac1V1bMXWXT4IuufDJy8dhFJkiRJ081LxiRJkiR1XpLTktyY5PKeea9O8tkkn07yniT7tvM3JPmvJJe2f389vsglqZtMCEmSJElaD04Hjlgw7wLg4VX1o8B/Aif1LPtCVT2y/XvBiGKUpHVjtwkhM/GSJEmSxq2qLgJuXjDv/Kra2U5eTHNnSknSAAYZQ+h04A3AW3vmXQCcVFU7k/wfmkz8Ce2yL1TVI4capSRJkiQt7VeBd/ZMPzDJJ4FvAr9fVf/S70lJjgeOB5iZmWFubm7ZO57ZCzZv3LnL/JVsq6t27NgxUeVZaNLLB5NfRsu3fLtNCFXVRUk2LJh3fs/kxcAzhhqVJEmSJA0oyctp7kx5ZjtrO3D/qropyaOB9yZ5WFV9c+Fzq2oLsAVg06ZNNTs7u+z9v/7Mczn1sl0PrbYevfxtddXc3BwreW3Wi0kvH0x+GS3f8g3jLmNjy8R3ySizkf3OPsBwzkBMSlbVcnSL5ZAkSWslyTHAU4DDq6oAquo24Lb28SVJvgA8GPj42AKVpI5ZVUJo3Jn4LhllNvLYE9/fd/4wzkBMSlbVcnSL5ZAkSWshyRE0Q1f8fFV9q2f+fYGbq+r2JA8CDgW+OKYwJamTVnyXsZ5M/NG9mfiquql9fAkwn4mXJEmSpBVLchbwEeAhSbYlOY5mrNN9gAsW3NTm54BPJ/kUcA7wgqq6ue+GJWlKraiHkJl4SZIkSaNUVc/uM/sti6z7LuBdaxuRJK1vu00ItZn4WWD/JNuAV9DcVexuNJl4gIur6gU0mfg/SrITuB0z8ZIkSZIkSZ0zyF3GzMRLkiRJkiRNkBWPISRJkiRJkqT1yYSQJEmSJEnSlFnVbeclSdL02nDi+8cdgiRJklbIHkKSJEmSJElTxoSQJEmSJEnSlPGSMd3JZdd9g2P7XAKw9ZQnjyEaSZIkSZK0FuwhJEkaqyS/m+SKJJcnOSvJ3ZPsl+SCJJ9v/9973HFKkiRJk8SEkCRpbJIcBLwY2FRVDwf2AI4CTgQurKpDgQvbaUmSJElDYkJIkjRuewJ7JdkTuAdwPXAkcEa7/Azg6WOKTZIkSZpIjiEkSRqbqrouyWuAa4D/As6vqvOTzFTV9nad7Unu1+/5SY4HjgeYmZlhbm5uRJF/z44dO8ay380bdy65fGavpddZbsy7299y7C42WH58wzKu93N3uhoXdDe2rsYF3Y5NkjQ9TAhJksamHRvoSOCBwNeBv0/y3EGfX1VbgC0AmzZtqtnZ2bUIc0lzc3OMY7/9bgDQa/PGnZx62eLN/NajZ4e6v+XYXWyw/PiGZVzv5+50NS7obmxdjQu6HZskaXp4yZgkaZweB1xdVV+pqu8A7wZ+GrghyQEA7f8bxxijJEmSNHFMCEmSxuka4CeT3CNJgMOBK4HzgGPadY4Bzh1TfJIkSdJEMiEkSRqbqvoocA7wCeAymnZpC3AK8Pgknwce305LkqZYktOS3Jjk8p55+yW5IMnn2//37ll2UpKrknwuyRPGE7UkddduE0JWvJKktVRVr6iqH66qh1fV86rqtqq6qaoOr6pD2/83jztOSdLYnQ4csWDeicCFVXUocGE7TZKHAkcBD2uf88Yke4wuVEnqvkEGlT4deAPw1p558xXvKUlObKdPWFDxHgj8U5IHV9Xtww1bkiRNsg2LDGK99ZQnjzgSSV1RVRcl2bBg9pHAbPv4DGAOOKGd/46qug24OslVwGHAR0YRqyStB7tNCFnxSpI0eUy4SJoQM1W1HaCqtie5Xzv/IODinvW2tfN2keR44HiAmZkZ5ubmlh/EXs0dFBdayba6aseOHRNVnoUmvXww+WW0fMu30tvOd6Li7ZJRfvj6NTYwnAZnUhqzSakMLEe3TEo5JEmaAukzr/qtWFVbaMavY9OmTTU7O7vsnb3+zHM59bJdD622Hr38bXXV3NwcK3lt1otJLx9Mfhkt3/KtNCG0mJFWvF0yyg/fsYud1R1CgzMpjdmkVAaWo1smpRzqJnvsSNKK3JDkgPYk9QHAje38bcAhPesdDFw/8ugkqcNWmhCy4pUkaQQWSxRJkgA4DziG5m6UxwDn9sx/e5I/pxnb9FDgY2OJUJI6aqW3nZ+veGHXiveoJHdL8kCseCVJkiQNQZKzaMYmfUiSbUmOo0kEPT7J54HHt9NU1RXA2cBngA8CL/RGN5J0Z7vtIdRWvLPA/km2Aa+gqWjPbivha4BnQlPxJpmveHdixTtSXm4gSZKkSVVVz15k0eGLrH8ycPLaRSRJ69sgdxmz4l3nlnO5weaNaxiIJEmSJEnqhJVeMiZJkiRJkqR1ath3GZMkSZIkSa1+V2w4rIe6wB5CkiRJkiRJU8aEkCRJkiRJ0pTxkjFJkrRu2O1ekiRpOOwhJEmSJEmSNGXsIdRxy7llfNd5VleSJEmSpG6wh5AkSZIkSdKUMSEkSRqrJPsmOSfJZ5NcmeSnkuyX5IIkn2//33vccUqSJEmTxISQJGncXgd8sKp+GHgEcCVwInBhVR0KXNhOS5IkSRoSxxCSJI1NknsCPwccC1BV3wa+neRIYLZd7QxgDjhh9BFOn0kau06SJEmLMyEkSRqnBwFfAf42ySOAS4DfBmaqajtAVW1Pcr9+T05yPHA8wMzMDHNzcyMJuteOHTuGst/NG3euPpgeM3sNf5vDMuzYhvm+D+v9HLauxgXdja2rcUG3Y5MkTQ8TQpKkcdoTeBTwW1X10SSvYxmXh1XVFmALwKZNm2p2dnZNglzK3Nwcw9jvsUPumbN5405OvaybzfywY9t69OzQtjWs93PYuhoXdDe2rsYF3Y5Nw7VYr0vvtCupCxxDSJI0TtuAbVX10Xb6HJoE0Q1JDgBo/984pvgkSZKkidTNU4eSpKlQVV9Ocm2Sh1TV54DDgc+0f8cAp7T/zx1jmJKkDkvyEOCdPbMeBPxvYF/gf9Jcmgzwsqr6wIjDm0r2jJLWhxUnhKx4JUlD8lvAmUnuCnwR+BWaHqxnJzkOuAZ45hjjkyR1WHtC4ZEASfYArgPeQ9OevLaqXjPG8CSps1acELLilSQNQ1VdCmzqs+jwUcciSVr3Dge+UFVfSjLuWCSp04Z1yZgVryRJkqRxOwo4q2f6RUmeD3wc2FxVX1v4hGHcsXK5d09cj3eZW87d8RZ7Lbpc7rW8+1+/12M93xm1qyzf8g0rITSWirdL1urDN+pbBi/WmK3VLZXX6n2flMrAcnTLpJRDkqRJ1F56/DTgpHbWm4A/Bqr9fyrwqwufN4w7Vr7+zHOXdffEYd4dcVSWc3e8xe6c2eVyr+Xd//q9HuN4LSb9DoeWb/lWnRAaZ8XbJat9cxYbeG3U434vdivgYVRYo6wIJ6UysBzdMinl0PgtXudLklbhicAnquoGgPn/AEn+BnjfuAKTpC4axm3nd6l4q+r2qvou8DfAYUPYhyRJkiQt5dn0XLWQ5ICeZb8EXD7yiCSpw4bR/WSXireqtreTVrySJEmS1lSSewCPB369Z/arkjyS5sqFrQuWSdLUW1VCyIpXkiRJ0rhV1beA+yyY97wxhSNJ68KqEkJWvJIkSZIkSevPMMYQkiRJkiRJ0jpiQkiSJEmSJGnKmBCSJEmSJEmaMsO4y5imwIYT37/LvK2nPHkMkUiSJEmSpNUyIaR1r1+yavPGncyOPhRJkiRpqPr91gVPzkpaPS8ZkyRJkiRJmjImhCRJkiRJkqaMl4xJkiRJkjrFS+WktWcPIUmSJEmSpCljD6E1ZFZbkiRJ0iS47LpvcOyC45vlHtd452KpW0wIaegWS4RJ0mKS7AF8HLiuqp6SZD/gncAGYCvwrKr62vgilCRJXWBSSRoeLxmTJHXBbwNX9kyfCFxYVYcCF7bTkiRJkobEHkKSpLFKcjDwZOBk4H+1s48EZtvHZwBzwAmjjk3rg5doS5IkLZ8JIUnSuP0F8HvAPj3zZqpqO0BVbU9yv35PTHI8cDzAzMwMc3Nzaxzqrnbs2LHs/W7euHNtgukxs9do9rMSo4ptJZ+Hlbyfo9DVuKC7sXU1Luh2bJKk6bGqhFCSrcAtwO3Azqra5LgPu+cYO5LUSPIU4MaquiTJ7HKfX1VbgC0AmzZtqtnZZW9i1ebm5ljufhcOyrkWNm/cyamXdfO8z6hi23r07LKfs5L3cxS6Ghd0N7auxgXdjm298rhEkpZvGL/GHltVX+2Znh/34ZQkJ7bTdvOXJPXzGOBpSZ4E3B24Z5K3ATckOaDtHXQAcONYo5QkrQcel4yYJ7ql9W0tTs857oMkaSBVdRJwEkDbQ+glVfXcJK8GjgFOaf+fO7YgtW55Jxpp6nlcIklLWG1CqIDzkxTwf9uu+wON+yBJ0hJOAc5OchxwDfDMMccjSeq2FR+XDGM8uuWOjbacfSy23defueu5ko0H3Wvg7S7XWo3/tthrsVav52LWcmyvfmVZL+MerieWb/lWmxB6TFVd31auFyT57KBP7MJAoMPU783p6mCeS1lORd+Vyrvf/mb2Gk8lO2yTUqlZDg2iquZozt5SVTcBh48znmGwK70kjcyKj0uGMR7d6888d3ljo1126y6zFuvBuJxx51Yydtqgll3GAS0W86jLvZZje/Ury1q+V4uZ9PHLLN/yreobXVXXt/9vTPIe4DAGHPehCwOBDlO/N2cUg4YO23IG+uxK5d1vf5s37uRZ6/wzBZNTqVkOSZK0llZzXCJJ02rFCaEkewN3qapb2se/CPwRcB6O+yBJkjposV5bji0krV8el0jSyqymh9AM8ARoOX0AACAASURBVJ4k89t5e1V9MMl/4LgPU8FLISRJktQBE3Fc4m9rSaO24oRQVX0ReESf+RMx7oMkSZKk7vO45HvsBblyl133jf5j/fR57XydNSnW4rbzkiRJkiSte/bc0iQzISRJkiRJAhZPgGzeOOJA1lC/Mk5S+aRB3WXcAUiSJEmSJGm07CEkSZIkSROsy2PeeEmWND4mhCRJkiRJE8dkk7Q0LxmTJEmSJEmaMvYQ0lh1ufuqJEmSpO7rSk+grsQhDcoeQpIkSZIkSVPGHkIS9lSSJEmSJE0XewhJkiRJkiRNGXsISZIkSZI0Ql6hoC6wh5AkSZIkSdKUsYeQpooj/0uSJEnqKnsOaZRMCKmT+lWEVoLS5ElyCPBW4AeA7wJbqup1SfYD3glsALYCz6qqr40rTkmSJGnSmBAagg0nvp/NG3dyrL1POqUrvYFMbklL2glsrqpPJNkHuCTJBcCxwIVVdUqSE4ETgRPGGKckSZI0UVY8hlCSQ5J8OMmVSa5I8tvt/FcmuS7Jpe3fk4YXriRpklTV9qr6RPv4FuBK4CDgSOCMdrUzgKePJ0JJUtd5XCJJK7OaHkKLndUFeG1VvWb14UmSpkWSDcCPAR8FZqpqOzRJoyT3W+Q5xwPHA8zMzDA3NzeSWHvt2LFj0f1u3rhztMH0mNlrvPtfShdjm38Pl3o/x6mrcUF3Y+tqXNDt2NYpj0skaQVWnBBqf6jP/1i/Jcn8WV1pTXTlEjBJw5fk+4F3Ab9TVd9MMtDzqmoLsAVg06ZNNTs7u2YxLmZubo7F9jvOS4k3b9zJqZd188rwLsa29ehZYOn3c5y6Ghd0N7auxgXdjm098rhEklZmKL/GFpzVfQzwoiTPBz5Ok63fZSDQLpzVHZbNG3d28mznSliOOxvG57JfHINud1LOIFoOLSXJ99Ekg86sqne3s29IckDbO+gA4MbxRShJWi/GdVyyXn9Dv/7Mc3eZt3lj/3XXaxkH1fXy9XuvNh50r2VtY9J/y1q+5Vt1QqjPWd03AX8MVPv/VOBXFz6vC2d1h+XYdlDprp3tXAnLcWfzZ4xXo18PgUG3OylnEC2HFpOmK9BbgCur6s97Fp0HHAOc0v7f9VeQNALeGEBaP8Z5XPL6M8+diN/QS5mU44TFrMfyLfdYZdJ/y1q+5VvVJ77fWd2quqFn+d8A71tVhJKkSfYY4HnAZUkubee9jCYRdHaS44BrgGeOKb6BeEmrJI2XxyWStHwrTggtdlZ3vot/O/lLwOWrC1GSNKmq6l+BxQYMOnyUsWi6zSf1Nm/cOdaxnyQtn8clkrQyq+khtNhZ3WcneSRN18ytwK+vKkJJkjpkYW+gZryB9dXFXGtjsZ5iXmImrTmPSyRpBVZzl7HFzup+YOXhSJIkSdLgPC6RpJXxlKbUIf16HsyOJxRJ0iIcM0qSJE0CE0LSEry7jCRJkqQuWu4JitOP2HukcXjc1H0mhCRJkiRJ0h3sDTsdTAgtwiynus7PqCRJkiRppe4y7gAkSZIkSZI0WvYQkiRJWkPDGI/usuu+wbELtmOPUEmStBomhJbJaynlZ0CSJEmStN6ZEJIkSZIkacL1622q6TZVCSF7dkiSpC7wxgCSJGncHFRakiRJkiRpykxVDyFpGgxj8FJJkiRJ0mSbyISQl4ZJd7bcSxNMKkmSJEnSZJvIhJDUdSYtJUmSJEnjZEJI6jiTR5I0PRar8zdvHHxde3RKktYb27TxMCEkaerY4KiXnwdJkqTR8GR3t6xZQijJEcDrgD2AN1fVKWu1L0krM4wK2YNpraVxtiX+YNE0WE4dvti6px+x91Bj2t0+bV+0XB6XSOuX7cDaWpOEUJI9gL8CHg9sA/4jyXlV9Zlh7scf65Lm2VhMnlG1JZKkyWVbIk2ecZygmFRr1UPoMOCqqvoiQJJ3AEcCVrzSOrXcBGzv+ps37uTYJZ7flcSNSaXOsS2RVqArJ8ymtU6d1nJ3mG2JNCZdaY/Wo36v3VokvFJVw99o8gzgiKr6tXb6ecBPVNWLetY5Hji+nXwI8LmhBzJa+wNfHXcQQ2A5usVydMtyyvGAqrrvWgYz6dZRW9LVz3dX4wJjW4muxgXdja2rccHgsdmWrNII25Iuf96GZdLLOOnlg8kvo+Xrb9G2ZK16CKXPvDtlnqpqC7BljfY/ckk+XlWbxh3HalmObrEc3TIp5VhH1kVb0tXPRVfjAmNbia7GBd2NratxQbdjm0AjaUum4T2d9DJOevlg8sto+ZbvLsPcWI9twCE90wcD16/RviRJk8m2RJK0WrYlkrSItUoI/QdwaJIHJrkrcBRw3hrtS5I0mWxLJEmrZVsiSYtYk0vGqmpnkhcB/4/m9o6nVdUVa7GvDpmUy98sR7dYjm6ZlHKsC+uoLenq56KrcYGxrURX44LuxtbVuKDbsU2UEbYl0/CeTnoZJ718MPlltHzLtCaDSkuSJEmSJKm71uqSMUmSJEmSJHWUCSFJkiRJkqQpY0JoCJLsm+ScJJ9NcmWSnxp3TCuR5HeTXJHk8iRnJbn7uGMaRJLTktyY5PKeefsluSDJ59v/9x5njINYpByvbj9Xn07yniT7jjPGQfQrR8+ylySpJPuPI7ZBLVaGJL+V5HPt9+RV44pPo5fkiPa9vyrJiX2WH91+Tz+d5N+TPKIrsfWs9+NJbk/yjC7FlmQ2yaXt9+qfuxBXknsl+Yckn2rj+pURxbVo/dkuT5K/bOP+dJJHjSKuAWMb53dgydh61hvpd2CQuMbx+dfqDFB/jO17Ogxdbu+Gpcvt5jB0te0dlq624cMy8t8CVeXfKv+AM4Bfax/fFdh33DGtoAwHAVcDe7XTZwPHjjuuAWP/OeBRwOU9814FnNg+PhH4P+OOc4Xl+EVgz/bx/1mv5WjnH0IzoOOXgP3HHecK3ovHAv8E3K2dvt+44/RvZJ+HPYAvAA9q6/hPAQ9dsM5PA/duHz8R+GhXYutZ70PAB4BndCU2YF/gM8D92+k1/14NGNfL5utb4L7AzcBdRxBb3/qzZ/mTgH8EAvzkqD5nA8Y2lu/AILH1vO+j/g7s7jUb+effv1W/p4PUH2P7no6ofGP7ro+qjD3rjbTOGOF7uG7rni634UMs40h/C9hDaJWS3JPmTXsLQFV9u6q+Pt6oVmxPYK8kewL3AK4fczwDqaqLaL7ovY6kSdTR/n/6SINagX7lqKrzq2pnO3kxcPDIA1umRd4PgNcCvwd0fiT7RcrwG8ApVXVbu86NIw9M43IYcFVVfbGqvg28g6aOuUNV/XtVfa2dHOV3dbextX4LeBcwys/tILE9B3h3VV0DI/teDRJXAfskCfD9NPXBTtbYEvXnvCOBt1bjYmDfJAesdVyDxDbG78AgrxuM4TswQFzj+PxrdQapP8b2PR2CLrd3w9LldnMYutr2Dktn2/BhGfVvARNCq/cg4CvA3yb5ZJI3J9l73EEtV1VdB7wGuAbYDnyjqs4fb1SrMlNV2wHa//cbczzD8Ks02eB1J8nTgOuq6lPjjmUVHgz8bJKPJvnnJD8+7oA0MgcB1/ZMb2vnLeY4Rvdd3W1sSQ4Cfgn46xHFNG+Q1+3BwL2TzCW5JMnzOxLXG4AfoTkxchnw21X13RHEtjvL/SyOyyi/A7s1xu/A7ozj86/VGeQ7uF6+p/10ub0bli63m8PQ1bZ3WNZzGz4sQ61jTAit3p40XbreVFU/BtxKc4nSupJmjJ0jgQcCBwJ7J3nueKPSvCQvp8lsnznuWJYryT2AlwP/e9yxrNKewL1puma+FDi7PfOgydfvfe7b0y3JY2l+IJ+wphH17LLPvIWx/QVwQlXdPoJ4eg0S257Ao4EnA08A/iDJgzsQ1xOAS2naw0cCb2h7BI/bwJ/FcRnDd2AQ4/oO7M44Pv9anUG+g53/ni6hy+3dsHS53RyGrra9w7Ke2/BhGWods+cqAlFjG7Ctqj7aTp/DOkwIAY8Drq6qrwAkeTfNNcJvG2tUK3dDkgOqanvbhW49dYW8kyTHAE8BDq/2wtF15gdpEo2favMnBwOfSHJYVX15rJEtzzaa7rUFfCzJd4H9aXoIarJtoxkDa97B9LmkNsmPAm8GnlhVN3Uotk3AO9rv3/7Ak5LsrKr3diC2bcBXq+pW4NYkFwGPAP5zzHH9Cs0logVcleRq4IeBj61hXIMY6LM4LmP6DgxiXN+B3RnH51+rM2i91tnv6W50ub0bli63m8PQ1bZ3WNZzGz4sQ61j7CG0Su0B7bVJHtLOOpxmkK715hrgJ5Pco+31cDhw5ZhjWo3zgGPax8cA544xlhVLcgTNmZenVdW3xh3PSlTVZVV1v6raUFUbaCqxR62zZBDAe4FfAGjPotwV+OpYI9Ko/AdwaJIHJrkrcBRNHXOHJPcH3g08r6pG+YNqt7FV1QN7vn/nAL85oh+1u42Npm7+2SR7tr0Jf4K1b3sGiesamnaQJDPAQ4AvrnFcgzgPeH57h5GfpLm8e/u4g4Kxfgd2a4zfgd0Zx+dfqzNI/dHZ7+kAutzeDUuX281h6GrbOyzruQ0flqHWMfYQGo7fAs5sP5RfpMlKritV9dEk5wCfoLk06ZPAlvFGNZgkZwGzwP5JtgGvAE6huaTnOJpK4Znji3Awi5TjJOBuwAXtWYqLq+oFYwtyAP3KUVVvGW9Uy7PIe3EacFqaW0B+GzhmnfbY0jJV1c4kL6K5S94ewGlVdUWSF7TL/5rmksj7AG9sv6s7q2pTR2Ibi0Fiq6ork3wQ+DTwXeDNVbXkrcNHERfwx8DpSS6j6Zp9QlWteQJ4kbrn+3ri+gDN3UWuAr7FCH9vDBDbWL4DA8Y2FruLaxyff63OgPXH2L6nq9Xl9m5YutxuDkNX295h6XIbPiyj/i0Qj2ckSZIkSZKmi5eMSZIkSZIkTRkTQpIkSZIkSVPGhJAkSZIkSdKUMSEkSZIkSZI0ZUwISZIkSZIkTRkTQpIkSZIkSVPGhJAkSZIkSdKUMSEkSZIkSZI0ZUwISZIkSZIkTRkTQpIkSZIkSVPGhJAkSZIkSdKUMSEkSZIkSZI0ZUwISZIkSZIkTRkTQpIkSZIkSVPGhJAkSZIkSdKUMSEkSZIkSZI0ZUwISZIkSZIkTRkTQpIkSZIkSVPGhJA6JcnRSc5fYvlckl8bwn5mk2xb7XYkSd2X5Ngk/7rE8hW3LUnun2RHkj0WWf7KJG9bybYlSY0kD0nyySS3JHnxuOPpZ+HxxShiTrI1yePaxy9L8uZVbs82bcrsOe4ApF5VdSZw5rjjkCRpEFV1DfD9445Dkibc7wFzVfVj4w5kGUYac1X96RC2YZs2ZewhpKFLYqJxlXwNJan7rKslaWQeAFyx2MLFerSM2ZIxL8X2RaNiQkhD0XZXPCHJp4Fbk+yZ5CeT/HuSryf5VJLZnvWPTfLFtgvl1UmO7pn/rz3rPT7JZ5N8I8kbgPQsu1OXxSQbktR8BZrkV5Jc2e7ji0l+fcCyJMlrk9zY7vfTSR7eLrvTZQV94v3FJJ9rn/fGJP88v36SH0zyoSQ3JflqkjOT7LvUazj4OyBJAkhySJJ3J/lKW9++oWfZa5J8rW13nrjI8++S5PeTfKltB96a5F7tsvl25rgk1wAf6tP2PLCt+29JcgGw/4LtL7ttlKRpluRDwGOBN7SXMz04yelJ3pTkA0luBR6b5MAk72rr/6t7L9Nq6/YTk3yhbRvOTrLfIvvbP8n72nr65iT/kuQu7bJK8kM9656e5E8GjHl3xxGV5IVJPg98fpHYnte2TzclefmCZQuPjZ6W5Iq2HHNJfqSdf0KSi3vard9o17v7MNs0rQ8mhDRMzwaeDOwLzADvB/4E2A94CfCuJPdNsjfwl8ATq2of4KeBSxduLMn+wLuA36epfL4APGYZ8dwIPAW4J/ArwGuTPGqA5/0i8HPAg9uy/DJw0+6e1MZ7DnAScB/gczRlu2MV4M+AA4EfAQ4BXrlgM3e8hlW1c4BYJUmtNGeI3wd8CdgAHAS8o138EzT18v7Aq4C3JEmfzRzb/j0WeBBN1/k3LFjn52nq8Sf0ef7bgUva/fwxcExPfAexyrZRkqZNVf0C8C/Ai6rq+6vqP9tFzwFOBvYB/h34B+BTNHX/4cDvJJmvp18MPJ2m/j4Q+BrwV4vscjOwDbgvzTHNy4AaUsy783Sa9uqhCxckeSjwJuB5bRnuAxzcbyNJHgycBfxOW44PAP+Q5K7Aq4FvA7+f5FDgT4HnVtV/99nUitq0AcuqDjAhpGH6y6q6tqr+C3gu8IGq+kBVfbeqLgA+DjypXfe7wMOT7FVV26uqX3fKJwGfqapzquo7wF8AXx40mKp6f1V9oRr/DJwP/OwAT/0OTcPyw0Cq6sqq2j7A854EXFFV726TOX/ZG29VXVVVF1TVbVX1FeDPaRqlXr2voSRpeQ6j+ZH80qq6tar+u6rmz75+qar+pqpuB84ADqD5ob/Q0cCfV9UXq2oHTZL/qNy51+Yr2+3fqa5Ocn/gx4E/aOv6i2gOUOYNo22UJDXOrap/q6rvAhuB+1bVH1XVt6vqi8DfAEe16/468PKq2lZVt9GclH1G+vfI/w5NG/GAqvpOVf1LVS0rIbQKf1ZVNy9yLPAM4H1VdVFbhj+gaTf6+WXg/e2xx3eA1wB7AT/dvl7Pp0mSnQe8qqo+uXADQ2jTtA6YENIwXdvz+AHAM9vug19P8nXgZ4ADqupWmkrqBcD2JO9P8sN9tndg7zbbivjaPuv1leSJbXfIm9v9P4kF3Rz7qaoP0ZwN/ivghiRbktxzgF32i7f3TgP3S/KOJNcl+Sbwtj7xDFw+SdIuDqFJ/PTrYdmboP9W+7DfwJkH0vQwmvclmptw9CaPFqurDwS+1rZzvc+fN4y2UZLUWHjsceCC+vVlfK/ufgDwnp5lVwK30//EwKuBq4Dz28t4T1y7IuxiqWOBhccat7L4VQx3asvaJNC1NL2nqKqtwIdpetMu1lNqxW3aEmVQx5gQ0jD1Zs6vBf6uqvbt+du7qk4BqKr/V1WPp6kwPkuTwV9oO82Pe6AZ26d3GrgVuEfP9A/0rHs3msvNXgPMVNW+NF0l+10esGtBqv6yqh4NPIzm0rGX7m6fbbx3dNts4+3txvlnNK/Rj1bVPWmy6gvjGdXZB0maRNcC91/kjO+grqf5kTvv/sBO4IaeeYvV1duBe7eXf/U+vze+1baNkqTGwmOPqxfUr/tU1ZN6lj9xwfK7V9V1u2y06paq2lxVDwKeCvyvJIe3i7/F4scCu7PUcUS/Mi208NjoHjSXjfVzp7as5zjqunb6ScBPARfSJMAW29+K2zStDyaEtFbeBjw1yROS7NEOUjab5OAkM+0gZ3sDtwE7aDL0C70feFiS/9H+uH8xd644LwV+Lsn90wz4eVLPsrsCdwO+AuxMM3joLw4SeJIfT/ITSb6PpuL+7574LgX+R5J7pBlQ7rgF8W5M8vQ23hcuiHeftqxfb6+5fSmSpGH6GM0P2FOS7N22PcsZew6aMRd+tx1I8/tpxlZ45yDjulXVl2i6y/9hkrsm+Rmag4l5w2gbJUm7+hjwzXbA5L3aOvbhSX68Xf7XwMlJHgDQjt12ZL8NJXlKkh9qkyjfpKmLe48FntNu/wh2Hf5hKUsdRwziHOApSX6mHQvoj1j8eP5s4MlJDm+PaTbTtC3/3o57+hbg12jGBHpqmyC6k9W0acssl8bIhJDWRFVdCxxJ01XzKzQZ5JfSfObuQlMpXQ/cTFOR/mafbXwVeCZwCk13yEOBf+tZfgHwTuDTNIOdva9n2S00CaSzaQaNew7NNbKDuCfNWdmv0XSLvImmpxHAa2kGYbuBZgyKM/vE+6r2OQ+lqURva1f5Q+BRwDdokkfvHjAeSdIA2vGBngr8EHANzWW7v7zMzZwG/B1wEXA1zUmB31rG859DMyDozcArgLf2xLfqtlGStKue+v+RNHX3V4E3A/dqV3kdzbHA+UluAS6mqav7ORT4J5rE/EeAN1bVXLvst9v9fJ1mzLn3LiPMRY8jBtGOK/dCmoGet9Mcq2xbZN3P0VyN8Hqa1+KpwFOr6tvAFprxlz5QVTfRJKbenKRfb6OVtmlaJzK68bGk6ZLm9pTbgKOr6sPjjkeSJEmSpHlm76QhartM7tuOYfQymjGCLh5zWJIkSZIk3YkJIWm4fgr4At/rmvl0byEvSZIkSeoaLxmTJEmSJEmaMvYQkiRJkiRJmjJ7jjsAgP333782bNgw7jDucOutt7L33nuPO4xddDUu6G5sXY0LjG0luhoX7BrbJZdc8tWquu8YQ5o6g7QlXf4M7Y6xj4exj4exN2xLRm/Q45Kuf0aNb3W6HF+XYwPjW421im3JtqSqxv736Ec/urrkwx/+8LhD6KurcVV1N7auxlVlbCvR1biqdo0N+Hh1oH6dpr9B2pIuf4Z2x9jHw9jHw9gbtiXdbEuquv8ZNb7V6XJ8XY6tyvhWY61iW6ot8ZIxSZIkSZKkKWNCSJIkSZIkacqYEJIkSZIkSZoyJoQkSZIkSZKmzMAJoSR7JPlkkve10/sluSDJ59v/9+5Z96QkVyX5XJInrEXgkiRJkiRJWpnl9BD6beDKnukTgQur6lDgwnaaJA8FjgIeBhwBvDHJHsMJV5IkSZIkSau15yArJTkYeDJwMvC/2tlHArPt4zOAOeCEdv47quo24OokVwGHAR8ZWtRDtOHE9+8yb/PGnXcUTJKkYevX9mw95cljiESSNA62A5K6YKCEEPAXwO8B+/TMm6mq7QBVtT3J/dr5BwEX96y3rZ13J0mOB44HmJmZYW5ubnmRD8nmjTt3mTezF2OLZyk7duzoZFzQ3di6GhcY20p0NS7odmySJEmStNBuE0JJngLcWFWXJJkdYJvpM692mVG1BdgCsGnTppqdHWTTw3fsIj2EnjWmeJYyNzfHuF6n3elqbF2NC4xtJboaF3Q7NkmSJkGS04D5Y5OHL1j2EuDVwH2r6qvtvJOA44DbgRdX1f8bcciS1GmDjCH0GOBpSbYC7wB+IcnbgBuSHADQ/r+xXX8bcEjP8w8Grh9axJIkSZKm0ek0Y5TeSZJDgMcD1/TMc1xTSdqN3SaEquqkqjq4qjbQVKofqqrnAucBx7SrHQOc2z4+Dzgqyd2SPBA4FPjY0COXJEmSNDWq6iLg5j6LXkszvEXvVQl3jGtaVVcD8+OaSpJag44h1M8pwNlJjqPJxj8ToKquSHI28BlgJ/DCqrp91ZFKkiRJUo8kTwOuq6pPJXcauWKgcU3bbSx7bNPVjh3YbxzTYY5F2PWxDY1v5bocGxjfaowjtmUlhKpqjuZuYlTVTcDhi6x3Ms0dySRJkiRp6JLcA3g58Iv9FveZt8u4prCysU1XO3Zgv3FMuezWvuuu5O5jXR/b0PhWrsuxgfGtxjhiW00PIUmSJEkalx8EHgjM9w46GPhEksNwXFNJ2q1BBpWWJGnVkmxNclmSS5N8vJ23X5ILkny+/X/vnvVPSnJVks8lecL4IpckdVFVXVZV96uqDe14p9uAR1XVl3FcU0naLRNCkqRRemxVPbKqNrXTJwIXVtWhwIXttHeHkSTtIslZwEeAhyTZ1o5l2ldVXQHMj2v6QRzXVJJ24SVjkqRxOhKYbR+fQTNO3Qn03B0GuDrJ/N1hPjKGGCVJHfD/2bv7cMvOur7/748JhCSgkMYchiQ6UYM1YXhyTKloe0qgpIYytD+hoYFOSnRKf1HEDoWJtEV/Nu34ANWqtJ0CZtSQMCCYKfEhMXqackkSIAbyRMxIxjBkyPAoDPqLTPz2j73G7JzZ+5x9HvZZa5/9fl3Xuc7e977XXp+9zjl7nf1da913Vb1ykcc3zrvvuKaStAALQpKktVLA9UkK+B/NIJ4zVXUQoKoOJjmt6TvS7DBLnRmmKzNLLGd2ma5kXw6zt8Ps7Zjk7JKk6WJBaIiNg0b+Z3mj/EuSAHh+VT3YFH1uSPLJBfqONDvMUmeG6crMEoNml9l/8eyCy3Ql+3KYvR1mb8ckZ9f65WcbSYM4hpAkaU1U1YPN90PAB+hdAvZQkg0AzfdDTXdnh5EkSZLGyIKQJGnskpyc5ElHbwP/ELiT3iwwW5tuW4Frm9vODiNJkiSNkZeMSZLWwgzwgSTQ2/e8u6p+N8lHgD3NTDEPAC+H3uwwSY7ODnMEZ4eRJEmSVpUFIUnS2FXVp4BnDWj/AnD+kGWcHUaSJEkaEy8ZkyRJkiRJmjKeISRJkiRJApyRTJomFoQkSZIkqaMs0EgaFy8ZkyRJkiRJmjIWhCRJkiRJkqaMBSFJkiRJkqQpY0FIkiRJkiRpylgQkiRJktR5Sd6V5FCSO/vafi7JJ5N8IskHkjy577HLk+xLcm+SF7eTWpK6y4KQJEmSpElwJXDBvLYbgGdU1TOBPwEuB0hyDnARcG6zzNuTHLd2USWp+ywISZIkSeq8qroJ+OK8tuur6khz92bgjOb2FuCaqnq4qu4H9gHnrVlYSZoAx7cdQJIkSZJWwWuA9zS3T6dXIDrqQNN2jCTbgG0AMzMzzM3NLbqiw4cPj9RvmO2bjizeaRELrX9+vmHrG/QcS+m7XCvdfuPW5XxdzgbmW4k2slkQkiRJkjTRkrwZOAJcdbRpQLcatGxV7QJ2AWzevLlmZ2cXXd/c3Byj9Bvmkh3XLXvZo/ZfPHz98/MNW9+g51hK3+Va6fYbty7n63I2MN9KtJHNgpAkSZKkiZVkK/AS4PyqOlr0OQCc2dftDODBtc4mSV3mGEKSJEmSJlKSC4A3AS+tqr/oe2gvcFGSE5KcBZwN3NpGRknqKs8QkiRJktR5Sa4GZoFTkxwA3kJvVrETgBuSANxcVa+tqruS7AHupncp2WVV9Ug7ySWpmywISZIkSeq8qnrlgOZ3LtD/CuCK8SWSpMnmJWOSJEmSJElTxoKQJEmSJEnSlLEgJEmSJEmSTxovPAAAIABJREFUNGUsCEmSJEmSJE2ZRQtCSZ6Q5NYkH09yV5KfatpPSXJDkvua70/pW+byJPuS3JvkxeN8AZKkyZHkuCR/nOSDzX33JZIkSVILRjlD6GHgBVX1LODZwAVJngfsAG6sqrOBG5v7JDkHuAg4F7gAeHuS48YRXpI0cX4MuKfvvvsSSZIkqQWLFoSq53Bz93HNVwFbgN1N+27gZc3tLcA1VfVwVd0P7APOW9XUkqSJk+QM4ELgHX3N7kskSZKkFhw/SqfmqOzHgO8AfqWqbkkyU1UHAarqYJLTmu6nAzf3LX6gaZv/nNuAbQAzMzPMzc0t+0WsxPZNR45pmzlxcDvQWk6Aw4cPt7r+hXQ1W1dzgdmWo6u5oNvZOuQXgDcCT+prW9N9SVd+ToP2MZOSfTnM3g6zt2OSs0uSpstIBaGqegR4dpInAx9I8owFumfQUwx4zl3ALoDNmzfX7OzsKFFW3SU7rjumbfumI7z1jsGbZv/Fs2NONNzc3BxtbafFdDVbV3OB2Zajq7mg29m6IMlLgENV9bEks6MsMqBtxfuSrvycBu17Ftu/dCX7cpi9HWZvxyRnlyRNl5EKQkdV1ZeTzNEbz+GhJBuaI7obgENNtwPAmX2LnQE8uBphJUkT6/nAS5P8APAE4BuT/AbuSyRJmlgbBxzgANi/88I1TiJpOUaZZeybmzODSHIi8ELgk8BeYGvTbStwbXN7L3BRkhOSnAWcDdy62sElSZOjqi6vqjOqaiO9waL/oKpehfsSSZIkqRWjnCG0AdjdjCP0DcCeqvpgkg8De5JcCjwAvBygqu5Ksge4GzgCXNZcciZJ0nw7cV8iSZIkrblFC0JV9QngOQPavwCcP2SZK4ArVpxOkrTuVNUcMNfcdl8iSZIktWDRS8YkSZIkqW1J3pXkUJI7+9pOSXJDkvua70/pe+zyJPuS3Jvkxe2klqTusiAkSZIkaRJcSW9ym347gBur6mzgxuY+Sc6hN2bduc0yb2+GwJAkNSwISZIkSeq8qroJ+OK85i3A7ub2buBlfe3XVNXDVXU/sA84b02CStKEWNK085IkSZLUITNVdRCgqg4mOa1pPx24ua/fgabtGEm2AdsAZmZmmJubW3Slhw8fHqnfMNs3HVn2skcttP75+Yatb9BzjKvvQvm6psv5upwNzLcSbWSzICRJkiRpvcmAthrUsap2AbsANm/eXLOzs4s++dzcHKP0G+aSHdcte9mj9l88fP3z8w1b36DnGFffhfJ1TZfzdTkbmG8l2sjmJWOSJEmSJtVDSTYANN8PNe0HgDP7+p0BPLjG2SSp0ywISZIkSZpUe4Gtze2twLV97RclOSHJWcDZwK0t5JOkzvKSMUmSJEmdl+RqYBY4NckB4C3ATmBPkkuBB4CXA1TVXUn2AHcDR4DLquqRVoJLUkdZEJIkSZLUeVX1yiEPnT+k/xXAFeNLJEmTzUvGJEmSJEmSpowFIUmSJEmSpCljQUiSJEmSJGnKWBCSJEmSJEmaMhaEJEmSJEmSpoyzjC3Rxh3XHdO2f+eFLSSRJEmSJElaHs8QkiRJkiRJmjIWhCRJkiRJkqaMBSFJkiRJkqQpY0FIkiRJkiRpylgQkiRJkiRJmjLOMiZJUgcMmsUSnMlSkiRJ42FBSJI0dkmeANwEnEBv3/O+qnpLklOA9wAbgf3AK6rqS80ylwOXAo8Ar6uq32sh+ooMK/JIklZXkh8Hfggo4A7gXwInMWQfI0nykjFJ0tp4GHhBVT0LeDZwQZLnATuAG6vqbODG5j5JzgEuAs4FLgDenuS4VpJLkjotyenA64DNVfUM4Dh6+5CB+xhJUo8FIUnS2FXP4ebu45qvArYAu5v23cDLmttbgGuq6uGquh/YB5y3hpElSZPleODEJMfTOzPoQYbvYyRJeMmYJGmNNGf4fAz4DuBXquqWJDNVdRCgqg4mOa3pfjpwc9/iB5q2+c+5DdgGMDMzw9zc3IIZDh8+vGif1bR905EVP8fRvGudfTWZvR1mb8ckZ59UVfWZJD8PPAD8JXB9VV2/wD5GkoQFIUnSGqmqR4BnJ3ky8IEkz1igewY9xYDn3AXsAti8eXPNzs4umGFubo7F+qymS1ZhDKH9F88Ca599NZm9HWZvxyRnn1RJnkLvbKCzgC8D703yqiUsv6SDC7Dywt9qHjAYZH6+Yesb9Bzj6rtQvq7pcr4uZwPzrUQb2SwISZLWVFV9OckcvbGBHkqyoTlyuwE41HQ7AJzZt9gZ9E7/lyRpvhcC91fV5wCSvB/4XobvYx5jqQcXYOWFv9U8YNDv6GQG2zc9wls/9LW+RwZ/7Bv0HMOyrbRvv64XTrucr8vZwHwr0UY2xxCSJI1dkm9uzgwiyYn0/nn/JLAX2Np02wpc29zeC1yU5IQkZwFnA7eubWpJ0oR4AHhekpOSBDgfuIfh+xhJEp4hJElaGxuA3c04Qt8A7KmqDyb5MLAnyaX0/qF/OUBV3ZVkD3A3cAS4rLnkTJKkx2jGpHsfcBu9fcYf0zvj54kM2MdIknosCEmSxq6qPgE8Z0D7F+gdyR20zBXAFWOOJklaB6rqLcBb5jU/zJB9jCRphEvGkpyZ5A+T3JPkriQ/1rSfkuSGJPc135/St8zlSfYluTfJi8f5AiRJkiRJkrQ0o4whdATYXlXfBTwPuCzJOcAO4MaqOhu4sblP89hFwLn0Bgx9e3OJgCRJkiRJkjpg0YJQVR2sqtua21+lN0Db6fSmdtzddNsNvKy5vQW4pqoerqr7gX3AeasdXJIkSZIkScuzpFnGkmykNwbELcBMVR2EXtEIOK3pdjrw6b7FDjRtkiRJkiRJ6oCRB5VO8kTgN4HXV9VXejM6Du46oK0GPN82YBvAzMwMc3Nzo0ZZVds3HTmmbebEwe3DrFX2w4cPt7adFtPVbF3NBWZbjq7mgm5nkyRJkqT5RioIJXkcvWLQVVX1/qb5oSQbqupgkg3Aoab9AHBm3+JnAA/Of86q2kVvOkg2b95cs7Ozy3sFK3TJjuuOadu+6QhvvWP0Cdj2Xzy7iomGm5ubo63ttJiuZutqLjDbcnQ1F3Q7myRJkiTNN8osYwHeCdxTVW/re2gvsLW5vRW4tq/9oiQnJDkLOBu4dfUiS5IkSZIkaSVGOQ3m+cCrgTuS3N60/QSwE9iT5FLgAeDlAFV1V5I9wN30Zii7rKoeWfXkkiRJkiRJWpZFC0JV9SEGjwsEcP6QZa4ArlhBLkmSJEmSJI3JkmYZkyRJkiRJ0uSzICRJkiRJkjRlLAhJkiRJkiRNGQtCkiRJkiRJU2aUWcYkSZIkqbOSPBl4B/AMoIDXAPcC7wE2AvuBV1TVl1qKqCE27rhuYPv+nReucRJp+lgQkiRJkjTpfhH43ar6wSSPB04CfgK4sap2JtkB7ADe1GbI1TSskCJJo7IgJEmSJGliJflG4O8BlwBU1V8Bf5VkCzDbdNsNzLGOCkJrzQKUtP5MVUHINzFJkiRp3fk24HPAryZ5FvAx4MeAmao6CFBVB5Oc1mJGSeqcqSoISZIkSVp3jgeeC/xoVd2S5BfpXR42kiTbgG0AMzMzzM3NLbrM4cOHR+o3zPZNR5a97ChmThxtHYNew2pkW2zb9G+/YetbyfZdqZX+fMepy9nAfCvRRjYLQpIkSZIm2QHgQFXd0tx/H72C0ENJNjRnB20ADg1auKp2AbsANm/eXLOzs4uucG5ujlH6DXPJmK9c2L7pCG+9Y/GPevsvnj2mbTWyDXrefv3bb9j6FnuOcVrpz3ecupwNzLcSbWSzICRJkiRpYlXVZ5N8Osl3VtW9wPnA3c3XVmBn8/3aFmNqlQwaBsQZyaTlsSAkSRq7JGcCvwY8FfhrYFdV/WKSUxgyJXCSy4FLgUeA11XV77UQXZI0GX4UuKqZYexTwL8EvgHYk+RS4AHg5S3m6yTHWJWmmwUhSdJaOAJsr6rbkjwJ+FiSG+jNCHPMlMBJzgEuAs4Fngb8fpKnV9UjLeWXJHVYVd0ObB7w0PlrnUWSJsU3tB1AkrT+VdXBqrqtuf1V4B7gdGALvamAab6/rLm9Bbimqh6uqvuBfcB5a5takiRJWr88Q0iStKaSbASeA9zC8CmBTwdu7lvsQNM2/7mWNDPMWs/esJoztXR5VozFmL0dZm/HJGeXJE0XC0KSpDWT5InAbwKvr6qvJBnadUBbHdOwxJlh1nr2htWcqaXLs2IsxuztMHs7Jjm7JGm6eMmYJGlNJHkcvWLQVVX1/qb5oWYqYOZNCXwAOLNv8TOAB9cqqyRJkrTeWRCSJI1deqcCvRO4p6re1vfQXnpTAcNjpwTeC1yU5IQkZwFnA7euVV5JkiRpvfOSMUnSWng+8GrgjiS3N20/AexkwJTAVXVXkj3A3fRmKLvMGcYkSZKk1WNBSJI0dlX1IQaPCwRDpgSuqiuAK8YWSpIkSZpiFoQkSZIkaQw2rsLkApI0Lo4hJEmSJEmSNGUsCEmSJEmSJE0ZLxmTJEmSJE2NQZfy7d95YQtJpHZ5hpAkSZIkSdKUsSAkSZIkSZI0ZSwISZIkSZp4SY5L8sdJPtjcPyXJDUnua74/pe2MktQljiEkSZIkaT34MeAe4Bub+zuAG6tqZ5Idzf03tRVOk8nxhrSeeYaQJEmSpImW5AzgQuAdfc1bgN3N7d3Ay9Y6lyR1mWcIrYJBVWOwcixJkiStkV8A3gg8qa9tpqoOAlTVwSSnDVowyTZgG8DMzAxzc3OLruzw4cMj9du+6ciifcZh5sT21g0sum36t9+wnMOeY1D/UX4WS3mOxfItdX2radTfvbaYb/nayGZBSJIkSdLESvIS4FBVfSzJ7FKXr6pdwC6AzZs31+zs4k8xNzfHKP0uGXLgeNy2bzrCW+9o76Pe/otnF3y8f/sN20bDnmNQ/8XWt9TnWCzfUte3mkb93WuL+ZavjWyLvkskeRdw9E32GU3bKcB7gI3AfuAVVfWl5rHLgUuBR4DXVdXvjSW5JEmSJMHzgZcm+QHgCcA3JvkN4KEkG5qzgzYAh1pNOUW8gkKaDKOMIXQlcMG8tqMDtJ0N3NjcJ8k5wEXAuc0yb09y3KqllSRJkqQ+VXV5VZ1RVRvpfRb5g6p6FbAX2Np02wpc21JESeqkRQtCVXUT8MV5zcMGaNsCXFNVD1fV/cA+4LxVyipJkiRJo9oJvCjJfcCLmvuSpMZyLywdNkDb6cDNff0ONG3HWM7gbSs16sBqqzUI22q/JgfAWrqu5gKzLUdXc0G3s0mSNC2qag6Ya25/ATi/zTyS1GWrPdJYBrTVoI7LGbxtpUYd1G21BmFb7cHGHABr6bqaC8y2HF3NBd3Opsl2dByG7ZuOPGY/5jgMkiRJWolRxhAa5KFmYDbmDdB2ADizr98ZwIPLjydJkiRJkqTVttzTYI4O0LaTxw7Qthd4d5K3AU8DzgZuXWlISZIkSZK6zNnVNGlGmXb+amAWODXJAeAt9ApBe5JcCjwAvBygqu5Ksge4GzgCXFZVj4wpuyRJkiRpHRpWXFlKXwsx0sIWLQhV1SuHPDRwgLaqugK4YiWhJEmaJEv5p1WSJEnqgtUeVFqSpGMkeRfwEuBQVT2jaTsFeA+wEdgPvKKqvtQ8djlwKfAI8Lqq+r0WYkuSpFU0bKIESe2wICRJWgtXAr8M/Fpf2w7gxqramWRHc/9NSc4BLgLOpTce3e8nebqXIEuSpKXwDF5pYcudZUySpJFV1U3AF+c1bwF2N7d3Ay/ra7+mqh6uqvuBfcB5axJUkiRJmhIWhCRJbZmpqoMAzffTmvbTgU/39TvQtEmSJElaJV4yJknqmgxoq4Edk23ANoCZmRnm5uYWfOLDhw8v2mc5tm86surPOd/MiY9dzzhex7iMa7uvBbO3w+ySJI2fBSFJUlseSrKhqg4m2QAcatoPAGf29TsDeHDQE1TVLmAXwObNm2t2dnbBFc7NzbFYn+VYi4Ext286wlvveHS3vf/i2bGvc7WMa7uvBbO3w+ySJI2fl4xJktqyF9ja3N4KXNvXflGSE5KcBZwN3NpCPkmSJGnd8gwhSdLYJbkamAVOTXIAeAuwE9iT5FLgAeDlAFV1V5I9wN3AEeAyZxiTJEmSVpcFIUnS2FXVK4c8dP6Q/lcAV4wvkSRpvUhyJvBrwFOBvwZ2VdUvJjkFeA+wEdgPvKKqvtRWTknqGi8ZkyRJkjTJjgDbq+q7gOcBlyU5B9gB3FhVZwM3NvclSQ0LQpIkSZImVlUdrKrbmttfBe4BTge2ALubbruBl7WTUJK6yUvGJEmSJK0LSTYCzwFuAWaq6iD0ikZJThuyzDZgG8DMzAxzc3OLrufw4cMj9du+6chowVfZzIntrXsUXczX//Ps//kOyjnsZz/sNY3yuzKqUX/32mK+5WsjmwUhSZIkSRMvyROB3wReX1VfSTLSclW1C9gFsHnz5pqdnV10mbm5OUbpd8mO60bKsNq2bzrCW+/o7ke9Lubbf/Hs39zu//kO+hn29+037Oc9rP9yjPq71xbzLV8b2bxkTJIkSdJES/I4esWgq6rq/U3zQ0k2NI9vAA61lU+SuqhbZdlVsrGlSvx8w3Ls33nhGieRJEmS1qf0TgV6J3BPVb2t76G9wFZgZ/P92hbiSVJnrcuCkCRJkqSp8Xzg1cAdSW5v2n6CXiFoT5JLgQeAl7eUT5I6yYKQJEkTaNBZqJ6BKmkaVdWHgGEDBp2/llk0ufr3q9s3HWlt/CdpLTmGkCRJkiRJ0pTxDCFJkiRJkjrAcWi1ljxDSJIkSZIkacpYEJIkSZIkSZoyXjImSZIkSdKIhl3WtZT+XgKmLrAg1ALfECRJkiRJK+F4Q1opLxmTJEmSJEmaMp4h1HH9Vd/tm45wyY7rrPhKUouWepq4JEmS1EUWhDrCDxiSJEmSpEGOfl48epLAJHColO6zIDSBvFZUkjSI+wdJkiSNyjGEJEmSJEmSpoxnCK0jHhmWJEmSpO5zyBB1gQWhKbXU4tFib1j917JagJIkSdI08cO9poEnIKw/YysIJbkA+EXgOOAdVbVzXOvS6lmNnZlvFJJWi/uStbeU/YDv65ImgfsSTRv35RrVWApCSY4DfgV4EXAA+EiSvVV19zjWp8kwrlHmLUBJ65P7ktXTlSPXzjbSHveVmlbuS6Tx69/HrPYsaOvlf4eu7ofHdYbQecC+qvoUQJJrgC3Aqr7xduUf3K6bxO20Gn/4G3dcN/Ib0lIulWv7j/aorr6prJZBr2/7piPMrvE618v2nFBrsi9Ru1ZjH9X1v9OlvLes9/d2qQXuS6QFrPVnxfV0Rco4c6zV55JU1eo/afKDwAVV9UPN/VcDf6eqfqSvzzZgW3P3O4F7Vz3I8p0KfL7tEAN0NRd0N1tXc4HZlqOrueDYbN9aVd/cVpj1YEz7ki7/Di3G7O0wezvM3uO+ZIXG+Lmk67+j5luZLufrcjYw30qMK9vQfcm4zhDKgLbHVJ6qahewa0zrX5EkH62qzW3nmK+ruaC72bqaC8y2HF3NBd3ONsFWfV8yyT8ns7fD7O0wu1bRWD6XdP3nbL6V6XK+LmcD861EG9m+YUzPewA4s+/+GcCDY1qXJGl9cl8iSVop9yWSNMS4CkIfAc5OclaSxwMXAXvHtC5J0vrkvkSStFLuSyRpiLFcMlZVR5L8CPB79KZ3fFdV3TWOdY1JJy9lo7u5oLvZupoLzLYcXc0F3c42kca0L5nkn5PZ22H2dphdq2KMn0u6/nM238p0OV+Xs4H5VmLNs41lUGlJkiRJkiR117guGZMkSZIkSVJHWRCSJEmSJEmaMhaEGknOTPKHSe5JcleSH2s703xJjkvyx0k+2HaWo5I8Ocn7knyy2XZ/t+1MRyX58eZneWeSq5M8ocUs70pyKMmdfW2nJLkhyX3N96d0JNfPNT/PTyT5QJInr3WuYdn6HntDkkpyapeyJfnRJPc2v3c/20Y2QZILmp/DviQ7BjyeJP+1efwTSZ7bRs5BRsj+t5N8OMnDSd7QRsZhRsh+cbO9P5Hkj5I8q42cw4yQf0uT/fYkH03yfW3kHGSx7H39vifJI0l+cC3zLWSE7T6b5M+b7X57kv/QRs5BRtnuTf7bm/3C/17rjBqPUf/m1jBPJ//PbHIM/IzVoXxPSHJrko83+X6qS/maLI/5DNixbPuT3HF039jBfMd8Vu1KviTf2bdvuz3JV5K8fs3zVZVfvXGUNgDPbW4/CfgT4Jy2c83L+G+AdwMfbDtLX6bdwA81tx8PPLntTE2W04H7gROb+3uAS1rM8/eA5wJ39rX9LLCjub0D+JmO5PqHwPHN7Z9pI9ewbE37mfQGhvwz4NSuZAP+AfD7wAnN/dPa+n2b5i96A4b+KfBtzXvSx+e/lwM/APwOEOB5wC1t515C9tOA7wGuAN7QduYlZv9e4CnN7X/Ule2+hPxP5NGxF58JfLLt3KNm7+v3B8BvAz/Ydu4lbPfZLv3fs8TsTwbuBr6lue9+YR18jfo3t8aZOvl/ZrPugZ+xOpQvwBOb248Dbmn+N+hEvmb9j/kM2LFs+5n3/3jH8h3zWbVL+fpyHgd8FvjWtc7nGUKNqjpYVbc1t78K3EOvqNAJSc4ALgTe0XaWo5J8I70d0DsBquqvqurL7aZ6jOOBE5McD5wEPNhWkKq6CfjivOYt9N6kaL6/bE1DMThXVV1fVUeauzcDZ6x1ribHoG0G8F+ANwKtjYg/JNu/BnZW1cNNn0NrHkwA5wH7qupTVfVXwDX0/tb6bQF+rXpuBp6cZMNaBx1g0exVdaiqPgJ8vY2ACxgl+x9V1Zeau629twwxSv7D1fx3BpxMi+9B84zyOw/wo8BvAl16bxo1exeNkv2fA++vqgfA/cI60rnf267+nwkLfsbqSr6qqsPN3cc1X0VH8g35DNiJbAvoRL4FPqt2It885wN/WlV/xhrnsyA0QJKNwHPoVYi74hfofQj+67aD9Pk24HPArzanMb4jyclthwKoqs8APw88ABwE/ryqrm831TFmquog9HaW9I78d81r6J1J0QlJXgp8pqo+3naWAZ4OfH+SW5L87yTf03agKXU68Om++wc4trg/Sp82dDXXKJaa/VI69N7CiPmT/JMknwSuo/f+2AWLZk9yOvBPgP++hrlGMervzd9tLuf4nSTnrk20RY2S/enAU5LMJflYkn+xZuk0TpPyXt25/zPnfcbqTL7mkqzb6RXMb6iqLuUb9BmwK9mgVzy7vnmP29a0dSXfsM+qXcnX7yLg6ub2muazIDRPkifSO4L2+qr6Stt5AJK8BDhUVR9rO8s8x9M7PfW/VdVzgK/RO62tdc21lluAs4CnAScneVW7qSZLkjcDR4Cr2s4CkOQk4M1AZ8aPmOd44Cn0TjP+t8CeJGk30lQatM3nn8kxSp82dDXXKEbOnuQf0CsIvWmsiZZmpPxV9YGq+tv0jtb99NhTjWaU7L8AvKmqHlmDPEsxSvbbgG+tqmcBvwT81thTjWaU7McD303v6P6LgX+f5OnjDqaxm+T36tZ08TPWUVX1SFU9m96Zq+cleUbbmaDTnwH7Pb+qnkvvUvDLkvy9tgP16exn1X5JHg+8FHhvG+u3INQnyePovVFdVVXvbztPn+cDL02yn95pqS9I8hvtRgJ6R0QONFV0gPfR+6PrghcC91fV56rq68D76Y1f0SUPHb1MpfnemVPJk2wFXgJc3HeJRNu+nV6B7+PN38IZwG1JntpqqkcdoHdpQFXVrfSO5LQy6PWUO0BvnKmjzuDYy0VH6dOGruYaxUjZkzyT3mnvW6rqC2uUbRRL2vbN5RnfnpYGtp9nlOybgWua984fBN6epAunyC+avaq+cvRyjqr6beBxE7TdDwC/W1Vfq6rPAzcBnRpMXcsyKe/Vnfk/c8hnrM7kO6q5nGgOuIBu5Bv2GbAL2QCoqgeb74eAD9C7pLIr+YZ9Vu1KvqP+EXBbVT3U3F/TfBaEGs2R/HcC91TV29rO06+qLq+qM6pqI73Tyf6gqlo/26WqPgt8Osl3Nk3n0xs8sQseAJ6X5KTmZ3s+vWuWu2QvsLW5vRW4tsUsfyPJBfSO3L+0qv6i7TxHVdUdVXVaVW1s/hYO0Buk8LMtRzvqt4AXADRHgB8PfL7VRNPpI8DZSc5qjrhcRO9vrd9e4F+k53n0Lik9uNZBBxgle1ctmj3Jt9Arzr+6qv6khYwLGSX/dxw96y+9mekeD3ShqLVo9qo6q++9833A/1tVXTjTZpTt/tS+7X4evf9dJ2K709uvf3+S45uzXP8O3ftfREs3Ke/Vnfg/c4HPWF3J981pZtRNciK9g8qf7EK+BT4Dtp4NIMnJSZ509Da9iWnu7Eq+BT6rdiJfn1fy6OVisNb5quURtbvyBXwfvdM9PwHc3nz9QNu5BuScpUOzbQDPBj7abLffoplBpgtfwE/Re0O/E/h1mtmfWspyNb2xjL5Or5BxKfC3gBuB+5rvp3Qk1z5618Yf/Tv4713ZZvMe3097s4wN2m6PB36j+X27DXhBW79v0/5FbxaxP6E3C8ybm7bXAq9tbgf4lebxO4DNbWdeQvanNr9zXwG+3Nz+xrZzj5j9HcCX+t5bPtp25iXmfxNwV5P9w8D3tZ151Ozz+l5JR2YZG3G7/0iz3T9ObzDy720781K2O71LiO9u9g2vbzuzX+P72becp5P/ZzbZBn7G6lC+ZwJ/3OS7E/gPTXsn8vXlnOXRWcY6kY3eGD0fb77u6nsf7ES+Jssxn1U7lu8kegc5vqmvbU3zHZ0+VZIkSZIkSVPCS8YkSZIkSZKmjAUhSZIkSZKkKWNBSJIkSZIkacpYEJIkSZIkSZoyFoQkSZIkSZKmjAUhSZIkSZKkKWNBSJIkSZIkacpYEJIkSZIkSZoyFoQkSZIkSZKmjAUhSZIkSZKkKWNBSJIkSZIkacpYEJIkSZIkSZodFPRkAAAgAElEQVQyFoQkSZIkSZKmjAUhSZIkSZKkKWNBSJIkSZIkacpYEJIkSZIkSZoyFoQkSZIkSZKmjAUhSZIkSZKkKWNBSGsuyZVJ/uMqP+clST7Ud//5Se5LcjjJy1ZzXX3rqCTf0dz+70n+/Qqf7/uT3LvA46u+3SRpGo37/TTJXUlmx/X8kiRJq8GCkI6RZH+SF46r/xr5/4BfrqonVtVvjXtlVfXaqvrpFT7H/6mq71ytTJKkdlTVuVU1B5DkJ5P8RsuRJEkdkmQ2yYEB7XNJfqiNTJpOFoS0Xn0rcNdyFkxy/CpnkSRJkiSpUywI6TGS/DrwLcD/ai63emPT/tLmFPgvN5Xr71qk/3uTfDbJnye5Kcm5I67/O5L872a5zyd5T9O+sblE6/i+vgMr6En+FPi2vkwnzD+Lqf+Ibd9zX5rkAeAPhmT7t0kOJnkwyWvmPfaYyw+S/HCSfUm+mGRvkqc17f8tyfv6+v1MkhvT85gjBUmek+S2JF9ttsMT5q3zJUlub34mf5TkmaNsY0maNgu9ny70XtrsO96Q5BPNfuk9SZ7QPHZqkg82y30xyf9J8g19y70wyQXATwD/rNkffTzJy5N8bF6+7UnGfjarJGltNfuDy5PcneRLSX716H5E6gILQnqMqno18ADwj5vLrX42ydOBq4HXA98M/Da9YsvjB/Vvnup3gLOB04DbgKtGjPDTwPXAU4AzgF9axmv49nmZHh5x0b8PfBfw4vkPNP/UvwF4Eb3XNfQSuSQvAP4z8ApgA/BnwDXNw9uBZ6Y35tH3A5cCW6uq5j3H44HfAn4dOAV4L/D/9D3+XOBdwL8C/hbwP4C9SU4Y8bVK0lRY6P10xPfSVwAXAGcBzwQuadq3Awfo7Rdn6BV+HvNeXlW/C/wn4D3N/uhZwF7grKMHVhqvavJJktafi+l9vvh24OnAv2s3jvQoC0IaxT8DrquqG6rq68DPAycC3ztsgap6V1V9tSnG/CTwrCTfNMK6vk7vcq+nVdX/X1UfWmyBVfSTVfW1qvrLAY+9AvjVqrqzqr5G7zUNczHwrqq6rXn9lwN/N8nGqvoLev/4vw34DeBHq+qY64eB5wGPA36hqr5eVe8DPtL3+A8D/6OqbqmqR6pqN/Bws5wk6VELvZ+O8l76X6vqwar6IvC/gGc37V+nV/T/1uZ5/8/84v4gzX7hPfT2BTRn0G4EPrjSFypJ6qRfrqpPN/uRK4BXNu1Pa84y/Zsv4Pvai6lpZEFIo3gavbNcAKiqvwY+DZw+qHOS45LsTPKnSb4C7G8eOnWEdb0RCHBrc4naaxZbYBV9eoHHnjbv8T8b1pFjt9dh4As026uqbgU+Re917lngOT4z78NF/zq/Fdg+bwdyZrOcJOlRC72fjvJe+tm+238BPLG5/XPAPuD6JJ9KsmMJmXYD/zxJgFcDe5ZwNqskabLM/wxxdB/zYFU9uf8LWMuD4ZIFIQ00/wjng/T+aQag+Qf2TOAzQ/r/c2ALvcuqvonekU/oFUAWXnHVZ6vqh6vqafRO4X97elO7f63pclJf96cu+koe9bURll3oyO5Beq/5qG9ZoO/87XUyvUsRPtPcvww4oen3xgXWd3qzrQet89PAFfN2IidV1dUL5JKkabTQ++my30ubs2C3V9W3Af8Y+DdJzh/UdcCyNwN/BXw/vX2ml4tJ0vo1/zPEg20FkeazIKRBHqI3KPNRe4ALk5yf5HH0xk14GPijIf2f1Dz+BXpFmP806oqbwTbPaO5+id4/0o9U1efoFVRe1ZyB9Bp61+GO6nbgoiSPS7IZ+MElLAu9bXBJknOSnAS8ZYG+7wb+ZZJnN+NQ/Cfglqra34zH9B/pXSrwauCNSZ494Dk+DBwBXpfk+CT/FDiv7/H/Cbw2yd9pBqQ+OcmFSZ60xNclSevdQu+ny34vbQaj/o6m0PQV4JHma76HgI1HB5zu82vALwNH1vjyaEnS2rosyRlJTqE33tx72g4kHWVBSIP8Z+DfNafPv6Gq7qVXwPgl4PP0joT+46r6q0H96f2T+2f0Cjh3AzcvYd3fA9yS5DC9gTd/rKrubx77YeDf0is0ncujBalR/Ht6BaQvAT9Fr2gzsqr6HeAX6M1Ato8hM5E1fW9s1veb9I5Mfzu9YtTx9MYN+pmq+nhV3Udvp/Dr8weDbrbtP6U3eOmX6I3j9P6+xz9Kb3v8cvP4Ph4d6FSS1Fjo/XSF76VnA78PHKZXdHp7Vc0N6Pfe5vsXktzW1/7rwDPw7CBJWu/eTW/SnE81X/9x4e7S2skI4x9KkiRpFSU5ETgEPLc5QCBJWmeS7Ad+qKp+v+0s0iCeISRJkrT2/jXwEYtBkiSpLce3HUCSJGmaNEeMA7ys5SiSJGmKecmYJEmSJEnSlPGSMUmSJEmSpCmz6CVjSc6kN2vUU4G/BnZV1S820+a9B9gI7AdeUVVfapa5HLiU3vSrr6uq31toHaeeempt3Lhx6ONf+9rXOPnkk0d4Od3m6+gWX0d3rIfXAI99HR/72Mc+X1Xf3HKkqbLYvmSQSfjdm4SMMBk5zbh6JiHnJGSEhXO6L1l7y9mXrMSk/J4eZd7xm7TMk5YXJi/zSvMuuC+pqgW/gA30ZsAAeBLwJ8A5wM8CO5r2HfSm0qZ57OPACcBZwJ8Cxy20ju/+7u+uhfzhH/7hgo9PCl9Ht/g6umM9vIaqx74O4KO1yPurX6v7tdi+ZJBJ+N2bhIxVk5HTjKtnEnJOQsaqhXO6L5mMfclKTMrv6VHmHb9JyzxpeasmL/NK8y60L1n0krGqOlhVtzW3vwrcA5wObAF2N9128+jAiFuAa6rq4aq6H9gHnDdi8UqSJEmSJEljtqRZxpJsBJ4D3ALMVNVB6BWNkpzWdDsduLlvsQNN2/zn2gZsA5iZmWFubm7oeg8fPrzg45PC19Etvo7uWA+vAdbP65AkSZK0/o1cEEryROA3gddX1VeSDO06oO2YqcyqahewC2Dz5s01Ozs7dN1zc3Ms9Pik8HV0i6+jO9bDa4D18zokSZIkrX8jzTKW5HH0ikFXVdX7m+aHkmxoHt8AHGraDwBn9i1+BvDg6sSVJE2iJO9KcijJnX1tP5fkk0k+keQDSZ7c99jlSfYluTfJi9tJLUmSJK1fixaE0jsV6J3APVX1tr6H9gJbm9tbgWv72i9KckKSs4CzgVtXL7IkaQJdCVwwr+0G4BlV9Ux6ExZcDpDkHOAi4NxmmbcnOW7tokqSumjQwYW+x96QpJKc2tfmwQVJWsAoZwg9H3g18IIktzdfPwDsBF6U5D7gRc19quouYA9wN/C7wGVV9chY0kuSJkJV3QR8cV7b9VV1pLl7M70zSsHJCSRJg13JsQcXSHImvc8jD/S1eXBBkhax6BhCVfUhBo8LBHD+kGWuAK5YQa41s3HHdQPb9++8cI2TSNJUew3wnub2SJMTwNImKBhkEgYCn4SMMBk5x53xjs/8+cD2Tad/08jPMQnbESYj5yRkhMnJ2QVVdVMzyc18/wV4I49esQB9BxeA+5McPbjw4XHn1PrnZ0itF0uaZUySpNWW5M3AEeCqo00Duh0zOQEsbYKCQSZhIPBJyAiTkXPcGS8Z9gHh4tHXOQnbESYj5yRkhMnJ2VVJXgp8pqo+Pm/SmzU7uLASk1YQNG/P9k1HBravxrrcxuM3aZnHmdeCkCSpNUm2Ai8Bzq+qo0UfJyeQJC0qyUnAm4F/OOjhAW1jObiwEpNWEDRvz2ocABjGbTx+k5Z5nHlHmmVMkqTVluQC4E3AS6vqL/oecnICSdIovh04C/h4kv30DiDcluSpeHBBkhblGUKSpLFLcjUwC5ya5ADwFnqzip0A3NCc5n9zVb22qu5KcnRygiM4OYEkaYCqugM47ej9pii0uao+n2Qv8O4kbwOehgcXJOkYFoQkSWNXVa8c0PzOBfpPzOQEkqS1MejgQlUN3Jd4cEGSFmdBSJIkSVLnDTm40P/4xnn3PbggSQtwDCFJkiRJkqQpY0FIkiRJkiRpynjJmCRJmhobh00VvPPCNU4iSZpmw/ZHV15w8hon0TSzICRJkiRJ0pgMK/5IbfOSMUmSJEmSpCnjGUKSJGnqeSmZJEmaNuuyIOQ/dZIkSZIkScN5yZgkSZIkSdKUsSAkSZIkSZI0ZSwISZIkSZIkTRkLQpIkSZIkSVPGgpAkSZIkSdKUsSAkSZIkSZI0ZSwISZIkSeq8JO9KcijJnX1tP5fkk0k+keQDSZ7c99jlSfYluTfJi9tJLUndZUFIkiRJ0iS4ErhgXtsNwDOq6pnAnwCXAyQ5B7gIOLdZ5u1Jjlu7qJLUfRaEJEmSJHVeVd0EfHFe2/VVdaS5ezNwRnN7C3BNVT1cVfcD+4Dz1iysJE0AC0KSpLEbcpr/KUluSHJf8/0pfY95mr8kaaleA/xOc/t04NN9jx1o2iRJjePbDiBJmgpXAr8M/Fpf2w7gxqramWRHc/9N807zfxrw+0meXlWPrHFmSdKESPJm4Ahw1dGmAd1qyLLbgG0AMzMzzM3NjSPiQIcPH17T9a2UeXu2bzoysH3Yuob1H8RtPH6TlnmceS0ISZLGrqpuSrJxXvMWYLa5vRuYA95E32n+wP1Jjp7m/+G1yCpJmixJtgIvAc6vqqNFnwPAmX3dzgAeHLR8Ve0CdgFs3ry5Zmdnxxd2nrm5OdZyfStl3p5Ldlw3sH3/xYPXNaz/IFdecLLbeMwmLfM481oQkiS1ZaaqDgJU1cEkpzXtp9MbB+Kooaf5r/So7iQcIZqEjDAZOQ8fPsz2TUs70Wwpr2mpR4wHmYTtCJORcxIywuTk7KokF9A7mPD3q+ov+h7aC7w7ydvonW16NnBrCxElqbMsCEmSumbk0/xXelR3Eo4QTUJGmIycc3NzvPVDX1vSMsOO9g6y1CPGg0zCdoTJyDkJGWFycnZBkqvpnVl6apIDwFvozSp2AnBDEoCbq+q1VXVXkj3A3fQuJbvMS48l6bEsCEmS2vJQkg3N2UEbgENN+8in+Utt2LiEU/8lrZ6qeuWA5ncu0P8K4IrxJZKkyeYsY5KktuwFtja3twLX9rVflOSEJGfhaf6SJEnSqvMMIUnS2A05zX8nsCfJpcADwMsBPM1fXeLZQJI0ue74zJ8PvJx3/84Lj2kb9n4/qK+0XlgQkiSN3ZDT/AHOH9Lf0/wlSZKkMfKSMUmSJEmSpCmzaEEoybuSHEpyZ1/bTyb5TJLbm68f6Hvs8iT7ktyb5MXjCi5JkiRJkqTlGeUMoSuBCwa0/5eqenbz9dsASc4BLgLObZZ5e5LjViusJEmSJEmSVm7RglBV3QR8ccTn2wJcU1UPV9X9wD7gvBXkkyRJkiRJ0ipbyaDSP5LkXwAfBbZX1ZeA04Gb+/ocaNqOkWQbsA1gZmaGubm5oSs6fPjwgo/Pt33TkYHtg55jKX1Xaqmvo6t8Hd2yHl7HengNsH5ehyRJkqT1b7kFof8G/DRQzfe3Aq8BMqBvDXqCqtoF7ALYvHlzzc7ODl3Z3NwcCz0+36CpBQH2X3zscyyl70ot9XV0la+jW9bD61gPrwHWz+uQJEmStP4tqyBUVQ8dvZ3kfwIfbO4eAM7s63oG8OCy062yjUOKP5IkSZIkSdNkWdPOJ9nQd/efAEdnINsLXJTkhCRnAWcDt64soiRJkiRJklbTomcIJbkamAVOTXIAeAswm+TZ9C4H2w/8K4CquivJHuBu4AhwWVU9Mp7okiRJkiRJWo5FC0JV9coBze9coP8VwBUrCSVJkiRJkqTxWdYlY5IkSZIkSZpcFoQkSZIkdV6SdyU5lOTOvrZTktyQ5L7m+1P6Hrs8yb4k9yZ5cTupJam7LAhJkiRJmgRXAhfMa9sB3FhVZwM3NvdJcg5wEXBus8zbkxy3dlElqfssCEmSJEnqvKq6CfjivOYtwO7m9m7gZX3t11TVw1V1P7APOG9NgkrShFh0UGlJkiRJ6qiZqjoIUFUHk5zWtJ8O3NzX70DTdowk24BtADMzM8zNzY0v7TyHDx9e0/Wt1KTlnTkRtm86ckz7oNcwqN9q9F2o/yCTto0nLS9MXuZx5rUgJEmSJGm9yYC2GtSxqnYBuwA2b95cs7OzY4z1WHNzc6zl+lZq0vL+0lXX8tY7jv3Iu//i2WPaLtlx3cDnWGnfhfoPcuUFJ0/UNp603wmYvMzjzGtBSJIkqSPu+MyfD/zgsH/nhS2kkSbCQ0k2NGcHbQAONe0HgDP7+p0BPLjm6SSpwywISZKkdWnjvMJK75R9//WR1pm9wFZgZ/P92r72dyd5G/A04Gzg1lYSalXNf28Hi+bScjmotCSpVUl+PMldSe5McnWSJyw0jbAkaToluRr4MPCdSQ4kuZReIehFSe4DXtTcp6ruAvYAdwO/C1xWVY+0k1ySusnDZJKk1iQ5HXgdcE5V/WWSPfSmCT6H3jTCO5PsoDeN8JtajKoOG3S0WNL6U1WvHPLQ+UP6XwFcMb5EkjTZPENIktS244ETkxwPnERvjIdh0whLkiRJWgWeISRJak1VfSbJzwMPAH8JXF9V1ycZNo3wY6x0quBJmHZ0EjJCuzlHnc532PTD47aU7bKUKZLbNAm/l5OQESYnpyRp/bEgJElqTTM20BbgLODLwHuTvGrU5Vc6VfAkTDs6CRmh3ZyjTue7fdORgdMPj90dXzumadgAqEuZIrlNk/B7OQkZYXJySpLWHy8ZkyS16YXA/VX1uar6OvB+4HtpphEGmDeNsCRJkqRVYEFIktSmB4DnJTkpSegNDHoPj04jDI+dRliSJEnSKvCSMUlSa6rqliTvA24DjgB/TO8SsCcCe5ophR8AXt5eSkmSJGn9sSA0xLApbIdd8y9JWp6qegvwlnnNDzNkGmFJkiRJK2dBSJIkTYxhB2wkSZK0NBaEJEmSJEkawAMRWs8cVFqSJEmSJGnKeIbQKnC8IUmSJEmSNEk8Q0iSJEmSJGnKWBCSJEmSJEmaMhaEJEmSJE20JD+e5K4kdya5OskTkpyS5IYk9zXfn9J2TknqEgtCkiRJkiZWktOB1wGbq+oZwHHARcAO4MaqOhu4sbkvSWpYEJIkSZI06Y4HTkxyPHAS8CCwBdjdPL4beFlL2SSpk5xlbImGzSgmSZIkae1V1WeS/DzwAPCXwPVVdX2Smao62PQ5mOS0VoNKUsdYEJIkSVpjww4wbd+0xkGkdaAZG2gLcBbwZeC9SV61hOW3AdsAZmZmmJubG0fMgQ4fPrym61upLuTdvunIMW3DMs2cOHr/Qf2W6peuunZg+1Le27uwjZdi0vLC5GUeZ14LQpIkqXM8I1fSErwQuL+qPgeQ5P3A9wIPJdnQnB20ATg0aOGq2gXsAti8eXPNzs6uTWp6hYm1XN9KdSHvJQP2D/svnh3Y95euupa33nHsR95B/Qc9bxuuvODk1rfxUnThd2KpJi3zOPM6hpAkSZKkSfYA8LwkJyUJcD5wD7AX2Nr02fp/2bv/uMnq+r77r7egiIIKQS6XH8lqgiToRqIbYmprr0iIRKxrEvXGogFDHtukarTZ3LJo7mrScj82TbFajW020bipIBIjgWpLJegVm7uCiqLLDwkoKy6srD9QWW0xi5/7jzkbh2tndq8f8+vMvJ6Px/WYme85Z+b9vWbOnDPf8z3fA/TuviFJM8oeQpIkSZJaq6quT/J+4NPAXuAzdHr8HAFcnuR8Oo1GLx5fSkmaPDYIDdHi7u6b1u3lvM0fYseWs8aUSJIkSZo+VfVG4I2Lih+g01tIktTDQU8ZS/KuJLuT3NRVdnSSa5Lc3twe1TXtwiR3JLktyXOHFVySJEmSJEkrs5QxhN4NnLmobDNwbVWdBFzbPCbJKcDZwFOaZd6R5JCBpZUkSZIkSdKqHbRBqKo+BnxjUfEGYFtzfxvwwq7yy6rqgaq6E7gDOG1AWSVJUyjJ45K8P8nnk9ya5GcP1BNVkiRJ0uqtdAyhuaraBdBcxvHYpvx44Lqu+XY2ZftJshHYCDA3N8fCwkLfF9uzZ88Bpy+2ad3eJc87SnOHd7Itpy6TaLnvx6SyHpNjGuoA01OPMXgrcHVVvSjJI4BHAa+n0xN1S5LNdHqiXjDOkJIkSdI0GfSg0ulRVr1mrKqtdEb/Z/369TU/P9/3SRcWFjjQ9MXOWzSY86TYtG4vF28/lB3nzI87yqos9/2YVNZjckxDHWB66jFKSR4DPBs4D6Cqvgd8L8kGYL6ZbRuwgA1CkiRJ0sCstEHo3iRrmt5Ba4DdTflO4MSu+U4A7llNQEnSVHsS8FXgz5I8DbgBeA39e6I+xHJ6m/bShl5dbcgIg885jN6++3rqTrJ+GSftM9CGz2UbMkJ7ckqSps9KG4SuAs4FtjS3V3aVX5rkzcBxwEnAJ1YbUpI0tQ4Fng68uqquT/JWmgsVLMVyepv20oZeXW3ICIPPOYzevvt66k6yfhknrXdxGz6XbcgI7ckpSZo+S7ns/HuBjwMnJ9mZ5Hw6DUFnJLkdOKN5TFXdDFwO3AJcDbyyqh4cVnhJUuvtBHZW1fXN4/fTaSC6t+mByqKeqJIkSZIG4KCHyarqpX0mnd5n/ouAi1YTSpI0G6rqK0m+nOTkqrqNzrblluavV09USZIkSQMw2f2mJUmz4NXAJc0Vxr4IvIJOD9bLm16pdwEvHmM+SZKkkdh+97d6nja9Y8tZY0ijaWeDkCRprKrqRmB9j0k9e6JKkiTNmrU2EmkIDjqGkCRJkiRJkqaLDUKSJEmSJEkzptWnjPXqNidJkiRJkqQDs4eQJEmSpFZL8rgk70/y+SS3JvnZJEcnuSbJ7c3tUePOKUmTxAYhSZIkSW33VuDqqvpx4GnArcBm4NqqOgm4tnksSWq0+pQxSZIkSbMtyWOAZwPnAVTV94DvJdkAzDezbQMWgAtGn1CTxqFHpA4bhCRJkiS12ZOArwJ/luRpwA3Aa4C5qtoFUFW7khzba+EkG4GNAHNzcywsLIwkNMCePXtG+nqrNQl5N63bu19Zv0xzh/eef5ItJ/O43wuYjM/EcrUt8zDz2iAkSZIkqc0OBZ4OvLqqrk/yVpZxelhVbQW2Aqxfv77m5+eHErKXhYUFRvl6qzUJec/r0btnxznzPed92yVXcvH2dv3k3bRu75Iz96v3KE3CZ2K52pZ5mHkdQ0iSJElSm+0EdlbV9c3j99NpILo3yRqA5nb3mPJJ0kRqV3OpJEmaKo7jIGm1quorSb6c5OSqug04Hbil+TsX2NLcXjnGmJI0cWwQkiRJktR2rwYuSfII4IvAK+icDXF5kvOBu4AXjzGfJE0cG4QkSZIktVpV3Qis7zHp9FFnkaS2sEFIkiRJktRa/U4/3rRuxEGklnFQaUmSJEmSpBljg5AkSZIkSdKM8ZSxMejVpXHHlrNWPa8kSZIkSdJS2ENIkiRJkiRpxtggJEkauySHJPlMkg82j49Ock2S25vbo8adUZIkSZomNghJkibBa4Bbux5vBq6tqpOAa5vHkiRJkgbEMYQkSWOV5ATgLOAi4Leb4g3AfHN/G7AAXDDqbNKkcExBSZI0aDYITYheO3qSNCPeArwOOLKrbK6qdgFU1a4kx44lmSRJGqp+v4Ns9JaGzwYhSdLYJHk+sLuqbkgyv4LlNwIbAebm5lhYWFjW8nv27Fn2MqPWhoyw8pyb1u0dfJg+5g4f7eutxHIyvu2SK3uWrzv+sUt+ve13f2tFz9GGz2UbMkJ7ckqSpo8NQpKkcXoW8IIkzwMeCTwmyXuAe5OsaXoHrQF291q4qrYCWwHWr19f8/Pzy3rxhYUFlrvMqLUhI6w853kj7CG7ad1eLt4+2bs+g8i445z5Jc/b7/9/sOdow+eyDRmhPTklSdPHQaUlSWNTVRdW1QlVtRY4G/hIVb0MuAo4t5ntXKB3VwhJkiRJK2KDkCRpEm0BzkhyO3BG81iSJEnSgEx2v2lJ0syoqgU6VxOjqr4OnD7OPJKkdklyCPAp4O6qen6So4H3AWuBHcBLquq+8SWUBmu5FyZyoG4tZoOQJEnSFPES9ZphrwFuBR7TPN4MXFtVW5Jsbh5fMK5wkjRpbBCSJEmS1GpJTgDOAi4Cfrsp3gDMN/e30emFaoOQ1KVfLyMPJMwGxxCSJEmS1HZvAV4HfL+rbK6qdgE0t8eOI5gkTapV9RBKsgO4H3gQ2FtV6z1XV5Ik9bLcsQ4kaSmSPB/YXVU3JJlfwfIbgY0Ac3NzLCwsDDbgAezZs2ekr7daw8i7ad3enuX9Xqff/L3MHb68+SfBMDP3+p8u9/+/WNs+w9C+zMPMO4hTxn6uqr7W9dhzdSVJkiSNyrOAFyR5HvBI4DFJ3gPcm2RNVe1KsgbY3WvhqtoKbAVYv359zc/Pjyh250f3KF9vtYaR97x+pyyd0/t1+s3fy6Z1e7l4e7tGSRlm5l7/0+X+/xdr22cY2pd5mHmHccrYBjrn6NLcvnAIryFJkiRJVNWFVXVCVa0FzgY+UlUvA64Czm1mOxe4ckwRJWkirbZBqIAPJ7mh6WoJnqsrSZIkafy2AGckuR04o3ksSWqsti/as6rqniTHAtck+fxSF1zOubr9zpmb1fNBx32+Y9vOuezHekyOaagDTE89JElqq6paoHM1Marq68Dp48wjSZNsVQ1CVXVPc7s7yRXAaQzhXN1+58wt5/zRSTCo80GXej7nsLTtnMt+rMfkmIY6wPTUQ5IkSdL0W3HrRJJHAw+rqvub+78A/D4/OFd3C56rK0mSJElaJq9MKQ3farqrzAFXJNn3PJdW1dVJPglcnuR84C7gxauPKUmSpJXyh5UkyW2BFltxg1BVfRF4Wo9yz9WVJEmSJEmaYKsf0Eat1K91eMeWs0acRJI0Sdw+SJIkzYbVXnZekiRJkiRJLWMPIUmSNFCOUTBb7FUmSVI72SDUQu54SZIkSZpU/l6R2sFTxiRJkiRJkmaMPeukLCcAACAASURBVIQkSdJB9Tra65FeSZKk9rJBSJI0NklOBP4ceALwfWBrVb01ydHA+4C1wA7gJVV137hySrPGBkBJkqafDUJTxHN1JbXQXmBTVX06yZHADUmuAc4Drq2qLUk2A5uBC8aYU5Ikaab1+r25ad1e5kcfRQNig5AewkYlSaNUVbuAXc39+5PcChwPbIB/2L/YBixgg5AkSRPFq0pK7WaDkCRpIiRZC/wUcD0w1zQWUVW7khzbZ5mNwEaAubk5FhYWlvWae/bsWfYyozbqjJvW7V3yvG+75Mp/uD93+A8eb1o38FgDMXf48uo3DpOcsftz2P257Jd33OtWG9ZvaE9OSdL0sUFoBthyL2nSJTkC+EvgtVX17SRLWq6qtgJbAdavX1/z8/PLet2FhQWWu8yojTrjeSvcZmxat5eLt0/2boUZV2fHOfP/cL/7c9nvM9M9/zi0Yf2G9uScZI5HJ0krM5l7HJKkmZHk4XQagy6pqg80xfcmWdP0DloD7B5fQknShHM8upbwQLU0WWwQkiSNTTpdgd4J3FpVb+6adBVwLrClub2yx+KSRqj7h9ymdXtX3JtMGjTHo5MGz8a72WCDkCRpnJ4FvBzYnuTGpuz1dBqCLk9yPnAX8OIx5ZMktcg4xqNbjbaNIbU476SOebbPJI/L1k/bMs8dPv4x45ar7evdINkgpCXpdVTQK49JWq2q+lug34BBp48yiySp3cY1Ht1qtG0MqcV5J72n4CSPy9ZP2zJvWreXl7ToMwztX+8G6WFDeVZJkiRJGpEDjUfXTHc8OklaxAYhSZIkSa21hPHowPHoJGk/7emLptboNwCZp5hJkiRpCByPTpJWwAYhSZIkSa3leHSStDKeMiZJkiRJkjRj7CEkSdIM6nd6ryRJkmaDDUJaMX9MSJIkSZLUTjYISZIkSZKkFfGiQu1lg5AkSZIkTbFB/GBfu/lDbFq3l/M8S0CaGjYISZIkSZKksbGX0XjYICRJUgv12nHqt9PkmG9qq+V8ziVJ0vLYIKSRGdYPkuX8AHInUtIk8PtJkiRJ4/awcQeQJEmSJEnSaNlDSJKkKeGpYZokntYoDdewepu6PmqYlvv5slf1cNlDSJIkSZIkacbYQ0gzpbuFufuymbYyS1oqj1RJ7bHcI9HLvQT3ap9DkqRxskFIrWe3VkmSJEmSlmdoDUJJzgTeChwC/GlVbRnWa0mSptMotiWTcpS/X47u3oxS23kQR+Pg7xJpPPzOn3xDaRBKcgjwR8AZwE7gk0muqqpbhvF6kqTp47ZEUht5WulkcVsiTZ9hng48iByDHtx938HBYdRjWD2ETgPuqKovAiS5DNgA+MUrSVqq1m9LPDImjc7i9W3Tur0MYlfX9bj1RrItGdUPw0HOu5L5pWk3a436qarBP2nyIuDMqvr15vHLgZ+pqld1zbMR2Ng8PBm47QBPeQzwtYEHHT3rMVmsx+SYhjrAQ+vxI1X1+HGGabshbEt6acNnrw0ZoR05zTg4bcjZhoxw4JxuS1ZpRNuS1WjL53Qf8w5f2zK3LS+0L/Nq8/bdlgyrh1B6lD2k5amqtgJbl/Rkyaeqav0ggo2T9Zgs1mNyTEMdYHrqMUEGui3p+QIteM/akBHakdOMg9OGnG3ICO3J2WJD35asRtvef/MOX9syty0vtC/zMPM+bBhPSuf83BO7Hp8A3DOk15IkTSe3JZKk1XJbIkl9DKtB6JPASUmemOQRwNnAVUN6LUnSdHJbIklaLbclktTHUE4Zq6q9SV4F/A86l3d8V1XdvIqnHEsXziGwHpPFekyOaagDTE89JsIQtiW9tOE9a0NGaEdOMw5OG3K2ISO0J2crjWhbshpte//NO3xty9y2vNC+zEPLO5RBpSVJkiRJkjS5hnXKmCRJkiRJkiaUDUKSJEmSJEkzZuIbhJKcmeS2JHck2TzuPMuRZEeS7UluTPKppuzoJNckub25PWrcORdL8q4ku5Pc1FXWN3eSC5v357Ykzx1P6ofqU4c3Jbm7eT9uTPK8rmkTVweAJCcm+WiSW5PcnOQ1TXnb3o9+9WjNe5LkkUk+keSzTR1+rylv1XsxzQ62vUhyVJIrknyueS+f2jXtNUluat7b13aVD/w7e0g5/zDJ55tlrkjyuEnL2DX9d5JUkmMmMWOSVzfPe3OSf7eajMPKmeTUJNc135ufSnLaKjPut81cND1J/mNTh88lefrB6jfodWdIGQe93gw8Y9f0gaw3Gr302QdaNM9jk/zX/GAf4xXjyNqVp+c+z6J5+n6eR22Jec9pcn4uyf9K8rRxZO3Kc9DMXfP+dJIHk7xolBkXZVhS3iTzzbbp5iR/M+qcXTmW8pmYqPWuyXRIks8k+WCPacNZ56pqYv/oDPz2BeBJwCOAzwKnjDvXMvLvAI5ZVPbvgM3N/c3AH4w7Z4/czwaeDtx0sNzAKc37chjwxOb9OmRC6/Am4Hd6zDuRdWiyrQGe3tw/Evi7Jm/b3o9+9WjNewIEOKK5/3DgeuCZbXsvpvVvKdsL4A+BNzb3fxy4trn/VOAm4FF0Lrbw18BJzbSBfmcPMecvAIc29/9gNTmHlbGZfiKdgV2/xKLt4yRkBH6ueXxY8/jYCX2/Pwz8YnP/ecDCKnPut81cNP15wH+n8z34TOD6g9VvCOvOMDIObL0ZVsZBrjf+jeePPvtAi+Z5PT/Yf3g88A3gEWPM3HOfZ9E8PT/PE5z3HwFHNfd/cZx5l5q5mXYI8BHgvwEvmuS8wOOAW4Afbh6vahs6grwTtd41OX4buBT4YI9pQ1nnJr2H0GnAHVX1xar6HnAZsGHMmVZrA7Ctub8NeOEYs/RUVR+js0J065d7A3BZVT1QVXcCd9B538aqTx36mcg6AFTVrqr6dHP/fuBW4Hja9370q0c/E1eP6tjTPHx481e07L2YYkvZXpwCXAtQVZ8H1iaZA34CuK6qvltVe4G/AX6pWWbQ39lDyVlVH27KAK4DTpi0jI3/ALyOzrqzGsPK+JvAlqp6oFlu94TmLOAxzf3HAvesJuQStpkbgD9vvgevAx6XZM1B6jfQdWcYGQe83gzr/wiDW280BkvcByrgyCQBjqDzOdrLmBxgn6dbv8/zyC0lb1X9r6q6r3m46vV9tZb4PwZ4NfCXwGq3R6uyxLz/HPhAVd3VLDO2zEvMO1HrXZITgLOAP+0zy1DWuUlvEDoe+HLX450c+EfkpCngw0luSLKxKZurql3Q2UAAx44t3fL0y9229+hVTRe7d+UH3ddbUYcka4GfotPC3dr3Y1E9oEXvSdON80Y6G+VrqqrV78WUWcr/+7PALwOkc4rNj9DZIbwJeHaSH0ryKDpHYE5slhn0d/awcnb7NTpHkCYqY5IXAHdX1WdXkW2oGYEnA/8kyfVJ/ibJT09oztcCf5jky8C/By5cZc6D6VePA9Vv1Ps7K8nYbbXrzVIsO+OA1xuNWY99oH3eTqcR+B5gO/Caqvr+SMMt0mefp9tE7ecsIW+38xn++n5QB8uc5Hg6BwL+8zjyLbaE//GTgaOSLDS/f3919Cl/YAl5J229ewudxv9+GYayzk16g1B6lLXp6MizqurpdLolvjLJs8cdaAja9B79J+BHgVOBXcDFTfnE1yHJEXSODry2qr59oFl7lE1MXXrUo1XvSVU9WFWn0vmxdlq6xvroYSLrMMWW8v/eQmdH5UY6R9w+A+ytqlvpnC5yDXA1nR/owzpCNNScSd7QlF0ySRmbBo03AP96FbmGmrFZ5lDgKDpdsf9v4PLmyOGk5fxN4F9V1YnAvwLeuYqMS9GvHpP0PbfijANab5ZiWRmHsN5ojA6yL/dc4EbgODr7RG9P8hjGaAn7PJO0/i95Hy3Jz9FpELpglPl6WULmtwAXVNWDo0+3vyXkPRR4Bp1eLs8F/p8kTx5xzH+whLwTs94leT6wu6puONBsPcpWvc5NeoPQTh569PMEVtktepSq6p7mdjdwBZ0uwffu69rV3I61+98y9Mvdmveoqu5tvhi+D/wJPzh9Z6LrkOThdHYgLqmqDzTFrXs/etWjre9JVX0TWADOpIXvxZQ66P+7qr5dVa9odg5+lc754nc2095ZVU+vqmfT6TJ8e7PYoL+zh5WTJOcCzwfOqarV7CAMI+OP0hlL67NJdjTP+ekkT5igjPue9wNNd+xP0DlKt5pBfIeV81xg3/bgLxj+6aj96nGg+o16f2clGQe53gwj46DXG41Jn325bq/gB989d9D5DvjxUWbsZ9E+T7eJ3M85QF6S/CSd03E2VNXXRxytrwNkXg9c1qz/LwLekWTsw40c5DNxdVV9p6q+BnwMGOvg3XDAvJO03j0LeEHzXl8GPCfJexbNM5R1btIbhD4JnJTkiUkeAZwNXDXmTEuS5NFJjtx3n87AhTfRyX9uM9u5wJXjSbhs/XJfBZyd5LAkTwROAj4xhnwHtegcy1+i837ABNehOTL9TuDWqnpz16RWvR/96tGm9yTJ49NcgSbJ4cDPA5+nZe/FFDvo9iLJ45ppAL8OfGzfUdokxza3P0zn9J33NvMN+jt7KDmTnEnnaOcLquq7k5axqrZX1bFVtbaq1tLZqXl6VX1lUjI28/0V8Jxm2pPpDPD7tRVmHGbOe4B/2tx/Dl0Ng0NyFfCr6Xgm8K3mNLAD1W/U+zvLzjjg9WbgGYew3mgMDrAv1+0u4PRm/jngZOCLo0m4vwPs83Tr93keuaXkbb5HPwC8vKr+bvQpH2opmavqiV3r//uBf1lVfzXysCz5M3ElndOuD02nh+PP0Bkza+SWmHdi1ruqurCqTmje67OBj1TVyxbNNpx1rsY4ivZS/uicM/93dK6+8IZx51lG7ifR6d79WeDmfdmBH6IzeOTtze3R487aI/t76Zy+8/d0dj7OP1BuOt2ZvwDcRnPVk3H/9anDf6FzfujnmhVqzSTXocn1j+l0BfwcnS6NNzbrRNvej371aM17AvwknVM5Pken4epfN+Wtei+m+a/X9gL4DeA3mvs/27xPn6ezU3hU17L/k86VMT4LnN5VPvDv7CHlvIPOeeX71q//PGkZFz3/DlZ5taQh/R8fAbynWcc/DTxnQt/vfwzc0JRfDzxjlRl7bTO7Mwb4o6YO24H1B6rfMNadIWUc9Hoz8IyDXm/8G/0f/feBuj8bx9G5euD25vvnZWPO3G+fZ0mf5wnN+6fAfV3vwacm/X+8aP53M96rjC0pL53TrW9p5nntJOedtPWuK/s8zVXGRrHOpXlySZIkSZIkzYhJP2VMkiRJkiRJA2aDkCRJkiRJ0oyxQUiSJEmSJGnG2CAkSZIkSZI0Y2wQkiRJkiRJmjE2CEmSJEmSJM0YG4QkSZIkSZJmjA1CkiRJkiRJM8YGIUmSJEmSpBljg5AkSZIkSdKMsUFIkiRJkiRpxtggJEmSJEmSNGNsEJIkSZIkSZoxNghJkiRJkiTNGBuEJEmSJEmSZowNQpIkSZIkSTPGBiFJkiRJkqQZY4OQJEmSJEnSjLFBSK2UZEeSnx/0vJKk2ZLkvCR/u8rnmE+yc1yvL0lqryRrk1SSQ5vH/z3JuePOpdlgg5B0AEkWkvz6uHNIkiRJmn5V9YtVtQ08aKDhs0FIkiRJkiRpxtggpLFLckGSu5Pcn+S2JKcneXeSf9s1T9/u+EnelOT9Sd7XPMenkzxt0WynJvlckm818z2yWfaoJB9M8tUk9zX3T2imXQT8E+DtSfYkeXtT/uNJrknyjSbvS7qyPC/JLU2Ou5P8zoD/XZKkFUiyOckXmu/nW5L8Up/5ntL1HX9vktc35YcleUuSe5q/tyQ5bNGym5LsTrIrySu6yh+b5M+bbc2XkvxuEvfBJKllkvxU81vj/uY3xWVJ/m2vnjzNaWA/1tw/K8lnknw7yZeTvOkAr7GQ5NeT/ATwn4GfbX6LfDPJTzfbpkO75v+VJDcOqcqacu6MaKySnAy8CvjpqjoSeC6wYwVPtQH4C+Bo4FLgr5I8vGv6S4AzgScCPwmc15Q/DPgz4EeAHwb+N/B2gKp6A/A/gVdV1RFV9aokjwauaV7jWOClwDuSPKV5vncC/6Kpy1OBj6ygLpKkwfsCnUb+xwK/B7wnyZruGZIcCfw1cDVwHPBjwLXN5DcAzwROBZ4GnAb8btfiT2ie+3jgfOCPkhzVTHtbM+1JwD8FfhV4BZKk1kjyCOCvgP9C5zfHXwC/ssTFv0Pnu/9xwFnAbyZ54YEWqKpbgd8APt78FnlcVX0S+DpwRtesL2sySctmg5DG7UHgMOCUJA+vqh1V9YUVPM8NVfX+qvp74M3AI+nsuO/zH6vqnqr6BvBf6ezQU1Vfr6q/rKrvVtX9wEV0dtb7eT6wo6r+rKr2VtWngb8EXtRM//umLo+pqvua6ZKkMauqv2i2A9+vqvcBt9Np1On2fOArVXVxVf2fqrq/qq5vpp0D/H5V7a6qr9JpVHp517J/30z/+6r6b8Ae4OQkhwD/F3Bh83w7gIsXLStJmnzPBB4OvKX5rn8/8MmlLFhVC1W1vdkGfQ54Lwf+zXEg2+g0ApHkaDoH1C9d4XNpxtkgpLGqqjuA1wJvAnY33S6PW8FTfbnrOb8P7KRzdHefr3Td/y5wBECSRyX546YL/7eBjwGPa3bge/kR4GeaLpvfTPJNOj8SntBM/xXgecCXkvxNkp9dQV0kSQOW5FeT3Nj13f1U4JhFs51IpydRL8cBX+p6/CUeup35elXt7Xq8b1tzDPCIHssev/xaSJLG6Djg7qqqrrIv9Zu5W5KfSfLR5tThb9Hp+bN4G7RU7wH+WZIj6JwF8T+ratcKn0szzgYhjV1VXVpV/5hOY0sBf0CnW+WjumZ7Qq9lu5y4704zLsMJwD1LePlNwMnAz1TVY4Bn73uaffEWzf9l4G+aLpv7/o6oqt9s6vLJqtpA53SyvwIuX0IGSdIQJfkR4E/onKL8Q1X1OOAmfvBdv8+XgR/t8zT30NlO7fPDLG078zU6vYcWL3v3EpaVJE2OXcDxSbq3HT/c3D7kt0uSxb9dLgWuAk6sqsfSGRto8Taol8W/Raiqu4GPA79Ep7epp4tpxWwQ0lglOTnJc5qBOf8PnTF8HgRuBJ6X5OjmC/W1B3mqZyT55WaAtdcCDwDXLSHCkc1rfrPpcvnGRdPvpTPmwz4fBJ6c5OVJHt78/XSSn0jyiCTnJHlsc+rat5u6SJLG69F0dqq/CtAM+PzUHvN9EHhCktc2g0gfmeRnmmnvBX43yeOTHAP8azpHaQ+oqh6kc3Dgoub5fgT47aUsK0maKB8H9gK/leTQJL/MD049/izwlCSnpnPxmjctWvZI4BtV9X+SnAb88yW+5r3ACc34Rd3+HHgdsA64YvlVkTpsENK4HQZsoXME9St0eta8nk5L92fpDDD9YeB9B3meK+mM0XAfnZbyX24aZQ7mLcDhzetfR2cg0W5vBV6UzhXI/mMzztAvAGfTOTL8FTo9mvZdaeblwI7m9LPfoDm/V5I0PlV1C51xez5OZ+d6HfD/9ZjvfjoDdf4zOt/vtwM/10z+t8CngM8B24FPN2VL8Wo6R4+/CPwtnSPF71pZbSRJ41BV3wN+mc7Fae6j89vjA820vwN+n86FCW6n813f7V8Cv5/kfjoHFJZ6FsFHgJuBryT5Wlf5FXR6nl5RVd9ZSX0kgDz0FEipfZrLNv5YVdn4IkmSJGkkkrwb2FlVv3uweYfw2l+gc3Xjvx71a2t62ENIkiRJkqSWSPIrdE6F/si4s6jdDh13AEmSJEmSdHBJFoBTgJc3V1eWVsxTxiRJkiRJkmaMp4xJkiRJkiTNmIk4ZeyYY46pxz/+8Tz60Y8ed5SD+s53vmPOAWpLTmhPVnMO1kpz3nDDDV+rqscPIZL6OOaYY2rt2rXjjtFXWz7z+7Qpr1mHp01525QVlpbXbcnorXRb0rbP30pMex2nvX4w/XW0fr0dcFtSVWP/e8YznlEf/ehHqw3MOVhtyVnVnqzmHKyV5gQ+VRPw/TpLf894xjOW9uaMSVs+8/u0Ka9Zh6dNeduUtWpped2WPPQPOBm4sevv28BrgaOBa+hc7vsa4KiuZS4E7gBuA557sNdY6bakbZ+/lZj2Ok57/aqmv47Wr7cDbUs8ZUySJEnSxKuq26rq1Ko6FXgG8F3gCmAzcG1VnQRc2zwmySnA2cBTgDOBdyQ5ZCzhJWkC2SAkSZIkqW1OB75QVV8CNgDbmvJtwAub+xuAy6rqgaq6k05PodNGnlSSJtREjCEkSZIkSctwNvDe5v5cVe0CqKpdSY5tyo8HrutaZmdT9hBJNgIbAebm5lhYWFh2mD179qxouTaZ9jpOe/1g+uto/ZbPBiFJkiRJrZHkEcAL6IwPdMBZe5TVfgVVW4GtAOvXr6/5+fllZ1pYWGAly7XJtNdx2usH019H67d8njImSZIkqU1+Efh0Vd3bPL43yRqA5nZ3U74TOLFruROAe0aWUpImnA1CkqShS/KuJLuT3NRj2u8kqSTHdJVdmOSOJLclee5o00qSJtxL+cHpYgBXAec2988FruwqPzvJYUmeCJwEfGJkKSVpwnnK2Bis3fyh/cp2bDlrDEkkaWTeDbwd+PPuwiQnAmcAd3WVdV8V5jjgr5M8uaoeHFnaFnBbImkWJXkUne3Gv+gq3gJcnuR8OtuTFwNU1c1JLgduAfYCr3Rbon623/0tznPbqhljg5Akaeiq6mNJ1vaY9B+A1/GDo7nQdVUY4M4k+64K8/Fh55QkTbaq+i7wQ4vKvk7nqmO95r8IuGgE0SSpdTxlTJI0FkleANxdVZ9dNOl44Mtdj3teFUaSJEnSytlDSJI0ck2X/zcAv9Brco+y/a4K0zzPqi8VPCqDvlTopnV79ysb5PO36dKtZh2eNuVtU1ZoX15J0vSxQUiSNA4/CjwR+GwS6Fz55dNJTmMZV4UZxKWCR2XQlwrtOc7BOYN7/jZdutWsw9OmvG3KCu3LK0maPp4yJkkauaraXlXHVtXaqlpLpxHo6VX1FbwqjCRJkjR0NghJkoYuyXvpDAp9cpKdzZVgeqqqm4F9V4W5Gq8KI0mSJA2cp4xJkoauql56kOlrFz32qjCSJEnSENlDSJIkSZIkacbYICRJkiRJkjRjPGVMkqQJsLbHVcMAdmw5a8RJJEnSNNp+97f2u0qp+xmzzR5CkiRJkiRJM8YGIUmSJEmSpBlz0AahJCcm+WiSW5PcnOQ1TfnRSa5Jcntze1TXMhcmuSPJbUmeO8wKSJIkSZIkaXmW0kNoL7Cpqn4CeCbwyiSnAJuBa6vqJODa5jHNtLOBpwBnAu9IcsgwwkuSJEmSJGn5DtogVFW7qurTzf37gVuB44ENwLZmtm3AC5v7G4DLquqBqroTuAM4bdDBJUmSJEmStDLLGkMoyVrgp4Drgbmq2gWdRiPg2Ga244Evdy22symTJEmSpBVL8rgk70/y+WZIi591KAtJWpklX3Y+yRHAXwKvrapvJ+k7a4+y6vF8G4GNAHNzc+zZs4eFhYWlxhmbQeTctG7vfmWDrvss/T9HpS1ZzTlYbckpSdKMeCtwdVW9KMkjgEcBr6czlMWWJJvpDGVxwaKhLI4D/jrJk6vqwXGFl6RJsqQGoSQPp9MYdElVfaApvjfJmqralWQNsLsp3wmc2LX4CcA9i5+zqrYCWwHWr19fRxxxBPPz8yurxQgtLCysOud5mz+0X9mOc1b3nIsNIucotCUntCerOQerLTklSZp2SR4DPBs4D6Cqvgd8L8kGYL6ZbRuwAFxA11AWwJ1J9g1l8fGRBpekCXXQBqF0ugK9E7i1qt7cNekq4FxgS3N7ZVf5pUneTKcl/iTgE4MMLUmSJGnmPAn4KvBnSZ4G3AC8hkVDWSTpHsriuq7lew5lsfjMhZX0DJ6FHsXTXse5w0dzJsc49arjNNVv2j+jw6jfUnoIPQt4ObA9yY1N2evpNARdnuR84C7gxQBVdXOSy4Fb6Fyh7JV2y5QkSZK0SocCTwdeXVXXJ3krzZWO+1jSUBaLz1xYSc/gWehRPO11fNslV3Lx9v1/Hg/6TI5x6lXHaarftH9Gh1G/gzYIVdXf0vvLFOD0PstcBFy0ilySJEmS1G0nsLOqrm8ev59Og9CqhrKQpFm1rKuMSZK0EknelWR3kpu6yv6wuUrM55JckeRxXdO8Kowk6SGq6ivAl5Oc3BSdTueshH1DWcD+Q1mcneSwJE/EoSwk6SFsEJIkjcK7gTMXlV0DPLWqfhL4O+BCgEVXhTkTeEeSQ0YXVZI0wV4NXJLkc8CpwP9LZyiLM5LcDpzRPKaqbgb2DWVxNQ5lIUkPseTLzkuStFJV9bEkaxeVfbjr4XXAi5r7XhVGktRTVd0IrO8xyaEsJGmZbBCSJE2CXwPe19xf0lVhYDBXhhmVg10ZoteVTaD/1T+GfSWUNl2pw6zD06a8bcoK7csrSZo+NghJksYqyRvoXJXykn1FPWbb76owMJgrw4zKwa4Mcd7mD/Us73f1j17zD/JKIW26UodZh6dNeduUFdqXV5I0fWwQkiSNTZJzgecDp1fVvkYfrwojSZIkDZmDSkuSxiLJmcAFwAuq6rtdk7wqjCRJkjRk9hCSJA1dkvcC88AxSXYCb6RzVbHDgGuSAFxXVb9RVTcn2XdVmL14VRhJkiRp4GwQkiQNXVW9tEfxOw8wv1eFkSRJkobIU8YkSZIkSZJmjD2EBmBtvyvDbDlrxEkkSZIkSZIOzgYhSZKmhAcoJEmStFSeMiZJkiRJkjRj7CE0RP2O1EqSJEmSJI2TPYQkSZIkSZJmjA1CkiRJkiRJM8YGIUmSJEmSpBljg5AkSZKkVkiyI8n2JDcm+VRTdnSSa5Lc3twe1TX/hUnuSHJbkueOL7kkTR4bhCRJkiS1yc9V1alVtb55vBm4tqpOAq5tHpPkFOBs4CnAmcA7khwyjsCSNIlscVE8ugAAH7pJREFUEJIkSZLUZhuAbc39bcALu8ovq6oHqupO4A7gtDHkk6SJ5GXnJUmSJLVFAR9OUsAfV9VWYK6qdgFU1a4kxzbzHg9c17XszqbsIZJsBDYCzM3NsbCwsOxQe/bsWdFybTLtdZw7HDat27tf+TTVuVcdp6l+0/4ZHUb9bBCSJEmS1BbPqqp7mkafa5J8/gDzpkdZ7VfQaVTaCrB+/fqan59fdqiFhQVWslybTHsd33bJlVy8ff+fxzvOmR99mCHpVcdpqt+0f0aHUT8bhCRJQ5fkXcDzgd1V9dSm7GjgfcBaYAfwkqq6r5l2IXA+8CDwW1X1P8YQeyKs3fyhcUeQpIlRVfc0t7uTXEHnFLB7k6xpegetAXY3s+8ETuxa/ATgnpEGlqQJ5hhCkqRReDedAT27OQioJGnJkjw6yZH77gO/ANwEXAWc28x2LnBlc/8q4OwkhyV5InAS8InRppakyWUPIUnS0FXVx5KsXVS8AZhv7m8DFoAL6BoEFLgzyb5BQD8+iqySpIk1B1yRBDq/Yy6tqquTfBK4PMn5wF3AiwGq6uYklwO3AHuBV1bVg+OJLkmTxwYhSdK4rGoQUBjMQKCjcrCBAHsNZDko0z5AqlmHp01525QV2pd3ElTVF4Gn9Sj/OnB6n2UuAi4acjRJaiUbhCRJk2ZJg4DCYAYCHZWDDQR43hDHClrJgJFtGpjRrMPTprxtygrtyytJmj4HHUMoybuS7E5yU1fZm5LcneTG5u95XdMuTHJHktuSPHdYwSVJrXdvM/gnDgIqSZIkjdZSBpV+N/sPBArwH6rq1Obvv4EDgUqSlsVBQCVJkqQxOegpY30GAu1n6gcC3X73t4barV+SplGS99IZQPqYJDuBNwJbcBDQmbK2x/Zzx5azxpBEkiRJqxlD6FVJfhX4FLCpqu5jFQOBtmVgvbnDhzPw56Dr3pb/Z1tyQnuymnOw2pJz0lXVS/tMchBQSZIkaQxW2iD0n4B/Q2eQz38DXAz8GqsYCPSII45oxcB6b7vkSi7ePvixuFcy4OeBtGWgwrbkhPZkNedgtSWnJEmSJC3HUsYQ2k9V3VtVD1bV94E/oXNaGDgQqCRJkiRJ0sRbUVeXJGuqalfz8JeAfVcguwq4NMmbgeNwIFBJkqaC4/9IkiRNl4M2CPUZCHQ+yal0TgfbAfwLcCBQSZIkSZKkNljKVcZ6DQT6zgPM70CgkiRJkiRJE2xFYwhJkiRJkiSpvWwQkiRJkiRJmjE2CEmSJEmSJM0YG4QkSZIkSZJmzIouOy9JkjQptt/9Lc7b/KH9yndsOWsMaSRJktrBHkKSJEmSWiPJIUk+k+SDzeOjk1yT5Pbm9qiueS9MckeS25I8d3ypJWny2CAkSZIkqU1eA9za9XgzcG1VnQRc2zwmySnA2cBTgDOBdyQ5ZMRZJWli2SAkSZIkqRWSnACcBfxpV/EGYFtzfxvwwq7yy6rqgaq6E7gDOG1UWSVp0jmGkCRprJL8K+DXgQK2A68AHgW8D1gL7ABeUlX3jSmiJGlyvAV4HXBkV9lcVe0CqKpdSY5tyo8Hruuab2dT9hBJNgIbAebm5lhYWFh2qD179qxouTaZ9jrOHQ6b1u3dr3ya6tyrjtNUv2n/jA6jfjYITYi1PQbDBAfElDTdkhwP/BZwSlX97ySX0+nefwqd7v9bkmym0/3/gjFGlSSNWZLnA7ur6oYk80tZpEdZ7VdQtRXYCrB+/fqan1/KUz/UwsICK1muTaa9jm+75Eou3r7/z+Md58yPPsyQ9KrjNNVv2j+jw6ifp4xJksbtUODwJIfS6Rl0D/27/0uSZtezgBck2QFcBjwnyXuAe5OsAWhudzfz7wRO7Fr+BDrbGEkSNghJksaoqu4G/j1wF7AL+FZVfZhF3f+BY/s/iyRpFlTVhVV1QlWtpdOb9CNV9TLgKuDcZrZzgSub+1cBZyc5LMkTgZOAT4w4tiRNLE8ZkySNTXNp4A3AE4FvAn+R5GXLWH7V4z6MysHO++41bsGgDGI8jEGMqzCssRnaNO5D28Y3aFPeNmWF9uWdcFuAy5OcT+cAw4sBqurm5lTkW4C9wCur6sHxxZSkyWKDkCRpnH4euLOqvgqQ5APAP6Lp/t8MDtrd/f8hBjHuw6gc7Lzv8/qMJTcIKxkfYHHeXvmW+7yDeI5e2jTuQ9vGN2hT3jZlhfblnTRVtQAsNPe/DpzeZ76LgItGFkySWsQGIUnSON0FPDPJo4D/TWeH/lPAd+h0+9/CQ7v/awV6XbjAixZIkiTNNhuEJEljU1XXJ3k/8Gk63fk/Q6fHzxH06P4vSZIkaTBsEJIkjVVVvRF446LiB+jT/V+SJEnS6nmVMUmSJEmSpBljg5AkSZIkSdKMsUFIkiRJkiRpxjiGUAv1uloMeMUYSZIkSZK0NPYQkiRJkiRJmjE2CEmSJEmSJM0YG4QkSZIkSZJmjGMISZKkf7BvnLpN6/ZyXp8x6yRJktR+9hCSJEmSJEmaMTYISZIkSZIkzZiDNggleVeS3Ulu6io7Osk1SW5vbo/qmnZhkjuS3JbkucMKLkmSJEmSpJVZSg+hdwNnLirbDFxbVScB1zaPSXIKcDbwlGaZdyQ5ZGBpJUmSJM2kJI9M8okkn01yc5Lfa8o9WC1JK3DQBqGq+hjwjUXFG4Btzf1twAu7yi+rqgeq6k7gDuC0AWWVJEmSNLseAJ5TVU8DTgXOTPJMPFgtSSuy0quMzVXVLoCq2pXk2Kb8eOC6rvl2NmX7SbIR2AgwNzfHnj17WFhYWGGc0Zk7vHPllVHp9T/p9/rd87bl/9mWnNCerOYcrLbklCRp2lVVAXuahw9v/orOQen5pnwbsABcQNfBauDOJPsOVn98dKklaXIN+rLz6VFWvWasqq3AVoD169fXEUccwfz8/IDjDN7bLrmSi7cP+t/W345z5vcr63cZ4O55FxYWWvH/bEtOaE9Wcw5WW3K2WZLHAX8KPJXONuPXgNuA9wFrgR3AS6rqvjFFlCRNiKaHzw3AjwF/VFXXJ1nVwerFB6pXciBoFg4gTXsd+x34n6Y696rjNNVv2j+jw6jfSls27k2ypvnCXQPsbsp3Aid2zXcCcM9qAkqSpt5bgaur6kVJHgE8Cng9ne7/W5JsptP9/4JxhtR0W9vvYMuWs0acRNKBVNWDwKnNwYQrkjz1ALMv6WD14gPVKzkQNAsHkKa9jv0O/Pc6QN9Wveo4TfWb9s/oMOq30svOXwWc29w/F7iyq/zsJIcleSJwEvCJ1UWUJE2rJI8Bng28E6CqvldV36T/WHWSJNFsKxbojA10b3OQGg9WS9LSHbSHUJL30jkn95gkO4E3AluAy5OcD9wFvBigqm5OcjlwC7AXeGXTiq8V6nfEUpKmxJOArwJ/luRpdE4DeA39x6p7iEF08x+Vg3XzHeX4dNC/i/i+HEsZM2+5/+9hdcUfRDf/pYzPNwht687eprxtygrtyzsJkjwe+Puq+maSw4GfB/6AHxys3sL+B6svTfJm4Dg8WC1JD3HQBqGqemmfSaf3mf8i4KLVhJIkzYxDgacDr27GgXgrzdVhlmIQ3fxH5WDdfPuNDzcs/bqI78uxad3eg46Zt9xu5r3qOIiu6oPo5r+U8fkGoW3d2duUt01ZoX15J8QaYFszjtDDgMur6oNJPo4HqyVp2UY3OrIkSfvbCeysquubx++n0yDUb6w6SdKMqqrPAT/Vo/zreLBakpZtpWMISZK0alX1FeDLSU5uik6ncyS331h1kiRJkgbAHkKSpHF7NXBJc4WxLwKvoDkVYHH3f0mSJEmDYYOQJGmsqupGYH2PST27/0uSJElaPU8ZkyRJkiRJmjE2CEmSJEmSJM0YG4QkSZIkSZJmjA1CkiRJkiRJM8YGIUmSJEmSpBnjVcb6WLv5Qz3LN60bcRBJkoag33ZOkiRJs8EGIUmS1Bq9GrIm5WBNv0a2HVvOGnESSZKkg/OUMUmSJEmSpBljg5AkSZIkSdKMsUFIkiRJkiRpxjiG0BTpHrtg07q9nNc87jV2geMcSJIkSZI0u+whJEmSJGniJTkxyUeT3Jrk5iSvacqPTnJNktub26O6lrkwyR1Jbkvy3PGll6TJY4OQJEmSpDbYC2yqqp8Angm8MskpwGbg2qo6Cbi2eUwz7WzgKcCZwDuSHDKW5JI0gTxlbAb0Oz1MkiZFs4P+KeDuqnp+kqOB9wFrgR3AS6rqvvEl1Ki57ZK0WFXtAnY19+9PcitwPLABmG9m2wYsABc05ZdV1QPAnUnuAE4DPj7a5JI0mWwQkiRNgtcAtwKPaR7vO9q7Jcnm5vEF4wonSZosSdYCPwVcD8w1jUVU1a4kxzazHQ9c17XYzqZs8XNtBDYCzM3NsbCwsOw8e/bsWdFybTLtdZw7vDMO62LTVOdedZym+u3+xrd42yVX7le+7vjHjiHN4A1jHbRBSJI0VklOAM4CLgJ+uynud7RXLWDvHknDlOQI4C+B11bVt5P0nbVHWe1XULUV2Aqwfv36mp+fX3amhYUFVrJcm0x7Hd92yZVcvH3/n8c7zpkffZgh6VXHaa8fTE8dh7EO2iAkSRq3twCvA47sKut3tPchBnFUd1QOdlSn11HJcep3pLRbv/ospy6DeI5BHNXt93qDfo62HWFvU942ZYX25Z0USR5OpzHokqr6QFN8b5I1zfZiDbC7Kd8JnNi1+AnAPaNLK0mTzQYhSdLYJHk+sLuqbkgyv9zlB3FUd1QOdlTnvAnrVbNp3d6eR9m69Tvitpy6DOI5+mVdzhHBfq836Odo2xH2NuVtU1ZoX95JkE5XoHcCt1bVm7smXQWcC2xpbq/sKr80yZuB44CTgE+MLrEkTTYbhCRJ4/Qs4AVJngc8EnhMkvfQ/2ivJGl2PQt4ObA9yY1N2evpNARdnuR84C7gxQBVdXOSy4Fb6Fyh7JVV9eDoY0vSZLJBSJI0NlV1IXAhQNND6Heq6mVJ/pDeR3slSTOqqv6W3uMCAZzeZ5mL6IxRJ0la5GHjDiBJUg9bgDOS3A6c0TyWJEmSNCD2EJIkTYSqWqBzNTGq6uv0OdorLVWvq53t2HLWGJJIkiRNnlU1CCXZAdwPPAjsrar1SY4G3gesBXYAL6mq+1YXU5IkSZIkSYMyiFPGfq6qTq2q9c3jzcC1VXUScG3zWJIkSZIkSRNiGKeMbQDmm/vb6HT/v2AIryNJksao1ylZkiRJaofVNggV8OEkBfxxVW0F5qpqF0BzueBjey2YZCOwEWBubo49e/awsLCwyjiDs2nd3p7lc4f3nzZJVppz1O/BpL3vB9KWrOYcrLbklLQ0NmJJkiR1rLZB6FlVdU/T6HNNks8vdcGm8WgrwPr16+uII45gfn5+lXEG57w+O4yb1u3l4u2TPxb3SnPuOGd+8GEOYGFhYaLe9wNpS1ZzDlZbckqSJEnScqyqZaOq7mludye5AjgNuDfJmqZ30Bpg9wBySpLUOt29UTat2/sPBxu80pUkSZLGbcUNQkkeDTysqu5v7v8C8PvAVcC5wJbm9spBBB0Wu45LkiRJkqRZs5oeQnPAFUn2Pc+lVXV1kk8Clyc5H7gLePHqY0qSJEmSJGlQVtwgVFVfBJ7Wo/zrwOmrCSVJkiRJkqThmfzRkSVJ0tTy1G1JkqTxsEFID9Fvx9wBUCVJai+375IkaTEbhCRJkiZcrwYdG3MkSdJq2CAkSdKIeZqUJC1fkncBzwd2V9VTm7KjgfcBa4EdwEuq6r5m2oXA+cCDwG9V1f8YQ2xJmlg2CEmSxibJicCfA08Avg9sraq3HmgHXxo3G/SksXk38HY62419NgPXVtWWJJubxxckOQU4G3gKcBzw10meXFUPjjiz/v/27j9YrrK+4/j7YxIUghblR4wkGHQyGVqpkMmksczQtJQ2oibaykwcSkOLk9ohrbTYijJj7XSYwbbaH06rkxLadBpgUn6UjD8o9EfqtDaIYjAJEYkQ4YaYWGhBpFO98u0f57mwudlz7+695+xzzu7nNbNzd8/++jznPrvPs895zjlm1lgvyx3AzMxG2jhwTUScA6wCrkqd+IkO/lLgn9Nts1Zacu1nX7zsOfSMB5TMZigivgA8PWnxOmBrur4VeGfH8lsj4v8i4jHgALByIEHNzFrCM4TMzCybiDgMHE7XvytpP3AmRUd+dXrYVmAn8MEMEc0ayweKNgNgQWpLiIjDks5Iy88EdnU8biwtMzOzxANCZmbWCJKWAOcD91HewZ/8nI3ARoAFCxawc+fOgWTt1TXnjr94fcGJx95uujblrTNrtzo1m/eayNpvXe3nPfvJPF2O5557rnGfqzJtygrty9tC6rIsuj6wgrZkFP6fw17GsrZkmMrcrYzDXj4YnjLW8Rn0gJDNmLdMmllVJJ0M3A5cHRHPSt368ceLiM3AZoAVK1bE6tWra8s4E1d0fE9ec+44H9/Tnma3TXnrzHrwstXHLbtiFrt8TWTt9rpT6ec9+8k8XY6dO3fStM9VmTZlhfblbbAjkhamjQcLgaNp+RiwuONxi4Anu71AFW3JKPw/h72Mn9x2V9e2pN/v6ybrVsZhLx8MTxnr+Az6GEJmZpaVpHkUg0HbIuKOtPhI6tgzqYNvZmbWaQewIV3fANzVsXy9pJdLOhtYCnwpQz4zs8bygJCZmWWjYirQFmB/RHyi466yDr6ZmY0oSbcA/wkskzQm6UrgBuBiSY8AF6fbRMQ+YDvwEHA3cJXPMGZmdqx2zAW37HxGFDOryQXA5cAeSbvTsg9TdOi3p87+48ClmfKZmVlDRMR7Su66qOTx1wPX15fIzKzdPCBkZmbZRMS/0/3An1DSwTczMzMzs9nzgJCZmZlZCc+QNTMzs2HlYwiZmZmZmZmZmY0YzxAyMzMzG7CymUcHb3jbgJOYmZnZqPKAkJmZmVlDeBc1MzMzGxQPCJmZmZmZmdmU9hx6hismDVp7VqNZu43UgJC3upmZmZmZmZmZ+aDSZmZmZmZmZmYjZ6RmCJmZmZnZS3xwazMzs9HlGUJmZmZmZmZmZiPGM4RsYLpthfybNfMzJDEzM7Nh4wPempmZ9ccDQla5Kg7e3e013KkzMzObXpXt8DXnjr84yFJFO+xd1MzMzJpjKAeEfDYxK+OOqJmZmZmZmdmQDghZe3Sb3m1mZmZmZmZm9fKAkLVGvzO/POvHzHrhmYNmvfEMbDMzs8EY1PF3axsQkrQG+DNgDnBjRNxQ13uZddOUjquPh2Q2c25LzMxstgbRlpTNenefz8yarJYBIUlzgL8ALgbGgPsl7YiIh+p4P7PZ6meGQFMGmuo07INY/fwPfSa8fJralozCd4CZ2bBoaltiZtYEdc0QWgkciIhHASTdCqwDKv3idafc6tZZxzrPtNI2/qxYSw2kLTEzq9Kwb1RpIbclZmYlFBHVv6j0bmBNRLw33b4c+ImI2NTxmI3AxnRzGfAU8F+Vh6neaThnldqSE9qT1TmrNdOcr4+I06sOM0pm2JY8PPCgvWtLnZ/QprzOWp825W1TVugtr9uSWRpgW9K2+jcTw17GYS8fDH8ZXb7uStuSumYIqcuyY0aeImIzsPnFJ0hfjogVNeWpjHNWqy05oT1ZnbNabck5pPpuS5qsbXWpTXmdtT5tytumrNC+vC02kLZkFP6fw17GYS8fDH8ZXb7+vazKF+swBizuuL0IeLKm9zIzs+HktsTMzGbLbYmZWYm6BoTuB5ZKOlvSCcB6YEdN72VmZsPJbYmZmc2W2xIzsxK17DIWEeOSNgH/SHF6x5siYt80T2vFlH+cs2ptyQntyeqc1WpLzqEzw7akydpWl9qU11nr06a8bcoK7cvbSgNsS0bh/znsZRz28sHwl9Hl61MtB5U2MzMzMzMzM7PmqmuXMTMzMzMzMzMzaygPCJmZmZmZmZmZjZjaB4QkrZH0sKQDkq7tcv9lkr6WLl+U9OaO+w5K2iNpt6QvNyDraknPpDy7JX2k1+cOOOfvdGTcK+mHkl6T7hvIOpV0k6SjkvaW3C9Jf57K8DVJy3stX4asjaijPeRsSv2cLmf2+pnea7Gkf5W0X9I+Se/v8pjG1FNrN0nvT/V9n6Src+eZrNvnVtJrJN0r6ZH099U5M04oyXppWrcvSGrM6WZLsv6RpK+n75Q7JZ2SM2Onkrx/kLLulnSPpNflzDhhqrZG0gckhaTTcmSbrGS9flTSoY728JKcGW3mJL1C0pckPZi+h34/d6Y6SJoj6auSPpM7Sx0G2QfNQdIpkm5L7c9+SW/JnakqkpZ1fJfulvRsE/tasyXpt9J3zF5Jt0h6RSUvHBG1XSgO3PZN4A3ACcCDwI9OesxPAq9O198K3Ndx30HgtDoz9pl1NfCZmTx3kDknPf4dwL8Mep0CFwLLgb0l918CfB4QsGri/z7IddlH1qbU0elyZq+fveRsQv1M77UQWJ6uvxL4RpfPfGPqqS/tvQBvAvYCJ1GczOGfgKW5c03KeNznFvhD4Np0/VrgY7lzTpH1HGAZsBNYkTvjNFl/Dpibrn+sKet1iryv6rj+m8Cnc+csy5qWL6Y4ePC3BtWezHC9fhT4QO5svlTy/xVwcro+D7gPWJU7Vw3l/G3g5m59zWG4DLIPmql8W4H3pusnAKfkzlRTOecA3wZenztLxeU6E3gMODHd3g5cUcVr1z1DaCVwICIejYjvA7cC6zofEBFfjIj/Tjd3AYtqzlRm2qw1PbfunO8BbqkpS6mI+ALw9BQPWQf8bRR2AadIWshg12VPWZtSR3tYp2UGuk77zJmlfgJExOGIeCBd/y6wn+LLtlNj6qm12jnAroh4PiLGgX8D3pU50zFKPrfrKDqQpL/vHGioEt2yRsT+iHg4U6RSJVnvSfUA8vZ7jlOS99mOm/OBRpyNZIq25k+A36UhOWFW7be1QOojPJduzkuXxtS/KkhaBLwNuDF3FuufpFdRDExvAYiI70fE/+RNVZuLgG9GxLdyB6nBXOBESXMpNjI+WcWL1j0gdCbwRMftMY7/wdXpSoqt8RMCuEfSVyRtrCFfp16zviVNCf28pB/r87lV6Pm9JJ0ErAFu71g8yHU6lbJyDHJdzkTOOtqL3PWzZ02qn5KWAOdTbNXr1NZ6as2yF7hQ0qmp3l9CMYuh6RZExGEoBlCBMzLnGUa/yrFtSiNJul7SE8BlwEeme3wuktYChyLiwdxZerQp7Y53U1N2ybSZSbtT7QaOAvdGxOT+RNv9KcVA6wu5g9SoaX36Kr0B+A7w12m3vxslzc8dqibrybSxuU4RcQj4Y+Bx4DDwTETcU8Vr1z0gpC7Luo6YS/ppih/bH+xYfEFELKfYTecqSRdWH/GlCF2WTc76AMX0szcDnwT+oY/nVqWf93oH8B8R0blVapDrdCpl5RjkuuxLA+rodJpQP/vRiPop6WSKQamrJ20JhxbWU2ueiNhPsWvQvcDdFLsYjk/5JBt6kq6jqAfbcmeZTkRcFxGLKbJuyp2nmzTYeh0NHrCa5FPAG4HzKDr3H88bx2YjIn4YEedRzPhbKelNuTNVRdLbgaMR8ZXcWWrWpD591eZS7Lb6qYg4H/gexa7gQ0XSCcBa4O9zZ6la2miwDjgbeB0wX9IvVfHadQ8IjXHsVtBFdJnaJOnHKaYgrouIpyaWR8ST6e9R4E6K3TSyZY2IZyemhEbE54B56YCFPZVzUDk7HDdCOuB1OpWycgxyXfasIXV0Sg2pn/3IXj8lzaMYDNoWEXd0eUir6qk1V0RsiYjlEXEhxa4jj+TO1IMjaRdJ0t+jmfMMDUkbgLcDl0U6GEBL3Az8Yu4QJd5I0VF+UNJBiu/lByS9NmuqEhFxJA0ivAD8FRn7D1adtBvOTooZ0MPiAmBt+lzdCvyMpL/LG6l6TerT12AMGOuYuXYbxQDRsHkr8EBEHMkdpAY/CzwWEd+JiB8Ad1Ac53bW6h4Quh9YKunsNGK3HtjR+QBJZ1EU6PKI+EbH8vmSXjlxneIgjF3PWjTArK+VpHR9JcX6e6qX5w4yZ8r3I8BPAXd1LBv0Op3KDuCXVVhFMe3tMINdlz1pUB2dUkPqZ0+aUD/TutoC7I+IT5Q8rDX11JpN0hnp71nAL9CO6cw7gA3p+gY6Pq82c5LWUMw0XRsRz+fOMx1JSzturgW+nivLVCJiT0ScERFLImIJxQ+g5RHx7czRupoYbE3eRcb+g82OpNOVzhYo6USKH26N/JzMRER8KCIWpc/VeoqTgVQyM6Epmtanr1r6HnxC0rK06CLgoYyR6pLt2KQD8DiwStJJ6TfMRRTHP521uVW8SJmIGJe0ieJsD3OAmyJin6T3pfs/TTG191TgL9Nv2fGIWAEsAO5My+YCN0fE3Zmzvhv4dUnjwP8C69OWva7PzZgTis7FPRHxvY6nD2ydSrqF4qxXp0kaA36P4iB7Exk/R3EcjQPA88CvTFW+OjL2kbURdbSHnNnrZ485IXP9TC4ALgf2qNjvH+DDwFkdWRtTT631bpd0KvAD4Kp46UD1jVDyub0B2C7pSoqOyKX5Er6kJOvTFLvKng58VtLuiPj5fCkLJVk/BLwcuDd93+2KiPdlC9mhJO8l6UfECxRn7mps1ojYkjdVdyXrdbWk8yh2Nz4I/Fq2gDZbC4GtkuZQbIzbHhFDeWr2ITboPmgOvwFsSxsyHyX1aYeFit2GL2ZIv0sj4j5Jt1EcImQc+CqwuYrXVrtmKpuZmZmZmZmZ2WzVvcuYmZmZmZmZmZk1jAeEzMzMzMzMzMxGjAeEzMzMzMzMzMxGjAeEzMzMzMzMzMxGjAeEzMzMzMzMzMxGjAeEzMzMzMzMzMxGjAeEzMzMzMzMzMxGzP8DvdHnPbwU9OEAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 1440x1080 with 12 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"df.hist(bins=50, figsize=(20,15))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.025747,
"end_time": "2020-11-19T11:26:26.990651",
"exception": false,
"start_time": "2020-11-19T11:26:26.964904",
"status": "completed"
},
"tags": []
},
"source": [
"<h2>Buscando correlação</h2>"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:26:27.051307Z",
"iopub.status.busy": "2020-11-19T11:26:27.048805Z",
"iopub.status.idle": "2020-11-19T11:26:27.055637Z",
"shell.execute_reply": "2020-11-19T11:26:27.054926Z"
},
"papermill": {
"duration": 0.037966,
"end_time": "2020-11-19T11:26:27.055763",
"exception": false,
"start_time": "2020-11-19T11:26:27.017797",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"corr_matrix = df.corr()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:26:27.116190Z",
"iopub.status.busy": "2020-11-19T11:26:27.115137Z",
"iopub.status.idle": "2020-11-19T11:26:27.120208Z",
"shell.execute_reply": "2020-11-19T11:26:27.119616Z"
},
"papermill": {
"duration": 0.039049,
"end_time": "2020-11-19T11:26:27.120334",
"exception": false,
"start_time": "2020-11-19T11:26:27.081285",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"quality 1.000000\n",
"alcohol 0.476166\n",
"sulphates 0.251397\n",
"citric acid 0.226373\n",
"fixed acidity 0.124052\n",
"residual sugar 0.013732\n",
"free sulfur dioxide -0.050656\n",
"pH -0.057731\n",
"chlorides -0.128907\n",
"density -0.174919\n",
"total sulfur dioxide -0.185100\n",
"volatile acidity -0.390558\n",
"Name: quality, dtype: float64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"corr_matrix[\"quality\"].sort_values(ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:26:27.181393Z",
"iopub.status.busy": "2020-11-19T11:26:27.180529Z",
"iopub.status.idle": "2020-11-19T11:26:27.185846Z",
"shell.execute_reply": "2020-11-19T11:26:27.185133Z"
},
"papermill": {
"duration": 0.039503,
"end_time": "2020-11-19T11:26:27.185974",
"exception": false,
"start_time": "2020-11-19T11:26:27.146471",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"citric acid 1.000000\n",
"fixed acidity 0.671703\n",
"density 0.364947\n",
"sulphates 0.312770\n",
"quality 0.226373\n",
"chlorides 0.203823\n",
"residual sugar 0.143577\n",
"alcohol 0.109903\n",
"total sulfur dioxide 0.035533\n",
"free sulfur dioxide -0.060978\n",
"pH -0.541904\n",
"volatile acidity -0.552496\n",
"Name: citric acid, dtype: float64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"corr_matrix[\"citric acid\"].sort_values(ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:26:27.388890Z",
"iopub.status.busy": "2020-11-19T11:26:27.384176Z",
"iopub.status.idle": "2020-11-19T11:26:29.342178Z",
"shell.execute_reply": "2020-11-19T11:26:29.342790Z"
},
"papermill": {
"duration": 2.130264,
"end_time": "2020-11-19T11:26:29.342954",
"exception": false,
"start_time": "2020-11-19T11:26:27.212690",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x576 with 25 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"attributes = [\"alcohol\",\"sulphates\",\"citric acid\",\"fixed acidity\",\"volatile acidity\"]\n",
"\n",
"pd.plotting.scatter_matrix(df[attributes], figsize=(10,8)), \n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.03224,
"end_time": "2020-11-19T11:26:29.408542",
"exception": false,
"start_time": "2020-11-19T11:26:29.376302",
"status": "completed"
},
"tags": []
},
"source": [
"<h3> Verificando a existência de algum dado nulo </h3>"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:26:29.483329Z",
"iopub.status.busy": "2020-11-19T11:26:29.482137Z",
"iopub.status.idle": "2020-11-19T11:26:29.487073Z",
"shell.execute_reply": "2020-11-19T11:26:29.486360Z"
},
"papermill": {
"duration": 0.045451,
"end_time": "2020-11-19T11:26:29.487210",
"exception": false,
"start_time": "2020-11-19T11:26:29.441759",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"fixed acidity 0\n",
"volatile acidity 0\n",
"citric acid 0\n",
"residual sugar 0\n",
"chlorides 0\n",
"free sulfur dioxide 0\n",
"total sulfur dioxide 0\n",
"density 0\n",
"pH 0\n",
"sulphates 0\n",
"alcohol 0\n",
"quality 0\n",
"dtype: int64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
"_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
"execution": {
"iopub.execute_input": "2020-11-19T11:26:29.562644Z",
"iopub.status.busy": "2020-11-19T11:26:29.561796Z",
"iopub.status.idle": "2020-11-19T11:26:29.566961Z",
"shell.execute_reply": "2020-11-19T11:26:29.566366Z"
},
"papermill": {
"duration": 0.046386,
"end_time": "2020-11-19T11:26:29.567130",
"exception": false,
"start_time": "2020-11-19T11:26:29.520744",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"5 681\n",
"6 638\n",
"7 199\n",
"4 53\n",
"8 18\n",
"3 10\n",
"Name: quality, dtype: int64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.quality.value_counts().sort_values(ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:26:29.641705Z",
"iopub.status.busy": "2020-11-19T11:26:29.640677Z",
"iopub.status.idle": "2020-11-19T11:26:29.646380Z",
"shell.execute_reply": "2020-11-19T11:26:29.646983Z"
},
"papermill": {
"duration": 0.046401,
"end_time": "2020-11-19T11:26:29.647174",
"exception": false,
"start_time": "2020-11-19T11:26:29.600773",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"bins = (2, 6.5, 8)\n",
"group_names = [0, 1] # 0 Is bad, and 1 is god\n",
"df['quality_encode'] = pd.cut(df['quality'], bins = bins, labels = group_names)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:26:29.723063Z",
"iopub.status.busy": "2020-11-19T11:26:29.721903Z",
"iopub.status.idle": "2020-11-19T11:26:29.725413Z",
"shell.execute_reply": "2020-11-19T11:26:29.724630Z"
},
"papermill": {
"duration": 0.043382,
"end_time": "2020-11-19T11:26:29.725554",
"exception": false,
"start_time": "2020-11-19T11:26:29.682172",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"attributes = [\"alcohol\",\"sulphates\",\"citric acid\",\"fixed acidity\",\"volatile acidity\",\"quality\"]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:26:29.803472Z",
"iopub.status.busy": "2020-11-19T11:26:29.802599Z",
"iopub.status.idle": "2020-11-19T11:26:29.806080Z",
"shell.execute_reply": "2020-11-19T11:26:29.805485Z"
},
"papermill": {
"duration": 0.046084,
"end_time": "2020-11-19T11:26:29.806210",
"exception": false,
"start_time": "2020-11-19T11:26:29.760126",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"df_w = df[attributes]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:26:29.891666Z",
"iopub.status.busy": "2020-11-19T11:26:29.890819Z",
"iopub.status.idle": "2020-11-19T11:26:30.057898Z",
"shell.execute_reply": "2020-11-19T11:26:30.057279Z"
},
"papermill": {
"duration": 0.217061,
"end_time": "2020-11-19T11:26:30.058082",
"exception": false,
"start_time": "2020-11-19T11:26:29.841021",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fc4ce8bad50>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1296x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(18,6))\n",
"df_w.quality.value_counts().sort_values(ascending=False).plot.bar()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:26:30.136258Z",
"iopub.status.busy": "2020-11-19T11:26:30.135460Z",
"iopub.status.idle": "2020-11-19T11:26:30.138895Z",
"shell.execute_reply": "2020-11-19T11:26:30.138303Z"
},
"papermill": {
"duration": 0.045043,
"end_time": "2020-11-19T11:26:30.139024",
"exception": false,
"start_time": "2020-11-19T11:26:30.093981",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"X_plt = df_w[\"citric acid\"].values\n",
"y_plt = df_w[\"fixed acidity\"].values"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:26:30.216937Z",
"iopub.status.busy": "2020-11-19T11:26:30.216116Z",
"iopub.status.idle": "2020-11-19T11:26:30.219754Z",
"shell.execute_reply": "2020-11-19T11:26:30.219073Z"
},
"papermill": {
"duration": 0.044906,
"end_time": "2020-11-19T11:26:30.219883",
"exception": false,
"start_time": "2020-11-19T11:26:30.174977",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"X = df_w[\"citric acid\"].values\n",
"y = df_w[\"fixed acidity\"].values"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:26:30.298185Z",
"iopub.status.busy": "2020-11-19T11:26:30.297355Z",
"iopub.status.idle": "2020-11-19T11:26:30.301186Z",
"shell.execute_reply": "2020-11-19T11:26:30.300466Z"
},
"papermill": {
"duration": 0.045428,
"end_time": "2020-11-19T11:26:30.301315",
"exception": false,
"start_time": "2020-11-19T11:26:30.255887",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"X = np.c_[np.ones((1599,1)), X]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:26:30.380210Z",
"iopub.status.busy": "2020-11-19T11:26:30.379226Z",
"iopub.status.idle": "2020-11-19T11:26:30.388638Z",
"shell.execute_reply": "2020-11-19T11:26:30.387725Z"
},
"papermill": {
"duration": 0.051049,
"end_time": "2020-11-19T11:26:30.388796",
"exception": false,
"start_time": "2020-11-19T11:26:30.337747",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"array([6.69281886, 6.00356031])"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"theta_best = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y)\n",
"theta_best"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:26:30.475177Z",
"iopub.status.busy": "2020-11-19T11:26:30.474031Z",
"iopub.status.idle": "2020-11-19T11:26:30.479173Z",
"shell.execute_reply": "2020-11-19T11:26:30.478561Z"
},
"papermill": {
"duration": 0.049956,
"end_time": "2020-11-19T11:26:30.479316",
"exception": false,
"start_time": "2020-11-19T11:26:30.429360",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 6.69281886, 12.69637917])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_new = np.array([[0],[1]])\n",
"X_new_b = np.c_[np.ones((2,1)),X_new]\n",
"y_predict = X_new_b.dot(theta_best)\n",
"y_predict"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:26:30.572149Z",
"iopub.status.busy": "2020-11-19T11:26:30.571145Z",
"iopub.status.idle": "2020-11-19T11:26:30.726960Z",
"shell.execute_reply": "2020-11-19T11:26:30.727854Z"
},
"papermill": {
"duration": 0.210894,
"end_time": "2020-11-19T11:26:30.728097",
"exception": false,
"start_time": "2020-11-19T11:26:30.517203",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<module 'matplotlib.pyplot' from '/opt/conda/lib/python3.7/site-packages/matplotlib/pyplot.py'>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.plot(X_new, y_predict, \"r-\")\n",
"plt.plot(X_plt,y_plt, \"b.\")\n",
"plt.show()\n",
"plt"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:26:30.823524Z",
"iopub.status.busy": "2020-11-19T11:26:30.822533Z",
"iopub.status.idle": "2020-11-19T11:26:30.826349Z",
"shell.execute_reply": "2020-11-19T11:26:30.825549Z"
},
"papermill": {
"duration": 0.055968,
"end_time": "2020-11-19T11:26:30.826486",
"exception": false,
"start_time": "2020-11-19T11:26:30.770518",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"#Splitting y and X\n",
"y = df['quality']\n",
"X = df[['alcohol','density']]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:26:30.913075Z",
"iopub.status.busy": "2020-11-19T11:26:30.912193Z",
"iopub.status.idle": "2020-11-19T11:26:32.020763Z",
"shell.execute_reply": "2020-11-19T11:26:32.019958Z"
},
"papermill": {
"duration": 1.154366,
"end_time": "2020-11-19T11:26:32.020902",
"exception": false,
"start_time": "2020-11-19T11:26:30.866536",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"#Splitting into test and train data\n",
"from sklearn.model_selection import train_test_split, GridSearchCV\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:26:32.107782Z",
"iopub.status.busy": "2020-11-19T11:26:32.106951Z",
"iopub.status.idle": "2020-11-19T11:26:32.750035Z",
"shell.execute_reply": "2020-11-19T11:26:32.749300Z"
},
"papermill": {
"duration": 0.689231,
"end_time": "2020-11-19T11:26:32.750195",
"exception": false,
"start_time": "2020-11-19T11:26:32.060964",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegression(max_iter=10000)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"log_reg = LogisticRegression(max_iter=10000)\n",
"log_reg.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:26:32.844962Z",
"iopub.status.busy": "2020-11-19T11:26:32.843870Z",
"iopub.status.idle": "2020-11-19T11:26:32.848147Z",
"shell.execute_reply": "2020-11-19T11:26:32.848777Z"
},
"papermill": {
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"end_time": "2020-11-19T11:26:32.848949",
"exception": false,
"start_time": "2020-11-19T11:26:32.790100",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"y_proba = log_reg.predict_proba(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:26:32.938986Z",
"iopub.status.busy": "2020-11-19T11:26:32.937948Z",
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"start_time": "2020-11-19T11:26:32.890179",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"0.540625"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"log_reg.score(X_test, y_test)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
},
"papermill": {
"duration": 15.950381,
"end_time": "2020-11-19T11:26:34.015553",
"environment_variables": {},
"exception": null,
"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2020-11-19T11:26:18.065172",
"version": "2.1.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| 0047/234/47234235.ipynb | s3://data-agents/kaggle-outputs/sharded/010_00047.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.023115,
"end_time": "2020-11-19T11:31:32.765345",
"exception": false,
"start_time": "2020-11-19T11:31:32.742230",
"status": "completed"
},
"tags": []
},
"source": [
"## [Real or Fake] : Fake Job Description Prediction\n",
"\n",
"\n",
"Этот набор данных содержит 18 тысяч описаний должностей, из которых около 800 - поддельные. Данные состоят из текстовой информации и метаинформации о вакансиях."
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.018949,
"end_time": "2020-11-19T11:31:32.803971",
"exception": false,
"start_time": "2020-11-19T11:31:32.785022",
"status": "completed"
},
"tags": []
},
"source": [
"Задача - обучить модель классифицировать вакансии как реальные или мошеннические."
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.018886,
"end_time": "2020-11-19T11:31:32.842213",
"exception": false,
"start_time": "2020-11-19T11:31:32.823327",
"status": "completed"
},
"tags": []
},
"source": [
"### О столбцах"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.019322,
"end_time": "2020-11-19T11:31:32.880958",
"exception": false,
"start_time": "2020-11-19T11:31:32.861636",
"status": "completed"
},
"tags": []
},
"source": [
"1. job_id - уникальный идентификатор вакансии;\n",
"2. title - заголовок объявления;\n",
"3. location - географическое расположение объявления о работе;\n",
"4. department - корпоративный отдел (например, продажи);\n",
"5. salary_range - ориентировочный диапазон заработной платы (например, 50 000-60 000);\n",
"6. company_profile - краткое описание компании;\n",
"7. description - подробное описание объявления о работе;\n",
"8. requirements - перечислены требования для вакансии;\n",
"9. benefits - перечислены предлагаемые льготы;\n",
"10. telecommuting - верно для удаленных должностей;\n",
"11. has_company_logo - верно, если присутствует логотип компании;\n",
"12. has_questions - верно, если присутствуют проверочные вопросы;\n",
"13. employment_type - вид занятости;\n",
"14. required_experience - необходимый опыт;\n",
"15. required_education - необходимый образование;\n",
"16. industry - индустрия;\n",
"17. function - выполняемая функция;\n",
"18. fraudulent - показывает мошенническая ли вакансия."
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.01904,
"end_time": "2020-11-19T11:31:32.919570",
"exception": false,
"start_time": "2020-11-19T11:31:32.900530",
"status": "completed"
},
"tags": []
},
"source": [
"### Импорт библиотек"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"execution": {
"iopub.execute_input": "2020-11-19T11:31:32.973442Z",
"iopub.status.busy": "2020-11-19T11:31:32.972617Z",
"iopub.status.idle": "2020-11-19T11:31:32.982653Z",
"shell.execute_reply": "2020-11-19T11:31:32.981593Z"
},
"papermill": {
"duration": 0.043085,
"end_time": "2020-11-19T11:31:32.982869",
"exception": false,
"start_time": "2020-11-19T11:31:32.939784",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/kaggle/input/real-or-fake-fake-jobposting-prediction/fake_job_postings.csv\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load\n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the read-only \"../input/\" directory\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
"\n",
"import os\n",
"for dirname, _, filenames in os.walk('/kaggle/input'):\n",
" for filename in filenames:\n",
" print(os.path.join(dirname, filename))\n",
"\n",
"# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
"# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:31:33.037717Z",
"iopub.status.busy": "2020-11-19T11:31:33.036853Z",
"iopub.status.idle": "2020-11-19T11:31:34.143424Z",
"shell.execute_reply": "2020-11-19T11:31:34.142540Z"
},
"papermill": {
"duration": 1.134165,
"end_time": "2020-11-19T11:31:34.143556",
"exception": false,
"start_time": "2020-11-19T11:31:33.009391",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"from functools import reduce\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.029382,
"end_time": "2020-11-19T11:31:34.193634",
"exception": false,
"start_time": "2020-11-19T11:31:34.164252",
"status": "completed"
},
"tags": []
},
"source": [
"### Загрузка датасета и его анализ"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
"_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
"execution": {
"iopub.execute_input": "2020-11-19T11:31:34.243521Z",
"iopub.status.busy": "2020-11-19T11:31:34.242138Z",
"iopub.status.idle": "2020-11-19T11:31:35.008472Z",
"shell.execute_reply": "2020-11-19T11:31:35.009128Z"
},
"papermill": {
"duration": 0.794435,
"end_time": "2020-11-19T11:31:35.009297",
"exception": false,
"start_time": "2020-11-19T11:31:34.214862",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>job_id</th>\n",
" <th>title</th>\n",
" <th>location</th>\n",
" <th>department</th>\n",
" <th>salary_range</th>\n",
" <th>company_profile</th>\n",
" <th>description</th>\n",
" <th>requirements</th>\n",
" <th>benefits</th>\n",
" <th>telecommuting</th>\n",
" <th>has_company_logo</th>\n",
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" <th>employment_type</th>\n",
" <th>required_experience</th>\n",
" <th>required_education</th>\n",
" <th>industry</th>\n",
" <th>function</th>\n",
" <th>fraudulent</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Marketing Intern</td>\n",
" <td>US, NY, New York</td>\n",
" <td>Marketing</td>\n",
" <td>NaN</td>\n",
" <td>We're Food52, and we've created a groundbreaki...</td>\n",
" <td>Food52, a fast-growing, James Beard Award-winn...</td>\n",
" <td>Experience with content management systems a m...</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Other</td>\n",
" <td>Internship</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Marketing</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>Customer Service - Cloud Video Production</td>\n",
" <td>NZ, , Auckland</td>\n",
" <td>Success</td>\n",
" <td>NaN</td>\n",
" <td>90 Seconds, the worlds Cloud Video Production ...</td>\n",
" <td>Organised - Focused - Vibrant - Awesome!Do you...</td>\n",
" <td>What we expect from you:Your key responsibilit...</td>\n",
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" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Full-time</td>\n",
" <td>Not Applicable</td>\n",
" <td>NaN</td>\n",
" <td>Marketing and Advertising</td>\n",
" <td>Customer Service</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>Commissioning Machinery Assistant (CMA)</td>\n",
" <td>US, IA, Wever</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Valor Services provides Workforce Solutions th...</td>\n",
" <td>Our client, located in Houston, is actively se...</td>\n",
" <td>Implement pre-commissioning and commissioning ...</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>Account Executive - Washington DC</td>\n",
" <td>US, DC, Washington</td>\n",
" <td>Sales</td>\n",
" <td>NaN</td>\n",
" <td>Our passion for improving quality of life thro...</td>\n",
" <td>THE COMPANY: ESRI – Environmental Systems Rese...</td>\n",
" <td>EDUCATION: Bachelor’s or Master’s in GIS, busi...</td>\n",
" <td>Our culture is anything but corporate—we have ...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Full-time</td>\n",
" <td>Mid-Senior level</td>\n",
" <td>Bachelor's Degree</td>\n",
" <td>Computer Software</td>\n",
" <td>Sales</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Bill Review Manager</td>\n",
" <td>US, FL, Fort Worth</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>SpotSource Solutions LLC is a Global Human Cap...</td>\n",
" <td>JOB TITLE: Itemization Review ManagerLOCATION:...</td>\n",
" <td>QUALIFICATIONS:RN license in the State of Texa...</td>\n",
" <td>Full Benefits Offered</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Full-time</td>\n",
" <td>Mid-Senior level</td>\n",
" <td>Bachelor's Degree</td>\n",
" <td>Hospital & Health Care</td>\n",
" <td>Health Care Provider</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" job_id title location \\\n",
"0 1 Marketing Intern US, NY, New York \n",
"1 2 Customer Service - Cloud Video Production NZ, , Auckland \n",
"2 3 Commissioning Machinery Assistant (CMA) US, IA, Wever \n",
"3 4 Account Executive - Washington DC US, DC, Washington \n",
"4 5 Bill Review Manager US, FL, Fort Worth \n",
"\n",
" department salary_range company_profile \\\n",
"0 Marketing NaN We're Food52, and we've created a groundbreaki... \n",
"1 Success NaN 90 Seconds, the worlds Cloud Video Production ... \n",
"2 NaN NaN Valor Services provides Workforce Solutions th... \n",
"3 Sales NaN Our passion for improving quality of life thro... \n",
"4 NaN NaN SpotSource Solutions LLC is a Global Human Cap... \n",
"\n",
" description \\\n",
"0 Food52, a fast-growing, James Beard Award-winn... \n",
"1 Organised - Focused - Vibrant - Awesome!Do you... \n",
"2 Our client, located in Houston, is actively se... \n",
"3 THE COMPANY: ESRI – Environmental Systems Rese... \n",
"4 JOB TITLE: Itemization Review ManagerLOCATION:... \n",
"\n",
" requirements \\\n",
"0 Experience with content management systems a m... \n",
"1 What we expect from you:Your key responsibilit... \n",
"2 Implement pre-commissioning and commissioning ... \n",
"3 EDUCATION: Bachelor’s or Master’s in GIS, busi... \n",
"4 QUALIFICATIONS:RN license in the State of Texa... \n",
"\n",
" benefits telecommuting \\\n",
"0 NaN 0 \n",
"1 What you will get from usThrough being part of... 0 \n",
"2 NaN 0 \n",
"3 Our culture is anything but corporate—we have ... 0 \n",
"4 Full Benefits Offered 0 \n",
"\n",
" has_company_logo has_questions employment_type required_experience \\\n",
"0 1 0 Other Internship \n",
"1 1 0 Full-time Not Applicable \n",
"2 1 0 NaN NaN \n",
"3 1 0 Full-time Mid-Senior level \n",
"4 1 1 Full-time Mid-Senior level \n",
"\n",
" required_education industry function \\\n",
"0 NaN NaN Marketing \n",
"1 NaN Marketing and Advertising Customer Service \n",
"2 NaN NaN NaN \n",
"3 Bachelor's Degree Computer Software Sales \n",
"4 Bachelor's Degree Hospital & Health Care Health Care Provider \n",
"\n",
" fraudulent \n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('../input/real-or-fake-fake-jobposting-prediction/fake_job_postings.csv')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:31:35.062165Z",
"iopub.status.busy": "2020-11-19T11:31:35.061284Z",
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"shell.execute_reply": "2020-11-19T11:31:35.090633Z"
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"status": "completed"
},
"tags": []
},
"outputs": [
{
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" <th>fraudulent</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>17880.000000</td>\n",
" <td>17880.000000</td>\n",
" <td>17880.000000</td>\n",
" <td>17880.000000</td>\n",
" <td>17880.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>8940.500000</td>\n",
" <td>0.042897</td>\n",
" <td>0.795302</td>\n",
" <td>0.491723</td>\n",
" <td>0.048434</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>5161.655742</td>\n",
" <td>0.202631</td>\n",
" <td>0.403492</td>\n",
" <td>0.499945</td>\n",
" <td>0.214688</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>1.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>4470.750000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>8940.500000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>13410.250000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>max</th>\n",
" <td>17880.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" job_id telecommuting has_company_logo has_questions \\\n",
"count 17880.000000 17880.000000 17880.000000 17880.000000 \n",
"mean 8940.500000 0.042897 0.795302 0.491723 \n",
"std 5161.655742 0.202631 0.403492 0.499945 \n",
"min 1.000000 0.000000 0.000000 0.000000 \n",
"25% 4470.750000 0.000000 1.000000 0.000000 \n",
"50% 8940.500000 0.000000 1.000000 0.000000 \n",
"75% 13410.250000 0.000000 1.000000 1.000000 \n",
"max 17880.000000 1.000000 1.000000 1.000000 \n",
"\n",
" fraudulent \n",
"count 17880.000000 \n",
"mean 0.048434 \n",
"std 0.214688 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 0.000000 \n",
"75% 0.000000 \n",
"max 1.000000 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:31:35.166112Z",
"iopub.status.busy": "2020-11-19T11:31:35.164911Z",
"iopub.status.idle": "2020-11-19T11:31:35.186173Z",
"shell.execute_reply": "2020-11-19T11:31:35.185171Z"
},
"papermill": {
"duration": 0.073141,
"end_time": "2020-11-19T11:31:35.186365",
"exception": false,
"start_time": "2020-11-19T11:31:35.113224",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 17880 entries, 0 to 17879\n",
"Data columns (total 18 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 job_id 17880 non-null int64 \n",
" 1 title 17880 non-null object\n",
" 2 location 17534 non-null object\n",
" 3 department 6333 non-null object\n",
" 4 salary_range 2868 non-null object\n",
" 5 company_profile 14572 non-null object\n",
" 6 description 17879 non-null object\n",
" 7 requirements 15185 non-null object\n",
" 8 benefits 10670 non-null object\n",
" 9 telecommuting 17880 non-null int64 \n",
" 10 has_company_logo 17880 non-null int64 \n",
" 11 has_questions 17880 non-null int64 \n",
" 12 employment_type 14409 non-null object\n",
" 13 required_experience 10830 non-null object\n",
" 14 required_education 9775 non-null object\n",
" 15 industry 12977 non-null object\n",
" 16 function 11425 non-null object\n",
" 17 fraudulent 17880 non-null int64 \n",
"dtypes: int64(5), object(13)\n",
"memory usage: 2.5+ MB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.02504,
"end_time": "2020-11-19T11:31:35.242935",
"exception": false,
"start_time": "2020-11-19T11:31:35.217895",
"status": "completed"
},
"tags": []
},
"source": [
"#### Вычислим процент пустых значений"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:31:35.330442Z",
"iopub.status.busy": "2020-11-19T11:31:35.329585Z",
"iopub.status.idle": "2020-11-19T11:31:35.356541Z",
"shell.execute_reply": "2020-11-19T11:31:35.355727Z"
},
"papermill": {
"duration": 0.085266,
"end_time": "2020-11-19T11:31:35.356665",
"exception": false,
"start_time": "2020-11-19T11:31:35.271399",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"job_id 0.0\n",
"title 0.0\n",
"location 1.9\n",
"department 64.6\n",
"salary_range 84.0\n",
"company_profile 18.5\n",
"description 0.0\n",
"requirements 15.1\n",
"benefits 40.3\n",
"telecommuting 0.0\n",
"has_company_logo 0.0\n",
"has_questions 0.0\n",
"employment_type 19.4\n",
"required_experience 39.4\n",
"required_education 45.3\n",
"industry 27.4\n",
"function 36.1\n",
"fraudulent 0.0\n",
"dtype: float64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pr = round(df.isna().sum()/len(df['job_id'])*100,1)\n",
"pr"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.023023,
"end_time": "2020-11-19T11:31:35.403107",
"exception": false,
"start_time": "2020-11-19T11:31:35.380084",
"status": "completed"
},
"tags": []
},
"source": [
"#### Удалим столбцы с слишком большим количеством NaN, а также столбец job_id, так как этот столбец хранит только id вакансии."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:31:35.467161Z",
"iopub.status.busy": "2020-11-19T11:31:35.466283Z",
"iopub.status.idle": "2020-11-19T11:31:35.484077Z",
"shell.execute_reply": "2020-11-19T11:31:35.483358Z"
},
"papermill": {
"duration": 0.057742,
"end_time": "2020-11-19T11:31:35.484220",
"exception": false,
"start_time": "2020-11-19T11:31:35.426478",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"department\n",
"salary_range\n",
"benefits\n",
"required_experience\n",
"required_education\n",
"function\n"
]
}
],
"source": [
"indexes = list(pr.index)\n",
"for i in range(len(pr)):\n",
" if pr[i]>30:\n",
" print(indexes[i])\n",
" del df[indexes[i]]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:31:35.538371Z",
"iopub.status.busy": "2020-11-19T11:31:35.537398Z",
"iopub.status.idle": "2020-11-19T11:31:35.542362Z",
"shell.execute_reply": "2020-11-19T11:31:35.541655Z"
},
"papermill": {
"duration": 0.033961,
"end_time": "2020-11-19T11:31:35.542491",
"exception": false,
"start_time": "2020-11-19T11:31:35.508530",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"del df['job_id']\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.024703,
"end_time": "2020-11-19T11:31:35.591400",
"exception": false,
"start_time": "2020-11-19T11:31:35.566697",
"status": "completed"
},
"tags": []
},
"source": []
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:31:35.661263Z",
"iopub.status.busy": "2020-11-19T11:31:35.660430Z",
"iopub.status.idle": "2020-11-19T11:31:35.672799Z",
"shell.execute_reply": "2020-11-19T11:31:35.671608Z"
},
"papermill": {
"duration": 0.056731,
"end_time": "2020-11-19T11:31:35.673004",
"exception": false,
"start_time": "2020-11-19T11:31:35.616273",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 17880 entries, 0 to 17879\n",
"Data columns (total 11 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 title 17880 non-null object\n",
" 1 location 17534 non-null object\n",
" 2 company_profile 14572 non-null object\n",
" 3 description 17879 non-null object\n",
" 4 requirements 15185 non-null object\n",
" 5 telecommuting 17880 non-null int64 \n",
" 6 has_company_logo 17880 non-null int64 \n",
" 7 has_questions 17880 non-null int64 \n",
" 8 employment_type 14409 non-null object\n",
" 9 industry 12977 non-null object\n",
" 10 fraudulent 17880 non-null int64 \n",
"dtypes: int64(4), object(7)\n",
"memory usage: 1.5+ MB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.024172,
"end_time": "2020-11-19T11:31:35.722533",
"exception": false,
"start_time": "2020-11-19T11:31:35.698361",
"status": "completed"
},
"tags": []
},
"source": [
"#### Теперь проверим значения в числовых столбцах на адекватность (мы точно знаем из описания столбцов, что в них должны быть значения только 0 и 1)."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:31:35.805685Z",
"iopub.status.busy": "2020-11-19T11:31:35.800510Z",
"iopub.status.idle": "2020-11-19T11:31:35.830992Z",
"shell.execute_reply": "2020-11-19T11:31:35.830211Z"
},
"papermill": {
"duration": 0.083995,
"end_time": "2020-11-19T11:31:35.831121",
"exception": false,
"start_time": "2020-11-19T11:31:35.747126",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"telecommuting is normal\n",
"has_company_logo is normal\n",
"has_questions is normal\n",
"fraudulent is normal\n"
]
}
],
"source": [
"ind = ['telecommuting','has_company_logo','has_questions','fraudulent']\n",
"for i in ind:\n",
" b=0\n",
" for j in df[i]:\n",
" if j !=np.int(1) and j!=np.int(0):\n",
" b+=1\n",
" print(j)\n",
" if b==0:\n",
" print(i+' is normal')\n",
" \n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.025346,
"end_time": "2020-11-19T11:31:35.882930",
"exception": false,
"start_time": "2020-11-19T11:31:35.857584",
"status": "completed"
},
"tags": []
},
"source": [
"#### В числовых столбцах всё в порядке."
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.024904,
"end_time": "2020-11-19T11:31:35.933047",
"exception": false,
"start_time": "2020-11-19T11:31:35.908143",
"status": "completed"
},
"tags": []
},
"source": [
"#### Теперь избавимся от NaN в остальных столбцах"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:31:35.995950Z",
"iopub.status.busy": "2020-11-19T11:31:35.995137Z",
"iopub.status.idle": "2020-11-19T11:31:38.179111Z",
"shell.execute_reply": "2020-11-19T11:31:38.178139Z"
},
"papermill": {
"duration": 2.220313,
"end_time": "2020-11-19T11:31:38.179313",
"exception": false,
"start_time": "2020-11-19T11:31:35.959000",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"17880"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"columns = list(df.columns)\n",
"columnsStr = []\n",
"for i in columns:\n",
" if df[i].dtype == np.object:\n",
" columnsStr.append(i)\n",
"\n",
"df2 = df[columnsStr]\n",
"text = []\n",
"for i in range(len(df2[columnsStr[0]])):\n",
" aa = list(df2.iloc[i])\n",
" aa = [' ' if type(i) == np.float else i for i in aa ]\n",
" st = str(reduce(lambda x,y:x+y, aa))\n",
" text.append(st)\n",
"len(text)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:31:38.244169Z",
"iopub.status.busy": "2020-11-19T11:31:38.243162Z",
"iopub.status.idle": "2020-11-19T11:31:38.247077Z",
"shell.execute_reply": "2020-11-19T11:31:38.246239Z"
},
"papermill": {
"duration": 0.0409,
"end_time": "2020-11-19T11:31:38.247213",
"exception": false,
"start_time": "2020-11-19T11:31:38.206313",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"df['text'] = text"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:31:38.307916Z",
"iopub.status.busy": "2020-11-19T11:31:38.306975Z",
"iopub.status.idle": "2020-11-19T11:31:38.321738Z",
"shell.execute_reply": "2020-11-19T11:31:38.321076Z"
},
"papermill": {
"duration": 0.047975,
"end_time": "2020-11-19T11:31:38.321911",
"exception": false,
"start_time": "2020-11-19T11:31:38.273936",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"for i in columnsStr:\n",
" del df[i]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:31:38.388597Z",
"iopub.status.busy": "2020-11-19T11:31:38.387534Z",
"iopub.status.idle": "2020-11-19T11:31:38.393645Z",
"shell.execute_reply": "2020-11-19T11:31:38.392994Z"
},
"papermill": {
"duration": 0.045415,
"end_time": "2020-11-19T11:31:38.393815",
"exception": false,
"start_time": "2020-11-19T11:31:38.348400",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>telecommuting</th>\n",
" <th>has_company_logo</th>\n",
" <th>has_questions</th>\n",
" <th>fraudulent</th>\n",
" <th>text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Marketing InternUS, NY, New YorkWe're Food52, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Customer Service - Cloud Video ProductionNZ, ,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Commissioning Machinery Assistant (CMA)US, IA,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Account Executive - Washington DCUS, DC, Washi...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Bill Review ManagerUS, FL, Fort WorthSpotSourc...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" telecommuting has_company_logo has_questions fraudulent \\\n",
"0 0 1 0 0 \n",
"1 0 1 0 0 \n",
"2 0 1 0 0 \n",
"3 0 1 0 0 \n",
"4 0 1 1 0 \n",
"\n",
" text \n",
"0 Marketing InternUS, NY, New YorkWe're Food52, ... \n",
"1 Customer Service - Cloud Video ProductionNZ, ,... \n",
"2 Commissioning Machinery Assistant (CMA)US, IA,... \n",
"3 Account Executive - Washington DCUS, DC, Washi... \n",
"4 Bill Review ManagerUS, FL, Fort WorthSpotSourc... "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.026896,
"end_time": "2020-11-19T11:31:38.448376",
"exception": false,
"start_time": "2020-11-19T11:31:38.421480",
"status": "completed"
},
"tags": []
},
"source": [
"#### Я собрал все текстовые столбцы в один, причём заменяя значения NaN на пустые строки"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"papermill": {
"duration": 0.026711,
"end_time": "2020-11-19T11:31:38.502192",
"exception": false,
"start_time": "2020-11-19T11:31:38.475481",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
},
"papermill": {
"duration": 12.006199,
"end_time": "2020-11-19T11:31:39.580343",
"environment_variables": {},
"exception": null,
"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2020-11-19T11:31:27.574144",
"version": "2.1.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| 0047/234/47234510.ipynb | s3://data-agents/kaggle-outputs/sharded/010_00047.jsonl.gz |
{"cells":[{"metadata":{},"cell_type":"markdown","source":"# Estruturas de Dados em Python\n[https://docs.python.org/3/tutorial/datastructures.html](http://)\n\n\n\n\n#### Fonte: [https://towardsdatascience.com/data-structures-in-python-a-brief-introduction-b4135d7a9b7d](http://)","attachments":{"image.png":{"image/png":"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"}}},{"metadata":{},"cell_type":"markdown","source":"## Listas\n### Guardam valores mutáveis e de diferentes tipos \n### Possuem diversos métodos já implementados para alteração, filtro, etc."},{"metadata":{},"cell_type":"markdown","source":"### Vamos começar criando uma lista simples, com diversos strings que contém nomes de frutas."},{"metadata":{"trusted":true},"cell_type":"code","source":"fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']","execution_count":6,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### É possível contar o número de aparições de um determinado elemento dentro de uma lista utilizando o método *count()*. Métodos funcionam de forma semelhantes à funções (ou seja, são pequenos blocos de código que executam tarefas determinadas), e cada classe diferente possui métodos próprios (ex. listas e tuplas possuem métodos de contagem, *floats* não)."},{"metadata":{"trusted":true},"cell_type":"code","source":"fruits.count('apple')","execution_count":7,"outputs":[{"output_type":"execute_result","execution_count":7,"data":{"text/plain":"2"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Quando tentamos realizar a contagem de um item que não existe na lista, o retorno é 0."},{"metadata":{"trusted":true},"cell_type":"code","source":"fruits.count('tangerine')","execution_count":8,"outputs":[{"output_type":"execute_result","execution_count":8,"data":{"text/plain":"0"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Também é possível obter a primeira posição na qual um elemento aparece na lista. Atenção, como podemos ver, apenas a posição primeira aparição do elemento é reportada."},{"metadata":{"trusted":true},"cell_type":"code","source":"fruits.index('banana')","execution_count":9,"outputs":[{"output_type":"execute_result","execution_count":9,"data":{"text/plain":"3"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Porém, através da documentação podemos descobrir que podemos estabelecer um início diferente de 0 para a busca por elementos de uma lista, dessa forma, conseguimos ver a posição do segundo elemento \"banana\", ao começar a busca a partir do quinto elemento."},{"metadata":{"trusted":true},"cell_type":"code","source":"fruits.index('banana', 4) # Find next banana starting a position 4","execution_count":10,"outputs":[{"output_type":"execute_result","execution_count":10,"data":{"text/plain":"6"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### De forma semelhante, é extremamente fácil inverter a ordem dos elementos de uma lista."},{"metadata":{"trusted":true},"cell_type":"code","source":"fruits.reverse()\nfruits","execution_count":11,"outputs":[{"output_type":"execute_result","execution_count":11,"data":{"text/plain":"['banana', 'apple', 'kiwi', 'banana', 'pear', 'apple', 'orange']"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Bem como adicionar novos elementos ao fim da mesma."},{"metadata":{"trusted":true},"cell_type":"code","source":"fruits.append('grape')\nfruits","execution_count":12,"outputs":[{"output_type":"execute_result","execution_count":12,"data":{"text/plain":"['banana', 'apple', 'kiwi', 'banana', 'pear', 'apple', 'orange', 'grape']"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Caso necessário também existem métodos para a ordenação dos elementos da lista."},{"metadata":{"trusted":true},"cell_type":"code","source":"fruits.sort()\nfruits","execution_count":13,"outputs":[{"output_type":"execute_result","execution_count":13,"data":{"text/plain":"['apple', 'apple', 'banana', 'banana', 'grape', 'kiwi', 'orange', 'pear']"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Por fim, a remoção do último elementos de uma lista também é extremamente fácil, fazendo uso do método *pop()*. O método ira retornar o elemento removido da lista, e atualizará a lista sem o mesmo presente."},{"metadata":{"trusted":true},"cell_type":"code","source":"fruits.pop()","execution_count":14,"outputs":[{"output_type":"execute_result","execution_count":14,"data":{"text/plain":"'pear'"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"fruits","execution_count":15,"outputs":[{"output_type":"execute_result","execution_count":15,"data":{"text/plain":"['apple', 'apple', 'banana', 'banana', 'grape', 'kiwi', 'orange']"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Usando listas como pilhas:\n### Uma outra estrutura de dado não implementada de forma nativa na linguagem mas que pode ser interessante para os usuários são os chamados *stacks*, ou pilhas. No caso do Python uma série de estruturas de dados não nativas como árvores e filas podem ser implementadas e definidas pelo usuário caso necessário. Aqui vamos mostrar como o conceito de listas pode ser usado para alguns desses casos."},{"metadata":{},"cell_type":"markdown","source":"### Como vemos, assim como em uma pilha podemos seguir o conceito de *First-in Last- out* (*FILO*), ou seja o primeiro elemento da estrutura é o último a ser retirado, utilizando de uma lista e dos métodos já conhecidos, *append()* e *pop()*. Começamos definindo uma nova lista, e a partir podemos adicionar elementos ao fim da mesma, bem como sempre retirar o último elemento e recuperar essa informação através do método."},{"metadata":{},"cell_type":"markdown","source":"### Definindo e adicionando novos elementos:"},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"stack = [3, 4, 5]\nstack.append(6)\nstack.append(7)\nstack","execution_count":17,"outputs":[{"output_type":"execute_result","execution_count":17,"data":{"text/plain":"[3, 4, 5, 6, 7]"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":" ### Removendo o último elemento da pilha:"},{"metadata":{"trusted":true},"cell_type":"code","source":"stack.pop()","execution_count":19,"outputs":[{"output_type":"execute_result","execution_count":19,"data":{"text/plain":"7"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Visualizando a pilha:"},{"metadata":{"trusted":true},"cell_type":"code","source":"stack","execution_count":20,"outputs":[{"output_type":"execute_result","execution_count":20,"data":{"text/plain":"[3, 4, 5, 6]"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Podemos remover mais alguns elementos e ver como é o resultado final guardado em nossa variável:"},{"metadata":{"trusted":true},"cell_type":"code","source":"stack.pop()\nstack.pop()\nstack","execution_count":21,"outputs":[{"output_type":"execute_result","execution_count":21,"data":{"text/plain":"[3, 4]"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Usando listas como filas\n### As filas são estruturas semelhantes às pilhas, porém onde o princípio de trabalho é um pouco diferente e conhecido como *First-in, First-Out* (*FIFO*). Neste caso, como em uma fila comum, o primeiro elemento é o primeiro a ser retirado da estrutura.\n"},{"metadata":{},"cell_type":"markdown","source":"### De forma semelhante ao caso anterior, podemos fazer uso de parte de uma biblioteca do Python chamada *collections*, de onde usaremos apenas a função *queue* para implementar a estrutura de dados de filas com uma entrada a partir de uma lista de elementos."},{"metadata":{"trusted":true},"cell_type":"code","source":"from collections import deque\nqueue = deque([\"Eric\", \"John\", \"Michael\"])\nqueue.append(\"Terry\") # Terry arrives\nqueue.append(\"Graham\") # Graham arrives\nqueue ","execution_count":22,"outputs":[{"output_type":"execute_result","execution_count":22,"data":{"text/plain":"deque(['Eric', 'John', 'Michael', 'Terry', 'Graham'])"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Utilizando do método *popleft()* podemos realizar a remoção do primeiro item e obter o mesmo no retorno da chamada:"},{"metadata":{"trusted":true},"cell_type":"code","source":"queue.popleft() # The first to arrive now leaves ","execution_count":23,"outputs":[{"output_type":"execute_result","execution_count":23,"data":{"text/plain":"'Eric'"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Podemos remover mais um item, agora o \"novo\" primeiro da fila, e ver o resultado final guardado na variável:"},{"metadata":{"trusted":true},"cell_type":"code","source":"queue.popleft() # The second to arrive now leaves\nqueue ","execution_count":24,"outputs":[{"output_type":"execute_result","execution_count":24,"data":{"text/plain":"deque(['Michael', 'Terry', 'Graham'])"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## List comprehension\n\n### No Python é ainda possível fazer uso de uma espécie de \"atalho\", ou simplificação da chamada de loops quando estamos interessados em criar listas, tuplas, dicionários, etc. Este tipo de forma concisa de criação de listas é conhecida como *List Comprehension* e tem uso frequente quando desejamos criar uma nova lista que é resultado de operações sobre membros de outro tipo de sequência ou objetivo iterativo."},{"metadata":{},"cell_type":"markdown","source":"### Abaixo podemos ver uma forma tradicional de criar uma lista que guarda os valores ao quadrado de 0 a 9:"},{"metadata":{"trusted":true},"cell_type":"code","source":"squares = []\nfor x in range(10):\n squares.append(x**2)\n\nsquares","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Agora podemos ver como utilizando de *List Comprehensions* conseguimos reduzir a quantidade de código para apenas uma linha, em vez de 3. A sintaxe é importante e devemos reparar que começa com a abertura dos colchetes que demarcam uma lista, seguidos do valor a ser adicionado a cada nova iteração, e por fim o loop sobre a sequência ou iterador, seguidos do fechamento dos colchetes:"},{"metadata":{"trusted":true},"cell_type":"code","source":"squares = [x**2 for x in range(10)]\nsquares","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Tuplas\n### Semelhantes a listas, mas usadas em outros contextos\n### Acessadas normalmente por unpacking e não por iteração\n### Elementos imutáveis"},{"metadata":{},"cell_type":"markdown","source":"### É possível criar uma tupla diretamente ao associarmos valores separados por vírgula, sem nenhum tipo de limitador (como colchetes, usados em listas), a uma variável, como podemos ver abaixo:"},{"metadata":{"trusted":true},"cell_type":"code","source":"t = 12345, 54321, 'hello!'\nt","execution_count":26,"outputs":[{"output_type":"execute_result","execution_count":26,"data":{"text/plain":"(12345, 54321, 'hello!')"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Assim como nas listas podemos acessar os elementos de uma tupla através da posição dos mesmos, fazendo uso da seguinte sintaxe: variavel[posição]. Onde variável é o nome da variável que guarda a tupla, e posição é um número inteiro que vai de 0 a (1 - número de elementos da tupla).\n\n#### PS: Na realidade é possível utilizar também numéros inteiros negativos, onde a contagem começa a partir do último elemento ( ex. variável[-1] retornará o último elemento da tupla)"},{"metadata":{"trusted":true},"cell_type":"code","source":"t[0]","execution_count":27,"outputs":[{"output_type":"execute_result","execution_count":27,"data":{"text/plain":"12345"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Uma tupla, assim como uma lista, pode possuir mais de uma dimensão e ser composta por elementos heterogêneos, como strings, listas, etc."},{"metadata":{"trusted":true},"cell_type":"code","source":"u = t, (1, 2, 3, 4, 5)\nu","execution_count":28,"outputs":[{"output_type":"execute_result","execution_count":28,"data":{"text/plain":"((12345, 54321, 'hello!'), (1, 2, 3, 4, 5))"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Quando tentamos alterar o valor de uma posição na tupla, veremos que isso não é possível, e uma exceção será retornada:"},{"metadata":{"trusted":true},"cell_type":"code","source":"t[0] = 88888","execution_count":29,"outputs":[{"output_type":"error","ename":"TypeError","evalue":"'tuple' object does not support item assignment","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)","\u001b[0;32m<ipython-input-29-d739abe3b757>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m88888\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"]}]},{"metadata":{"trusted":true},"cell_type":"code","source":"v = ([1, 2, 3], [3, 2, 1])\nv","execution_count":30,"outputs":[{"output_type":"execute_result","execution_count":30,"data":{"text/plain":"([1, 2, 3], [3, 2, 1])"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### A criação de uma tupla vazia também pode ser feita, de forma bastante simples:"},{"metadata":{"trusted":true},"cell_type":"code","source":"empty = ()\nempty","execution_count":32,"outputs":[{"output_type":"execute_result","execution_count":32,"data":{"text/plain":"()"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Bem como a criação de uma tupla de apenas um elemento (reparem na \",\" após o elemento)"},{"metadata":{"trusted":true},"cell_type":"code","source":"singleton = 'hello', # <-- note trailing comma\nsingleton","execution_count":34,"outputs":[{"output_type":"execute_result","execution_count":34,"data":{"text/plain":"('hello',)"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Podemos ver o tamanho da tupla vazia, e como espero, o resultado é 0:"},{"metadata":{"trusted":true},"cell_type":"code","source":"len(empty)","execution_count":36,"outputs":[{"output_type":"execute_result","execution_count":36,"data":{"text/plain":"0"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Bem como o tamanho da tupla de apenas um elemento:"},{"metadata":{"trusted":true},"cell_type":"code","source":"len(singleton)","execution_count":37,"outputs":[{"output_type":"execute_result","execution_count":37,"data":{"text/plain":"5"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Podemos fazer o despacotamento/unpacking dos valores da tupla em diferentes variáveis. Como referência vamos usar a tupla t, criada anteriormente: "},{"metadata":{"trusted":true},"cell_type":"code","source":"t","execution_count":38,"outputs":[{"output_type":"execute_result","execution_count":38,"data":{"text/plain":"(12345, 54321, 'hello!')"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"x, y, z = t\nprint(x,y,z)","execution_count":39,"outputs":[{"output_type":"stream","text":"12345 54321 hello!\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## Sets\n### Conjuntos de dados sem ordem e sem repetição\n### Úteis para verificações de pertencimento, operações de conjuntos ou remoção de repetições"},{"metadata":{},"cell_type":"markdown","source":"### Podemos criar um *set* utilizando das chaves para conter os elementos do mesmo. Abaixo temos um exemplo:"},{"metadata":{"trusted":true},"cell_type":"code","source":"basket = {'apple', 'orange', 'apple', 'pear', 'orange', 'banana'}\nprint(basket) # show that duplicates have been removed","execution_count":41,"outputs":[{"output_type":"stream","text":"{'banana', 'apple', 'orange', 'pear'}\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"### Diversas verificações típicas de conjuntos matemáticos são possíveis, como operações de pertencimento, união, intersecção, etc."},{"metadata":{"trusted":true},"cell_type":"code","source":"'orange' in basket # fast membership testing","execution_count":42,"outputs":[{"output_type":"execute_result","execution_count":42,"data":{"text/plain":"True"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"'crabgrass' in basket","execution_count":44,"outputs":[{"output_type":"execute_result","execution_count":44,"data":{"text/plain":"False"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### A função *set* também permite a conversão ou criação de dados passados a mesma em um novo *set*. No caso abaixo, ao informamos apenas uma string, a função era separar todas as letras e remover as letras duplicadas, gerando os elementos do novo conjunto."},{"metadata":{"trusted":true},"cell_type":"code","source":"a = set('abracadabra')\nb = set('alacazam')\na # unique letters in a","execution_count":45,"outputs":[{"output_type":"execute_result","execution_count":45,"data":{"text/plain":"{'a', 'b', 'c', 'd', 'r'}"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Mais algums exemplos de operações possíveis com os conjuntos:"},{"metadata":{"trusted":true},"cell_type":"code","source":"a - b # letters in a but not in b","execution_count":46,"outputs":[{"output_type":"execute_result","execution_count":46,"data":{"text/plain":"{'b', 'd', 'r'}"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"a | b # letters in a or b or both","execution_count":47,"outputs":[{"output_type":"execute_result","execution_count":47,"data":{"text/plain":"{'a', 'b', 'c', 'd', 'l', 'm', 'r', 'z'}"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"a & b # letters in both a and b","execution_count":48,"outputs":[{"output_type":"execute_result","execution_count":48,"data":{"text/plain":"{'a', 'c'}"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"a ^ b # letters in a or b but not both","execution_count":49,"outputs":[{"output_type":"execute_result","execution_count":49,"data":{"text/plain":"{'b', 'd', 'l', 'm', 'r', 'z'}"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Dicionários\n### Úteis para relacionar argumentos ou chaves com valores\n### Podem ser usados para passar argumentos para funções\n### Podem ser utilizadas operações de conjuntos e pertencimento"},{"metadata":{},"cell_type":"markdown","source":"### Os dicionários e conjuntos são muito semelhantes, sendo ambos delimitados com o uso de chaves. A diferença consiste no fato de que o dicionário guardar sempre um par de elementos (chave e valor) em cada posição sua, separados por \":\" entre a chave e o valor, e com \",\" entre diferentes pares."},{"metadata":{},"cell_type":"markdown","source":"### Abaixo temos um exemplo de criação de um dicionário com dois pares de chave e valor. Como podemos ver, podemos associar um novo par de chave e valor ao dicionário utilizado da sintaxe \"dicionario[chave]=valor\", onde dicionario é a variável que guarda nosso dicionário, chave é a chave a ser utilizada, e valor o valor associado à chave. Caso essa chave já existe no dicionário, o valor da mesma será substituído."},{"metadata":{"trusted":true},"cell_type":"code","source":"tel = {'jack': 4098, 'sape': 4139}\ntel['guido'] = 4127\ntel","execution_count":50,"outputs":[{"output_type":"execute_result","execution_count":50,"data":{"text/plain":"{'jack': 4098, 'sape': 4139, 'guido': 4127}"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Podemos recuperar o valor associado a uma chave de forma bastante simples:"},{"metadata":{"trusted":true},"cell_type":"code","source":"tel['jack']","execution_count":51,"outputs":[{"output_type":"execute_result","execution_count":51,"data":{"text/plain":"4098"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### A deleção de elementos do dicionário também é direta, através do enuncado *del*:"},{"metadata":{"trusted":true},"cell_type":"code","source":"del tel['sape']\ntel","execution_count":52,"outputs":[{"output_type":"execute_result","execution_count":52,"data":{"text/plain":"{'jack': 4098, 'guido': 4127}"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Apesar de as chaves precisarem ser únicas (assim como os elementos de um conjunto), os valores associados a cada chave podem ser repetidos."},{"metadata":{"trusted":true},"cell_type":"code","source":"tel['irv'] = 4127\ntel","execution_count":53,"outputs":[{"output_type":"execute_result","execution_count":53,"data":{"text/plain":"{'jack': 4098, 'guido': 4127, 'irv': 4127}"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### É possível também converter as chaves de um dicionário em uma lista:"},{"metadata":{"trusted":true},"cell_type":"code","source":"list(tel)","execution_count":54,"outputs":[{"output_type":"execute_result","execution_count":54,"data":{"text/plain":"['jack', 'guido', 'irv']"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Bem como obter diretamente uma lista ordenada através da função *sorted()*"},{"metadata":{"trusted":true},"cell_type":"code","source":"sorted(tel)","execution_count":55,"outputs":[{"output_type":"execute_result","execution_count":55,"data":{"text/plain":"['guido', 'irv', 'jack']"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Assim como nos conjuntos, verificações de pertencimento e afins são possíveis:"},{"metadata":{"trusted":true},"cell_type":"code","source":"'guido' in tel","execution_count":56,"outputs":[{"output_type":"execute_result","execution_count":56,"data":{"text/plain":"True"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"'jack' not in tel","execution_count":57,"outputs":[{"output_type":"execute_result","execution_count":57,"data":{"text/plain":"False"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Assim como temos para listas, tuplas e conjuntos, a criação de um dicionários também pode ser realizada diretamente pela função *dict()*. Onde basta informamos como entrada, por exemplo, uma lista contendo os pares em posições delimitadas."},{"metadata":{"trusted":true},"cell_type":"code","source":"dict([('sape', 4139), ('guido', 4127), ('jack', 4098)])","execution_count":59,"outputs":[{"output_type":"execute_result","execution_count":59,"data":{"text/plain":"{'sape': 4139, 'guido': 4127, 'jack': 4098}"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### O uso de técnicas como *List Comprehension* também é possível e podemos gerar dicionários de forma bastante semelhante às listas (substituindo o colchetes por chaves e adicionando a sintax de de chave:valor ao objeto retornado em cada iteração)"},{"metadata":{"trusted":true},"cell_type":"code","source":"{x: x**2 for x in (2, 4, 6)}","execution_count":58,"outputs":[{"output_type":"execute_result","execution_count":58,"data":{"text/plain":"{2: 4, 4: 16, 6: 36}"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Por fim, a função *dict()* também possibilita que passemos como argumento pares \"chave=valor\", separados por vírgula."},{"metadata":{"trusted":true},"cell_type":"code","source":"print(dict(sape=4139, guido=4127, jack=4098))","execution_count":61,"outputs":[{"output_type":"stream","text":"{'sape': 4139, 'guido': 4127, 'jack': 4098}\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## Técnicas de *Looping*"},{"metadata":{},"cell_type":"markdown","source":"### Existem algumas formas interessantes de trabalhar com loops e certas funções que tornam o trabalho mais fácil quando queremos extrair certas informações dessas diferentes estruturas de dados que vimos hoje."},{"metadata":{},"cell_type":"markdown","source":"### Por exemplo, através do uso do método *items()* aplicado no dicionário *knights* podemos extrair para duas variáveis em nosso loop \"k\" e \"v\", os valores respectivos de uma chave e seu valor, a cada passo da iteração. Passando por todos os elementos do mesmoe recuperando automaticamente a informação contida na estrutura."},{"metadata":{"trusted":true},"cell_type":"code","source":"knights = {'gallahad': 'the pure', 'robin': 'the brave'}\nfor k, v in knights.items():\n print(k, v)","execution_count":62,"outputs":[{"output_type":"stream","text":"gallahad the pure\nrobin the brave\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"### De forma semelhante, fazendo uso da função *enumerate()*, podemos recuperar o indíce e o valor desta posição associados a lista sobre a qual a função é aplicada:"},{"metadata":{"trusted":true},"cell_type":"code","source":"for i, v in enumerate(['tic', 'tac', 'toe']):\n print(i, v)","execution_count":63,"outputs":[{"output_type":"stream","text":"0 tic\n1 tac\n2 toe\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"### A função *zip()* permite que sejam extraídas informações de mais de uma lista ao memso tempo, como podemos ver abaixo:"},{"metadata":{"trusted":true},"cell_type":"code","source":"questions = ['name', 'quest', 'favorite color']\nanswers = ['lancelot', 'the holy grail', 'blue']\nfor q, a in zip(questions, answers):\n print('What is your {0}? It is {1}.'.format(q, a))","execution_count":64,"outputs":[{"output_type":"stream","text":"What is your name? It is lancelot.\nWhat is your quest? It is the holy grail.\nWhat is your favorite color? It is blue.\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"### Reverter a ordem de iterçaão de um objeto ou sequência também é bem direto através do uso da função *reverse()*."},{"metadata":{"trusted":true},"cell_type":"code","source":"for i in reversed(range(1, 10, 2)):\n print(i)","execution_count":65,"outputs":[{"output_type":"stream","text":"9\n7\n5\n3\n1\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"### O uso combinado dessas diversas funções nos permite fazer coisas como remover as duplicatas e ordernar elementos de uma lista inicial, por exemplo:"},{"metadata":{"trusted":true},"cell_type":"code","source":"basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana']\nfor f in sorted(set(basket)):\n print(f)","execution_count":66,"outputs":[{"output_type":"stream","text":"apple\nbanana\norange\npear\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"### Como as listas, tuplas e afins são estruturas que guardam diversos valores podemos iterar diretamente sobre elas, e, neste caso, aplicar um filtro aos valores, removendo aqueles que são do tipo *Not a Number*."},{"metadata":{"trusted":true},"cell_type":"code","source":"import math\nraw_data = [56.2, float('NaN'), 51.7, 55.3, 52.5, float('NaN'), 47.8]\nfiltered_data = []\nfor value in raw_data:\n if not math.isnan(value):\n filtered_data.append(value)\nprint(raw_data)\nfiltered_data","execution_count":67,"outputs":[{"output_type":"stream","text":"[56.2, nan, 51.7, 55.3, 52.5, nan, 47.8]\n","name":"stdout"},{"output_type":"execute_result","execution_count":67,"data":{"text/plain":"[56.2, 51.7, 55.3, 52.5, 47.8]"},"metadata":{}}]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4} | 0047/234/47234828.ipynb | s3://data-agents/kaggle-outputs/sharded/010_00047.jsonl.gz |
{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":24,"outputs":[{"output_type":"stream","text":"/kaggle/input/world-happiness/2017.csv\n/kaggle/input/world-happiness/2019.csv\n/kaggle/input/world-happiness/2016.csv\n/kaggle/input/world-happiness/2015.csv\n/kaggle/input/world-happiness/2018.csv\n","name":"stdout"}]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"raw_2015 = pd.read_csv(r'../input/world-happiness/2015.csv') \nraw_2016 = pd.read_csv(r'../input/world-happiness/2015.csv') \nraw_2017 = pd.read_csv(r'../input/world-happiness/2016.csv') \nraw_2018 = pd.read_csv(r'../input/world-happiness/2017.csv') \nraw_2019 = pd.read_csv(r'../input/world-happiness/2019.csv') \n\nraw_2016","execution_count":25,"outputs":[{"output_type":"execute_result","execution_count":25,"data":{"text/plain":" Country Region Happiness Rank \\\n0 Switzerland Western Europe 1 \n1 Iceland Western Europe 2 \n2 Denmark Western Europe 3 \n3 Norway Western Europe 4 \n4 Canada North America 5 \n.. ... ... ... \n153 Rwanda Sub-Saharan Africa 154 \n154 Benin Sub-Saharan Africa 155 \n155 Syria Middle East and Northern Africa 156 \n156 Burundi Sub-Saharan Africa 157 \n157 Togo Sub-Saharan Africa 158 \n\n Happiness Score Standard Error Economy (GDP per Capita) Family \\\n0 7.587 0.03411 1.39651 1.34951 \n1 7.561 0.04884 1.30232 1.40223 \n2 7.527 0.03328 1.32548 1.36058 \n3 7.522 0.03880 1.45900 1.33095 \n4 7.427 0.03553 1.32629 1.32261 \n.. ... ... ... ... \n153 3.465 0.03464 0.22208 0.77370 \n154 3.340 0.03656 0.28665 0.35386 \n155 3.006 0.05015 0.66320 0.47489 \n156 2.905 0.08658 0.01530 0.41587 \n157 2.839 0.06727 0.20868 0.13995 \n\n Health (Life Expectancy) Freedom Trust (Government Corruption) \\\n0 0.94143 0.66557 0.41978 \n1 0.94784 0.62877 0.14145 \n2 0.87464 0.64938 0.48357 \n3 0.88521 0.66973 0.36503 \n4 0.90563 0.63297 0.32957 \n.. ... ... ... \n153 0.42864 0.59201 0.55191 \n154 0.31910 0.48450 0.08010 \n155 0.72193 0.15684 0.18906 \n156 0.22396 0.11850 0.10062 \n157 0.28443 0.36453 0.10731 \n\n Generosity Dystopia Residual \n0 0.29678 2.51738 \n1 0.43630 2.70201 \n2 0.34139 2.49204 \n3 0.34699 2.46531 \n4 0.45811 2.45176 \n.. ... ... \n153 0.22628 0.67042 \n154 0.18260 1.63328 \n155 0.47179 0.32858 \n156 0.19727 1.83302 \n157 0.16681 1.56726 \n\n[158 rows x 12 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Country</th>\n <th>Region</th>\n <th>Happiness Rank</th>\n <th>Happiness Score</th>\n <th>Standard Error</th>\n <th>Economy (GDP per Capita)</th>\n <th>Family</th>\n <th>Health (Life Expectancy)</th>\n <th>Freedom</th>\n <th>Trust (Government Corruption)</th>\n <th>Generosity</th>\n <th>Dystopia Residual</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Switzerland</td>\n <td>Western Europe</td>\n <td>1</td>\n <td>7.587</td>\n <td>0.03411</td>\n <td>1.39651</td>\n <td>1.34951</td>\n <td>0.94143</td>\n <td>0.66557</td>\n <td>0.41978</td>\n <td>0.29678</td>\n <td>2.51738</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Iceland</td>\n <td>Western Europe</td>\n <td>2</td>\n <td>7.561</td>\n <td>0.04884</td>\n <td>1.30232</td>\n <td>1.40223</td>\n <td>0.94784</td>\n <td>0.62877</td>\n <td>0.14145</td>\n <td>0.43630</td>\n <td>2.70201</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Denmark</td>\n <td>Western Europe</td>\n <td>3</td>\n <td>7.527</td>\n <td>0.03328</td>\n <td>1.32548</td>\n <td>1.36058</td>\n <td>0.87464</td>\n <td>0.64938</td>\n <td>0.48357</td>\n <td>0.34139</td>\n <td>2.49204</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Norway</td>\n <td>Western Europe</td>\n <td>4</td>\n <td>7.522</td>\n <td>0.03880</td>\n <td>1.45900</td>\n <td>1.33095</td>\n <td>0.88521</td>\n <td>0.66973</td>\n <td>0.36503</td>\n <td>0.34699</td>\n <td>2.46531</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Canada</td>\n <td>North America</td>\n <td>5</td>\n <td>7.427</td>\n <td>0.03553</td>\n <td>1.32629</td>\n <td>1.32261</td>\n <td>0.90563</td>\n <td>0.63297</td>\n <td>0.32957</td>\n <td>0.45811</td>\n <td>2.45176</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>153</th>\n <td>Rwanda</td>\n <td>Sub-Saharan Africa</td>\n <td>154</td>\n <td>3.465</td>\n <td>0.03464</td>\n <td>0.22208</td>\n <td>0.77370</td>\n <td>0.42864</td>\n <td>0.59201</td>\n <td>0.55191</td>\n <td>0.22628</td>\n <td>0.67042</td>\n </tr>\n <tr>\n <th>154</th>\n <td>Benin</td>\n <td>Sub-Saharan Africa</td>\n <td>155</td>\n <td>3.340</td>\n <td>0.03656</td>\n <td>0.28665</td>\n <td>0.35386</td>\n <td>0.31910</td>\n <td>0.48450</td>\n <td>0.08010</td>\n <td>0.18260</td>\n <td>1.63328</td>\n </tr>\n <tr>\n <th>155</th>\n <td>Syria</td>\n <td>Middle East and Northern Africa</td>\n <td>156</td>\n <td>3.006</td>\n <td>0.05015</td>\n <td>0.66320</td>\n <td>0.47489</td>\n <td>0.72193</td>\n <td>0.15684</td>\n <td>0.18906</td>\n <td>0.47179</td>\n <td>0.32858</td>\n </tr>\n <tr>\n <th>156</th>\n <td>Burundi</td>\n <td>Sub-Saharan Africa</td>\n <td>157</td>\n <td>2.905</td>\n <td>0.08658</td>\n <td>0.01530</td>\n <td>0.41587</td>\n <td>0.22396</td>\n <td>0.11850</td>\n <td>0.10062</td>\n <td>0.19727</td>\n <td>1.83302</td>\n </tr>\n <tr>\n <th>157</th>\n <td>Togo</td>\n <td>Sub-Saharan Africa</td>\n <td>158</td>\n <td>2.839</td>\n <td>0.06727</td>\n <td>0.20868</td>\n <td>0.13995</td>\n <td>0.28443</td>\n <td>0.36453</td>\n <td>0.10731</td>\n <td>0.16681</td>\n <td>1.56726</td>\n </tr>\n </tbody>\n</table>\n<p>158 rows × 12 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"raw_2018.describe()","execution_count":26,"outputs":[{"output_type":"execute_result","execution_count":26,"data":{"text/plain":" Happiness.Rank Happiness.Score Whisker.high Whisker.low \\\ncount 155.000000 155.000000 155.000000 155.000000 \nmean 78.000000 5.354019 5.452326 5.255713 \nstd 44.888751 1.131230 1.118542 1.145030 \nmin 1.000000 2.693000 2.864884 2.521116 \n25% 39.500000 4.505500 4.608172 4.374955 \n50% 78.000000 5.279000 5.370032 5.193152 \n75% 116.500000 6.101500 6.194600 6.006527 \nmax 155.000000 7.537000 7.622030 7.479556 \n\n Economy..GDP.per.Capita. Family Health..Life.Expectancy. \\\ncount 155.000000 155.000000 155.000000 \nmean 0.984718 1.188898 0.551341 \nstd 0.420793 0.287263 0.237073 \nmin 0.000000 0.000000 0.000000 \n25% 0.663371 1.042635 0.369866 \n50% 1.064578 1.253918 0.606042 \n75% 1.318027 1.414316 0.723008 \nmax 1.870766 1.610574 0.949492 \n\n Freedom Generosity Trust..Government.Corruption. \\\ncount 155.000000 155.000000 155.000000 \nmean 0.408786 0.246883 0.123120 \nstd 0.149997 0.134780 0.101661 \nmin 0.000000 0.000000 0.000000 \n25% 0.303677 0.154106 0.057271 \n50% 0.437454 0.231538 0.089848 \n75% 0.516561 0.323762 0.153296 \nmax 0.658249 0.838075 0.464308 \n\n Dystopia.Residual \ncount 155.000000 \nmean 1.850238 \nstd 0.500028 \nmin 0.377914 \n25% 1.591291 \n50% 1.832910 \n75% 2.144654 \nmax 3.117485 ","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Happiness.Rank</th>\n <th>Happiness.Score</th>\n <th>Whisker.high</th>\n <th>Whisker.low</th>\n <th>Economy..GDP.per.Capita.</th>\n <th>Family</th>\n <th>Health..Life.Expectancy.</th>\n <th>Freedom</th>\n <th>Generosity</th>\n <th>Trust..Government.Corruption.</th>\n <th>Dystopia.Residual</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>155.000000</td>\n <td>155.000000</td>\n <td>155.000000</td>\n <td>155.000000</td>\n <td>155.000000</td>\n <td>155.000000</td>\n <td>155.000000</td>\n <td>155.000000</td>\n <td>155.000000</td>\n <td>155.000000</td>\n <td>155.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>78.000000</td>\n <td>5.354019</td>\n <td>5.452326</td>\n <td>5.255713</td>\n <td>0.984718</td>\n <td>1.188898</td>\n <td>0.551341</td>\n <td>0.408786</td>\n <td>0.246883</td>\n <td>0.123120</td>\n <td>1.850238</td>\n </tr>\n <tr>\n <th>std</th>\n <td>44.888751</td>\n <td>1.131230</td>\n <td>1.118542</td>\n <td>1.145030</td>\n <td>0.420793</td>\n <td>0.287263</td>\n <td>0.237073</td>\n <td>0.149997</td>\n <td>0.134780</td>\n <td>0.101661</td>\n <td>0.500028</td>\n </tr>\n <tr>\n <th>min</th>\n <td>1.000000</td>\n <td>2.693000</td>\n <td>2.864884</td>\n <td>2.521116</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.377914</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>39.500000</td>\n <td>4.505500</td>\n <td>4.608172</td>\n <td>4.374955</td>\n <td>0.663371</td>\n <td>1.042635</td>\n <td>0.369866</td>\n <td>0.303677</td>\n <td>0.154106</td>\n <td>0.057271</td>\n <td>1.591291</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>78.000000</td>\n <td>5.279000</td>\n <td>5.370032</td>\n <td>5.193152</td>\n <td>1.064578</td>\n <td>1.253918</td>\n <td>0.606042</td>\n <td>0.437454</td>\n <td>0.231538</td>\n <td>0.089848</td>\n <td>1.832910</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>116.500000</td>\n <td>6.101500</td>\n <td>6.194600</td>\n <td>6.006527</td>\n <td>1.318027</td>\n <td>1.414316</td>\n <td>0.723008</td>\n <td>0.516561</td>\n <td>0.323762</td>\n <td>0.153296</td>\n <td>2.144654</td>\n </tr>\n <tr>\n <th>max</th>\n <td>155.000000</td>\n <td>7.537000</td>\n <td>7.622030</td>\n <td>7.479556</td>\n <td>1.870766</td>\n <td>1.610574</td>\n <td>0.949492</td>\n <td>0.658249</td>\n <td>0.838075</td>\n <td>0.464308</td>\n <td>3.117485</td>\n </tr>\n </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"sorted_2015 = raw_2015.sort_values('Country')\nc2015 = sorted_2015['Country']\nc15 = c2015.to_frame().reset_index()\n\nsorted_2016 = raw_2016.sort_values('Country')\nc2016 = sorted_2016['Country']\nc16 = c2016.to_frame().reset_index()\n\nsorted_2017 = raw_2017.sort_values('Country')\nc2017 = sorted_2017['Country']\nc17 = c2017.to_frame().reset_index()\n\nsorted_2018 = raw_2018.sort_values('Country')\nc2018 = sorted_2018['Country']\nc18 = c2018.to_frame().reset_index()\n\nsorted_2019 = raw_2019.sort_values('Country or region')\nc2019 = sorted_2019['Country or region']\nc19 = c2019.to_frame().reset_index()","execution_count":27,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"c15 = c15.set_index('Country')\nc16 = c16.set_index('Country')\nc17 = c17.set_index('Country')\ncountry_15_16 = c15.join(c16, lsuffix='_2015', how='outer', rsuffix='_2016')\ncountry_15_16\n\ncountry_15_16_17 = country_15_16.join(c17, how='outer', rsuffix='_2017')\ncountry_15_16_17\n\nc18 = c18.set_index('Country')\nc18\n\ncountry_15_16_17_18 = country_15_16_17.join(c18, how='outer', rsuffix='_2018')\ncountry_15_16_17_18\n\nc19['Country'] = c19['Country or region'] \nc19 = c19.drop(['Country or region'], axis = 1)\nc19 = c19.set_index('Country')\nc19\n\ncountry_15_16_17_18_19 = country_15_16_17_18.join(c19, how='outer', rsuffix='_2019')\ncountry_15_16_17_18_19","execution_count":28,"outputs":[{"output_type":"execute_result","execution_count":28,"data":{"text/plain":" index_2015 index_2016 index index_2018 index_2019\nCountry \nAfghanistan 152.0 152.0 153.0 140.0 153.0\nAlbania 94.0 94.0 108.0 108.0 106.0\nAlgeria 67.0 67.0 37.0 52.0 87.0\nAngola 136.0 136.0 140.0 139.0 NaN\nArgentina 29.0 29.0 25.0 23.0 46.0\n... ... ... ... ... ...\nVenezuela 22.0 22.0 43.0 81.0 107.0\nVietnam 74.0 74.0 95.0 93.0 93.0\nYemen 135.0 135.0 146.0 145.0 150.0\nZambia 84.0 84.0 105.0 115.0 137.0\nZimbabwe 114.0 114.0 130.0 137.0 145.0\n\n[170 rows x 5 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>index_2015</th>\n <th>index_2016</th>\n <th>index</th>\n <th>index_2018</th>\n <th>index_2019</th>\n </tr>\n <tr>\n <th>Country</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>Afghanistan</th>\n <td>152.0</td>\n <td>152.0</td>\n <td>153.0</td>\n <td>140.0</td>\n <td>153.0</td>\n </tr>\n <tr>\n <th>Albania</th>\n <td>94.0</td>\n <td>94.0</td>\n <td>108.0</td>\n <td>108.0</td>\n <td>106.0</td>\n </tr>\n <tr>\n <th>Algeria</th>\n <td>67.0</td>\n <td>67.0</td>\n <td>37.0</td>\n <td>52.0</td>\n <td>87.0</td>\n </tr>\n <tr>\n <th>Angola</th>\n <td>136.0</td>\n <td>136.0</td>\n <td>140.0</td>\n <td>139.0</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>Argentina</th>\n <td>29.0</td>\n <td>29.0</td>\n <td>25.0</td>\n <td>23.0</td>\n <td>46.0</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>Venezuela</th>\n <td>22.0</td>\n <td>22.0</td>\n <td>43.0</td>\n <td>81.0</td>\n <td>107.0</td>\n </tr>\n <tr>\n <th>Vietnam</th>\n <td>74.0</td>\n <td>74.0</td>\n <td>95.0</td>\n <td>93.0</td>\n <td>93.0</td>\n </tr>\n <tr>\n <th>Yemen</th>\n <td>135.0</td>\n <td>135.0</td>\n <td>146.0</td>\n <td>145.0</td>\n <td>150.0</td>\n </tr>\n <tr>\n <th>Zambia</th>\n <td>84.0</td>\n <td>84.0</td>\n <td>105.0</td>\n <td>115.0</td>\n <td>137.0</td>\n </tr>\n <tr>\n <th>Zimbabwe</th>\n <td>114.0</td>\n <td>114.0</td>\n <td>130.0</td>\n <td>137.0</td>\n <td>145.0</td>\n </tr>\n </tbody>\n</table>\n<p>170 rows × 5 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Missing countries throughout the years\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"missing_countries = country_15_16_17_18_19[country_15_16_17_18_19.isna().any(axis=1)]\nmissing_countries = missing_countries.rename(columns={\"index\": \"index_2017\"})\n\nisnull_color = 'yellow'\nmissing_countries.style.applymap(lambda x: 'background-color: %s' % isnull_color if pd.isnull(x) else '')","execution_count":29,"outputs":[{"output_type":"execute_result","execution_count":29,"data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7fcf4e03e890>","text/html":"<style type=\"text/css\" >\n#T_7ab805e0_2a57_11eb_9339_0242ac130202row0_col4,#T_7ab805e0_2a57_11eb_9339_0242ac130202row1_col0,#T_7ab805e0_2a57_11eb_9339_0242ac130202row1_col1,#T_7ab805e0_2a57_11eb_9339_0242ac130202row1_col4,#T_7ab805e0_2a57_11eb_9339_0242ac130202row2_col2,#T_7ab805e0_2a57_11eb_9339_0242ac130202row3_col3,#T_7ab805e0_2a57_11eb_9339_0242ac130202row4_col2,#T_7ab805e0_2a57_11eb_9339_0242ac130202row4_col3,#T_7ab805e0_2a57_11eb_9339_0242ac130202row4_col4,#T_7ab805e0_2a57_11eb_9339_0242ac130202row5_col0,#T_7ab805e0_2a57_11eb_9339_0242ac130202row5_col1,#T_7ab805e0_2a57_11eb_9339_0242ac130202row5_col2,#T_7ab805e0_2a57_11eb_9339_0242ac130202row5_col3,#T_7ab805e0_2a57_11eb_9339_0242ac130202row6_col3,#T_7ab805e0_2a57_11eb_9339_0242ac130202row7_col0,#T_7ab805e0_2a57_11eb_9339_0242ac130202row7_col1,#T_7ab805e0_2a57_11eb_9339_0242ac130202row7_col2,#T_7ab805e0_2a57_11eb_9339_0242ac130202row7_col4,#T_7ab805e0_2a57_11eb_9339_0242ac130202row8_col3,#T_7ab805e0_2a57_11eb_9339_0242ac130202row9_col2,#T_7ab805e0_2a57_11eb_9339_0242ac130202row10_col4,#T_7ab805e0_2a57_11eb_9339_0242ac130202row11_col2,#T_7ab805e0_2a57_11eb_9339_0242ac130202row12_col0,#T_7ab805e0_2a57_11eb_9339_0242ac130202row12_col1,#T_7ab805e0_2a57_11eb_9339_0242ac130202row13_col4,#T_7ab805e0_2a57_11eb_9339_0242ac130202row14_col0,#T_7ab805e0_2a57_11eb_9339_0242ac130202row14_col1,#T_7ab805e0_2a57_11eb_9339_0242ac130202row14_col2,#T_7ab805e0_2a57_11eb_9339_0242ac130202row14_col3,#T_7ab805e0_2a57_11eb_9339_0242ac130202row15_col0,#T_7ab805e0_2a57_11eb_9339_0242ac130202row15_col1,#T_7ab805e0_2a57_11eb_9339_0242ac130202row15_col2,#T_7ab805e0_2a57_11eb_9339_0242ac130202row15_col3,#T_7ab805e0_2a57_11eb_9339_0242ac130202row16_col2,#T_7ab805e0_2a57_11eb_9339_0242ac130202row16_col3,#T_7ab805e0_2a57_11eb_9339_0242ac130202row16_col4,#T_7ab805e0_2a57_11eb_9339_0242ac130202row17_col0,#T_7ab805e0_2a57_11eb_9339_0242ac130202row17_col1,#T_7ab805e0_2a57_11eb_9339_0242ac130202row17_col3,#T_7ab805e0_2a57_11eb_9339_0242ac130202row17_col4,#T_7ab805e0_2a57_11eb_9339_0242ac130202row18_col0,#T_7ab805e0_2a57_11eb_9339_0242ac130202row18_col1,#T_7ab805e0_2a57_11eb_9339_0242ac130202row19_col0,#T_7ab805e0_2a57_11eb_9339_0242ac130202row19_col1,#T_7ab805e0_2a57_11eb_9339_0242ac130202row19_col3,#T_7ab805e0_2a57_11eb_9339_0242ac130202row19_col4,#T_7ab805e0_2a57_11eb_9339_0242ac130202row20_col2,#T_7ab805e0_2a57_11eb_9339_0242ac130202row20_col3,#T_7ab805e0_2a57_11eb_9339_0242ac130202row20_col4,#T_7ab805e0_2a57_11eb_9339_0242ac130202row21_col0,#T_7ab805e0_2a57_11eb_9339_0242ac130202row21_col1,#T_7ab805e0_2a57_11eb_9339_0242ac130202row22_col4,#T_7ab805e0_2a57_11eb_9339_0242ac130202row23_col3,#T_7ab805e0_2a57_11eb_9339_0242ac130202row23_col4,#T_7ab805e0_2a57_11eb_9339_0242ac130202row24_col2,#T_7ab805e0_2a57_11eb_9339_0242ac130202row24_col3,#T_7ab805e0_2a57_11eb_9339_0242ac130202row25_col3,#T_7ab805e0_2a57_11eb_9339_0242ac130202row26_col0,#T_7ab805e0_2a57_11eb_9339_0242ac130202row26_col1,#T_7ab805e0_2a57_11eb_9339_0242ac130202row26_col2,#T_7ab805e0_2a57_11eb_9339_0242ac130202row26_col4,#T_7ab805e0_2a57_11eb_9339_0242ac130202row27_col0,#T_7ab805e0_2a57_11eb_9339_0242ac130202row27_col1,#T_7ab805e0_2a57_11eb_9339_0242ac130202row27_col2,#T_7ab805e0_2a57_11eb_9339_0242ac130202row27_col3,#T_7ab805e0_2a57_11eb_9339_0242ac130202row28_col4{\n background-color: yellow;\n }</style><table id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >index_2015</th> <th class=\"col_heading level0 col1\" >index_2016</th> <th class=\"col_heading level0 col2\" >index_2017</th> <th class=\"col_heading level0 col3\" >index_2018</th> <th class=\"col_heading level0 col4\" >index_2019</th> </tr> <tr> <th class=\"index_name level0\" >Country</th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> </tr></thead><tbody>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >Angola</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row0_col0\" class=\"data row0 col0\" >136.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row0_col1\" class=\"data row0 col1\" >136.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row0_col2\" class=\"data row0 col2\" >140.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row0_col3\" class=\"data row0 col3\" >139.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row0_col4\" class=\"data row0 col4\" >nan</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >Belize</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row1_col0\" class=\"data row1 col0\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row1_col1\" class=\"data row1 col1\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row1_col2\" class=\"data row1 col2\" >51.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row1_col3\" class=\"data row1 col3\" >49.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row1_col4\" class=\"data row1 col4\" >nan</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >Central African Republic</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row2_col0\" class=\"data row2 col0\" >147.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row2_col1\" class=\"data row2 col1\" >147.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row2_col2\" class=\"data row2 col2\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row2_col3\" class=\"data row2 col3\" >154.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row2_col4\" class=\"data row2 col4\" >154.000000</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >Comoros</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row3_col0\" class=\"data row3 col0\" >139.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row3_col1\" class=\"data row3 col1\" >139.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row3_col2\" class=\"data row3 col2\" >137.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row3_col3\" class=\"data row3 col3\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row3_col4\" class=\"data row3 col4\" >141.000000</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >Djibouti</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row4_col0\" class=\"data row4 col0\" >125.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row4_col1\" class=\"data row4 col1\" >125.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row4_col2\" class=\"data row4 col2\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row4_col3\" class=\"data row4 col3\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row4_col4\" class=\"data row4 col4\" >nan</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >Gambia</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row5_col0\" class=\"data row5 col0\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row5_col1\" class=\"data row5 col1\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row5_col2\" class=\"data row5 col2\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row5_col3\" class=\"data row5 col3\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row5_col4\" class=\"data row5 col4\" >119.000000</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >Hong Kong</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row6_col0\" class=\"data row6 col0\" >71.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row6_col1\" class=\"data row6 col1\" >71.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row6_col2\" class=\"data row6 col2\" >74.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row6_col3\" class=\"data row6 col3\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row6_col4\" class=\"data row6 col4\" >75.000000</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >Hong Kong S.A.R., China</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row7_col0\" class=\"data row7 col0\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row7_col1\" class=\"data row7 col1\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row7_col2\" class=\"data row7 col2\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row7_col3\" class=\"data row7 col3\" >70.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row7_col4\" class=\"data row7 col4\" >nan</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >Laos</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row8_col0\" class=\"data row8 col0\" >98.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row8_col1\" class=\"data row8 col1\" >98.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row8_col2\" class=\"data row8 col2\" >101.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row8_col3\" class=\"data row8 col3\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row8_col4\" class=\"data row8 col4\" >104.000000</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >Lesotho</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row9_col0\" class=\"data row9 col0\" >96.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row9_col1\" class=\"data row9 col1\" >96.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row9_col2\" class=\"data row9 col2\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row9_col3\" class=\"data row9 col3\" >138.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row9_col4\" class=\"data row9 col4\" >143.000000</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >Macedonia</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row10_col0\" class=\"data row10 col0\" >92.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row10_col1\" class=\"data row10 col1\" >92.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row10_col2\" class=\"data row10 col2\" >94.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row10_col3\" class=\"data row10 col3\" >91.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row10_col4\" class=\"data row10 col4\" >nan</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >Mozambique</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row11_col0\" class=\"data row11 col0\" >93.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row11_col1\" class=\"data row11 col1\" >93.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row11_col2\" class=\"data row11 col2\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row11_col3\" class=\"data row11 col3\" >112.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row11_col4\" class=\"data row11 col4\" >122.000000</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row12\" class=\"row_heading level0 row12\" >Namibia</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row12_col0\" class=\"data row12 col0\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row12_col1\" class=\"data row12 col1\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row12_col2\" class=\"data row12 col2\" >112.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row12_col3\" class=\"data row12 col3\" >110.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row12_col4\" class=\"data row12 col4\" >112.000000</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row13\" class=\"row_heading level0 row13\" >North Cyprus</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row13_col0\" class=\"data row13 col0\" >65.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row13_col1\" class=\"data row13 col1\" >65.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row13_col2\" class=\"data row13 col2\" >61.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row13_col3\" class=\"data row13 col3\" >60.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row13_col4\" class=\"data row13 col4\" >nan</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row14\" class=\"row_heading level0 row14\" >North Macedonia</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row14_col0\" class=\"data row14 col0\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row14_col1\" class=\"data row14 col1\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row14_col2\" class=\"data row14 col2\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row14_col3\" class=\"data row14 col3\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row14_col4\" class=\"data row14 col4\" >83.000000</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row15\" class=\"row_heading level0 row15\" >Northern Cyprus</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row15_col0\" class=\"data row15 col0\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row15_col1\" class=\"data row15 col1\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row15_col2\" class=\"data row15 col2\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row15_col3\" class=\"data row15 col3\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row15_col4\" class=\"data row15 col4\" >63.000000</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row16\" class=\"row_heading level0 row16\" >Oman</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row16_col0\" class=\"data row16 col0\" >21.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row16_col1\" class=\"data row16 col1\" >21.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row16_col2\" class=\"data row16 col2\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row16_col3\" class=\"data row16 col3\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row16_col4\" class=\"data row16 col4\" >nan</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row17\" class=\"row_heading level0 row17\" >Puerto Rico</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row17_col0\" class=\"data row17 col0\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row17_col1\" class=\"data row17 col1\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row17_col2\" class=\"data row17 col2\" >14.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row17_col3\" class=\"data row17 col3\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row17_col4\" class=\"data row17 col4\" >nan</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row18\" class=\"row_heading level0 row18\" >Somalia</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row18_col0\" class=\"data row18 col0\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row18_col1\" class=\"data row18 col1\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row18_col2\" class=\"data row18 col2\" >75.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row18_col3\" class=\"data row18 col3\" >92.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row18_col4\" class=\"data row18 col4\" >111.000000</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row19\" class=\"row_heading level0 row19\" >Somaliland Region</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row19_col0\" class=\"data row19 col0\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row19_col1\" class=\"data row19 col1\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row19_col2\" class=\"data row19 col2\" >96.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row19_col3\" class=\"data row19 col3\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row19_col4\" class=\"data row19 col4\" >nan</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row20\" class=\"row_heading level0 row20\" >Somaliland region</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row20_col0\" class=\"data row20 col0\" >90.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row20_col1\" class=\"data row20 col1\" >90.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row20_col2\" class=\"data row20 col2\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row20_col3\" class=\"data row20 col3\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row20_col4\" class=\"data row20 col4\" >nan</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row21\" class=\"row_heading level0 row21\" >South Sudan</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row21_col0\" class=\"data row21 col0\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row21_col1\" class=\"data row21 col1\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row21_col2\" class=\"data row21 col2\" >142.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row21_col3\" class=\"data row21 col3\" >146.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row21_col4\" class=\"data row21 col4\" >155.000000</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row22\" class=\"row_heading level0 row22\" >Sudan</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row22_col0\" class=\"data row22 col0\" >117.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row22_col1\" class=\"data row22 col1\" >117.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row22_col2\" class=\"data row22 col2\" >132.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row22_col3\" class=\"data row22 col3\" >129.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row22_col4\" class=\"data row22 col4\" >nan</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row23\" class=\"row_heading level0 row23\" >Suriname</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row23_col0\" class=\"data row23 col0\" >39.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row23_col1\" class=\"data row23 col1\" >39.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row23_col2\" class=\"data row23 col2\" >39.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row23_col3\" class=\"data row23 col3\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row23_col4\" class=\"data row23 col4\" >nan</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row24\" class=\"row_heading level0 row24\" >Swaziland</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row24_col0\" class=\"data row24 col0\" >100.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row24_col1\" class=\"data row24 col1\" >100.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row24_col2\" class=\"data row24 col2\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row24_col3\" class=\"data row24 col3\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row24_col4\" class=\"data row24 col4\" >134.000000</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row25\" class=\"row_heading level0 row25\" >Taiwan</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row25_col0\" class=\"data row25 col0\" >37.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row25_col1\" class=\"data row25 col1\" >37.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row25_col2\" class=\"data row25 col2\" >34.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row25_col3\" class=\"data row25 col3\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row25_col4\" class=\"data row25 col4\" >24.000000</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row26\" class=\"row_heading level0 row26\" >Taiwan Province of China</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row26_col0\" class=\"data row26 col0\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row26_col1\" class=\"data row26 col1\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row26_col2\" class=\"data row26 col2\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row26_col3\" class=\"data row26 col3\" >32.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row26_col4\" class=\"data row26 col4\" >nan</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row27\" class=\"row_heading level0 row27\" >Trinidad & Tobago</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row27_col0\" class=\"data row27 col0\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row27_col1\" class=\"data row27 col1\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row27_col2\" class=\"data row27 col2\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row27_col3\" class=\"data row27 col3\" >nan</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row27_col4\" class=\"data row27 col4\" >38.000000</td>\n </tr>\n <tr>\n <th id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202level0_row28\" class=\"row_heading level0 row28\" >Trinidad and Tobago</th>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row28_col0\" class=\"data row28 col0\" >40.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row28_col1\" class=\"data row28 col1\" >40.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row28_col2\" class=\"data row28 col2\" >42.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row28_col3\" class=\"data row28 col3\" >37.000000</td>\n <td id=\"T_7ab805e0_2a57_11eb_9339_0242ac130202row28_col4\" class=\"data row28 col4\" >nan</td>\n </tr>\n </tbody></table>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Unification of columns and append all years"},{"metadata":{"trusted":true},"cell_type":"code","source":"cols_2015 = raw_2015.columns.to_frame().rename(columns={0: \"2015\"})\n\ncols_2016 = raw_2016.columns.to_frame().rename(columns={0: \"2016\"})\n\ncols_15_16 = cols_2015.join(cols_2016, how='outer')\ncols_15_16\n\n\nraw_2017 = raw_2017.rename(columns={'Whisker.high': \"Upper Confidence Interval\",\n 'Whisker.low': \"Lower Confidence Interval\",\n 'Economy..GDP.per.Capita.': \"Economy (GDP per Capita)\",\n 'Trust..Government.Corruption.': \"Trust (Government Corruption)\",\n 'Health..Life.Expectancy.': \"Health (Life Expectancy)\",\n 'Dystopia.Residual': \"Dystopia Residual\",\n 'Happiness.Rank': \"Happiness Rank\",\n 'Happiness.Score': \"Happiness Score\"})\n\ncols_2017 = raw_2017.columns.to_frame().rename(columns={0: \"2017\"})\ncols_2017\n\ncols_15_16_17 = cols_15_16.join(cols_2017, how='outer')\ncols_15_16_17\n\n\nraw_2018 = raw_2018.rename(columns={'Country or region': \"Country\",\n 'Health..Life.Expectancy.': \"Health (Life Expectancy)\",\n 'Perceptions of corruption': \"Trust (Government Corruption)\",\n 'Score': \"Happiness Score\",\n 'Overall rank': \"Happiness Rank\",\n 'Freedom to make life choices': \"Freedom\",\n 'Economy..GDP.per.Capita.': \"Economy (GDP per Capita)\",\n 'Happiness.Rank': \"Happiness Rank\",\n 'Happiness.Score': \"Happiness Score\",\n 'Dystopia.Residual': \"Dystopia Residual\",\n 'Trust..Government.Corruption.': \"Trust (Government Corruption)\",\n 'Whisker.high': \"Upper Confidence Interval\",\n 'Whisker.low': \"Lower Confidence Interval\"})\n \ncols_2018 = raw_2018.columns.to_frame().rename(columns={0: \"2018\"})\ncols_2018\n\ncols_15_16_17_18 = cols_15_16_17.join(cols_2018, how='outer')\ncols_15_16_17_18\n\n\nraw_2019 = raw_2019.rename(columns={'Perceptions of corruption': \"Trust (Government Corruption)\",\n 'Score': \"Happiness Score\",\n 'Overall rank': \"Happiness Rank\",\n 'Country or region': \"Country\",\n 'Healthy life expectancy': \"Health (Life Expectancy)\",\n 'GDP per capita': \"Economy (GDP per Capita)\",\n 'Freedom to make life choices': \"Freedom\",\n 'Social support': \"Family\",\n 'Whisker.high': \"Upper Confidence Interval\",\n 'Whisker.low': \"Lower Confidence Interval\"})\ncols_2019 = raw_2019.columns.to_frame().rename(columns={0: \"2019\"})\ncols_2019\n\ncols_15_16_17_18_19 = cols_15_16_17_18.join(cols_2019, how='outer')\nmissing_cols = cols_15_16_17_18_19","execution_count":30,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"missing_cols","execution_count":31,"outputs":[{"output_type":"execute_result","execution_count":31,"data":{"text/plain":" 2015 \\\nCountry Country \nDystopia Residual Dystopia Residual \nEconomy (GDP per Capita) Economy (GDP per Capita) \nFamily Family \nFreedom Freedom \nGenerosity Generosity \nHappiness Rank Happiness Rank \nHappiness Score Happiness Score \nHealth (Life Expectancy) Health (Life Expectancy) \nLower Confidence Interval NaN \nRegion Region \nStandard Error Standard Error \nTrust (Government Corruption) Trust (Government Corruption) \nUpper Confidence Interval NaN \n\n 2016 \\\nCountry Country \nDystopia Residual Dystopia Residual \nEconomy (GDP per Capita) Economy (GDP per Capita) \nFamily Family \nFreedom Freedom \nGenerosity Generosity \nHappiness Rank Happiness Rank \nHappiness Score Happiness Score \nHealth (Life Expectancy) Health (Life Expectancy) \nLower Confidence Interval NaN \nRegion Region \nStandard Error Standard Error \nTrust (Government Corruption) Trust (Government Corruption) \nUpper Confidence Interval NaN \n\n 2017 \\\nCountry Country \nDystopia Residual Dystopia Residual \nEconomy (GDP per Capita) Economy (GDP per Capita) \nFamily Family \nFreedom Freedom \nGenerosity Generosity \nHappiness Rank Happiness Rank \nHappiness Score Happiness Score \nHealth (Life Expectancy) Health (Life Expectancy) \nLower Confidence Interval Lower Confidence Interval \nRegion Region \nStandard Error NaN \nTrust (Government Corruption) Trust (Government Corruption) \nUpper Confidence Interval Upper Confidence Interval \n\n 2018 \\\nCountry Country \nDystopia Residual Dystopia Residual \nEconomy (GDP per Capita) Economy (GDP per Capita) \nFamily Family \nFreedom Freedom \nGenerosity Generosity \nHappiness Rank Happiness Rank \nHappiness Score Happiness Score \nHealth (Life Expectancy) Health (Life Expectancy) \nLower Confidence Interval Lower Confidence Interval \nRegion NaN \nStandard Error NaN \nTrust (Government Corruption) Trust (Government Corruption) \nUpper Confidence Interval Upper Confidence Interval \n\n 2019 \nCountry Country \nDystopia Residual NaN \nEconomy (GDP per Capita) Economy (GDP per Capita) \nFamily Family \nFreedom Freedom \nGenerosity Generosity \nHappiness Rank Happiness Rank \nHappiness Score Happiness Score \nHealth (Life Expectancy) Health (Life Expectancy) \nLower Confidence Interval NaN \nRegion NaN \nStandard Error NaN \nTrust (Government Corruption) Trust (Government Corruption) \nUpper Confidence Interval NaN ","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>2015</th>\n <th>2016</th>\n <th>2017</th>\n <th>2018</th>\n <th>2019</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>Country</th>\n <td>Country</td>\n <td>Country</td>\n <td>Country</td>\n <td>Country</td>\n <td>Country</td>\n </tr>\n <tr>\n <th>Dystopia Residual</th>\n <td>Dystopia Residual</td>\n <td>Dystopia Residual</td>\n <td>Dystopia Residual</td>\n <td>Dystopia Residual</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>Economy (GDP per Capita)</th>\n <td>Economy (GDP per Capita)</td>\n <td>Economy (GDP per Capita)</td>\n <td>Economy (GDP per Capita)</td>\n <td>Economy (GDP per Capita)</td>\n <td>Economy (GDP per Capita)</td>\n </tr>\n <tr>\n <th>Family</th>\n <td>Family</td>\n <td>Family</td>\n <td>Family</td>\n <td>Family</td>\n <td>Family</td>\n </tr>\n <tr>\n <th>Freedom</th>\n <td>Freedom</td>\n <td>Freedom</td>\n <td>Freedom</td>\n <td>Freedom</td>\n <td>Freedom</td>\n </tr>\n <tr>\n <th>Generosity</th>\n <td>Generosity</td>\n <td>Generosity</td>\n <td>Generosity</td>\n <td>Generosity</td>\n <td>Generosity</td>\n </tr>\n <tr>\n <th>Happiness Rank</th>\n <td>Happiness Rank</td>\n <td>Happiness Rank</td>\n <td>Happiness Rank</td>\n <td>Happiness Rank</td>\n <td>Happiness Rank</td>\n </tr>\n <tr>\n <th>Happiness Score</th>\n <td>Happiness Score</td>\n <td>Happiness Score</td>\n <td>Happiness Score</td>\n <td>Happiness Score</td>\n <td>Happiness Score</td>\n </tr>\n <tr>\n <th>Health (Life Expectancy)</th>\n <td>Health (Life Expectancy)</td>\n <td>Health (Life Expectancy)</td>\n <td>Health (Life Expectancy)</td>\n <td>Health (Life Expectancy)</td>\n <td>Health (Life Expectancy)</td>\n </tr>\n <tr>\n <th>Lower Confidence Interval</th>\n <td>NaN</td>\n <td>NaN</td>\n <td>Lower Confidence Interval</td>\n <td>Lower Confidence Interval</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>Region</th>\n <td>Region</td>\n <td>Region</td>\n <td>Region</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>Standard Error</th>\n <td>Standard Error</td>\n <td>Standard Error</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>Trust (Government Corruption)</th>\n <td>Trust (Government Corruption)</td>\n <td>Trust (Government Corruption)</td>\n <td>Trust (Government Corruption)</td>\n <td>Trust (Government Corruption)</td>\n <td>Trust (Government Corruption)</td>\n </tr>\n <tr>\n <th>Upper Confidence Interval</th>\n <td>NaN</td>\n <td>NaN</td>\n <td>Upper Confidence Interval</td>\n <td>Upper Confidence Interval</td>\n <td>NaN</td>\n </tr>\n </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"raw_2017 = raw_2017.rename(columns={'Whisker.high': \"Upper Confidence Interval\",\n 'Whisker.low': \"Lower Confidence Interval\",\n 'Economy..GDP.per.Capita.': \"Economy (GDP per Capita)\",\n 'Trust..Government.Corruption.': \"Trust (Government Corruption)\",\n 'Health..Life.Expectancy.': \"Health (Life Expectancy)\",\n 'Dystopia.Residual': \"Dystopia Residual\",\n 'Happiness.Rank': \"Happiness Rank\",\n 'Happiness.Score': \"Happiness Score\"})\nraw_2017","execution_count":32,"outputs":[{"output_type":"execute_result","execution_count":32,"data":{"text/plain":" Country Region Happiness Rank \\\n0 Denmark Western Europe 1 \n1 Switzerland Western Europe 2 \n2 Iceland Western Europe 3 \n3 Norway Western Europe 4 \n4 Finland Western Europe 5 \n.. ... ... ... \n152 Benin Sub-Saharan Africa 153 \n153 Afghanistan Southern Asia 154 \n154 Togo Sub-Saharan Africa 155 \n155 Syria Middle East and Northern Africa 156 \n156 Burundi Sub-Saharan Africa 157 \n\n Happiness Score Lower Confidence Interval Upper Confidence Interval \\\n0 7.526 7.460 7.592 \n1 7.509 7.428 7.590 \n2 7.501 7.333 7.669 \n3 7.498 7.421 7.575 \n4 7.413 7.351 7.475 \n.. ... ... ... \n152 3.484 3.404 3.564 \n153 3.360 3.288 3.432 \n154 3.303 3.192 3.414 \n155 3.069 2.936 3.202 \n156 2.905 2.732 3.078 \n\n Economy (GDP per Capita) Family Health (Life Expectancy) Freedom \\\n0 1.44178 1.16374 0.79504 0.57941 \n1 1.52733 1.14524 0.86303 0.58557 \n2 1.42666 1.18326 0.86733 0.56624 \n3 1.57744 1.12690 0.79579 0.59609 \n4 1.40598 1.13464 0.81091 0.57104 \n.. ... ... ... ... \n152 0.39499 0.10419 0.21028 0.39747 \n153 0.38227 0.11037 0.17344 0.16430 \n154 0.28123 0.00000 0.24811 0.34678 \n155 0.74719 0.14866 0.62994 0.06912 \n156 0.06831 0.23442 0.15747 0.04320 \n\n Trust (Government Corruption) Generosity Dystopia Residual \n0 0.44453 0.36171 2.73939 \n1 0.41203 0.28083 2.69463 \n2 0.14975 0.47678 2.83137 \n3 0.35776 0.37895 2.66465 \n4 0.41004 0.25492 2.82596 \n.. ... ... ... \n152 0.06681 0.20180 2.10812 \n153 0.07112 0.31268 2.14558 \n154 0.11587 0.17517 2.13540 \n155 0.17233 0.48397 0.81789 \n156 0.09419 0.20290 2.10404 \n\n[157 rows x 13 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Country</th>\n <th>Region</th>\n <th>Happiness Rank</th>\n <th>Happiness Score</th>\n <th>Lower Confidence Interval</th>\n <th>Upper Confidence Interval</th>\n <th>Economy (GDP per Capita)</th>\n <th>Family</th>\n <th>Health (Life Expectancy)</th>\n <th>Freedom</th>\n <th>Trust (Government Corruption)</th>\n <th>Generosity</th>\n <th>Dystopia Residual</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Denmark</td>\n <td>Western Europe</td>\n <td>1</td>\n <td>7.526</td>\n <td>7.460</td>\n <td>7.592</td>\n <td>1.44178</td>\n <td>1.16374</td>\n <td>0.79504</td>\n <td>0.57941</td>\n <td>0.44453</td>\n <td>0.36171</td>\n <td>2.73939</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Switzerland</td>\n <td>Western Europe</td>\n <td>2</td>\n <td>7.509</td>\n <td>7.428</td>\n <td>7.590</td>\n <td>1.52733</td>\n <td>1.14524</td>\n <td>0.86303</td>\n <td>0.58557</td>\n <td>0.41203</td>\n <td>0.28083</td>\n <td>2.69463</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Iceland</td>\n <td>Western Europe</td>\n <td>3</td>\n <td>7.501</td>\n <td>7.333</td>\n <td>7.669</td>\n <td>1.42666</td>\n <td>1.18326</td>\n <td>0.86733</td>\n <td>0.56624</td>\n <td>0.14975</td>\n <td>0.47678</td>\n <td>2.83137</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Norway</td>\n <td>Western Europe</td>\n <td>4</td>\n <td>7.498</td>\n <td>7.421</td>\n <td>7.575</td>\n <td>1.57744</td>\n <td>1.12690</td>\n <td>0.79579</td>\n <td>0.59609</td>\n <td>0.35776</td>\n <td>0.37895</td>\n <td>2.66465</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Finland</td>\n <td>Western Europe</td>\n <td>5</td>\n <td>7.413</td>\n <td>7.351</td>\n <td>7.475</td>\n <td>1.40598</td>\n <td>1.13464</td>\n <td>0.81091</td>\n <td>0.57104</td>\n <td>0.41004</td>\n <td>0.25492</td>\n <td>2.82596</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>152</th>\n <td>Benin</td>\n <td>Sub-Saharan Africa</td>\n <td>153</td>\n <td>3.484</td>\n <td>3.404</td>\n <td>3.564</td>\n <td>0.39499</td>\n <td>0.10419</td>\n <td>0.21028</td>\n <td>0.39747</td>\n <td>0.06681</td>\n <td>0.20180</td>\n <td>2.10812</td>\n </tr>\n <tr>\n <th>153</th>\n <td>Afghanistan</td>\n <td>Southern Asia</td>\n <td>154</td>\n <td>3.360</td>\n <td>3.288</td>\n <td>3.432</td>\n <td>0.38227</td>\n <td>0.11037</td>\n <td>0.17344</td>\n <td>0.16430</td>\n <td>0.07112</td>\n <td>0.31268</td>\n <td>2.14558</td>\n </tr>\n <tr>\n <th>154</th>\n <td>Togo</td>\n <td>Sub-Saharan Africa</td>\n <td>155</td>\n <td>3.303</td>\n <td>3.192</td>\n <td>3.414</td>\n <td>0.28123</td>\n <td>0.00000</td>\n <td>0.24811</td>\n <td>0.34678</td>\n <td>0.11587</td>\n <td>0.17517</td>\n <td>2.13540</td>\n </tr>\n <tr>\n <th>155</th>\n <td>Syria</td>\n <td>Middle East and Northern Africa</td>\n <td>156</td>\n <td>3.069</td>\n <td>2.936</td>\n <td>3.202</td>\n <td>0.74719</td>\n <td>0.14866</td>\n <td>0.62994</td>\n <td>0.06912</td>\n <td>0.17233</td>\n <td>0.48397</td>\n <td>0.81789</td>\n </tr>\n <tr>\n <th>156</th>\n <td>Burundi</td>\n <td>Sub-Saharan Africa</td>\n <td>157</td>\n <td>2.905</td>\n <td>2.732</td>\n <td>3.078</td>\n <td>0.06831</td>\n <td>0.23442</td>\n <td>0.15747</td>\n <td>0.04320</td>\n <td>0.09419</td>\n <td>0.20290</td>\n <td>2.10404</td>\n </tr>\n </tbody>\n</table>\n<p>157 rows × 13 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Labels are unified, missing column are highlighted with yellow"},{"metadata":{"trusted":true},"cell_type":"code","source":"isnull_color = 'yellow'\nmissing_cols.style.applymap(lambda x: 'background-color: %s' % isnull_color if pd.isnull(x) else '')","execution_count":33,"outputs":[{"output_type":"execute_result","execution_count":33,"data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7fcf4e067950>","text/html":"<style type=\"text/css\" >\n#T_7d6b8744_2a57_11eb_9339_0242ac130202row1_col4,#T_7d6b8744_2a57_11eb_9339_0242ac130202row9_col0,#T_7d6b8744_2a57_11eb_9339_0242ac130202row9_col1,#T_7d6b8744_2a57_11eb_9339_0242ac130202row9_col4,#T_7d6b8744_2a57_11eb_9339_0242ac130202row10_col3,#T_7d6b8744_2a57_11eb_9339_0242ac130202row10_col4,#T_7d6b8744_2a57_11eb_9339_0242ac130202row11_col2,#T_7d6b8744_2a57_11eb_9339_0242ac130202row11_col3,#T_7d6b8744_2a57_11eb_9339_0242ac130202row11_col4,#T_7d6b8744_2a57_11eb_9339_0242ac130202row13_col0,#T_7d6b8744_2a57_11eb_9339_0242ac130202row13_col1,#T_7d6b8744_2a57_11eb_9339_0242ac130202row13_col4{\n background-color: yellow;\n }</style><table id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >2015</th> <th class=\"col_heading level0 col1\" >2016</th> <th class=\"col_heading level0 col2\" >2017</th> <th class=\"col_heading level0 col3\" >2018</th> <th class=\"col_heading level0 col4\" >2019</th> </tr></thead><tbody>\n <tr>\n <th id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >Country</th>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row0_col0\" class=\"data row0 col0\" >Country</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row0_col1\" class=\"data row0 col1\" >Country</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row0_col2\" class=\"data row0 col2\" >Country</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row0_col3\" class=\"data row0 col3\" >Country</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row0_col4\" class=\"data row0 col4\" >Country</td>\n </tr>\n <tr>\n <th id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >Dystopia Residual</th>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row1_col0\" class=\"data row1 col0\" >Dystopia Residual</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row1_col1\" class=\"data row1 col1\" >Dystopia Residual</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row1_col2\" class=\"data row1 col2\" >Dystopia Residual</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row1_col3\" class=\"data row1 col3\" >Dystopia Residual</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row1_col4\" class=\"data row1 col4\" >nan</td>\n </tr>\n <tr>\n <th id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >Economy (GDP per Capita)</th>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row2_col0\" class=\"data row2 col0\" >Economy (GDP per Capita)</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row2_col1\" class=\"data row2 col1\" >Economy (GDP per Capita)</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row2_col2\" class=\"data row2 col2\" >Economy (GDP per Capita)</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row2_col3\" class=\"data row2 col3\" >Economy (GDP per Capita)</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row2_col4\" class=\"data row2 col4\" >Economy (GDP per Capita)</td>\n </tr>\n <tr>\n <th id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >Family</th>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row3_col0\" class=\"data row3 col0\" >Family</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row3_col1\" class=\"data row3 col1\" >Family</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row3_col2\" class=\"data row3 col2\" >Family</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row3_col3\" class=\"data row3 col3\" >Family</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row3_col4\" class=\"data row3 col4\" >Family</td>\n </tr>\n <tr>\n <th id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >Freedom</th>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row4_col0\" class=\"data row4 col0\" >Freedom</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row4_col1\" class=\"data row4 col1\" >Freedom</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row4_col2\" class=\"data row4 col2\" >Freedom</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row4_col3\" class=\"data row4 col3\" >Freedom</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row4_col4\" class=\"data row4 col4\" >Freedom</td>\n </tr>\n <tr>\n <th id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202level0_row5\" class=\"row_heading level0 row5\" >Generosity</th>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row5_col0\" class=\"data row5 col0\" >Generosity</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row5_col1\" class=\"data row5 col1\" >Generosity</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row5_col2\" class=\"data row5 col2\" >Generosity</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row5_col3\" class=\"data row5 col3\" >Generosity</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row5_col4\" class=\"data row5 col4\" >Generosity</td>\n </tr>\n <tr>\n <th id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202level0_row6\" class=\"row_heading level0 row6\" >Happiness Rank</th>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row6_col0\" class=\"data row6 col0\" >Happiness Rank</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row6_col1\" class=\"data row6 col1\" >Happiness Rank</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row6_col2\" class=\"data row6 col2\" >Happiness Rank</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row6_col3\" class=\"data row6 col3\" >Happiness Rank</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row6_col4\" class=\"data row6 col4\" >Happiness Rank</td>\n </tr>\n <tr>\n <th id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202level0_row7\" class=\"row_heading level0 row7\" >Happiness Score</th>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row7_col0\" class=\"data row7 col0\" >Happiness Score</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row7_col1\" class=\"data row7 col1\" >Happiness Score</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row7_col2\" class=\"data row7 col2\" >Happiness Score</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row7_col3\" class=\"data row7 col3\" >Happiness Score</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row7_col4\" class=\"data row7 col4\" >Happiness Score</td>\n </tr>\n <tr>\n <th id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202level0_row8\" class=\"row_heading level0 row8\" >Health (Life Expectancy)</th>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row8_col0\" class=\"data row8 col0\" >Health (Life Expectancy)</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row8_col1\" class=\"data row8 col1\" >Health (Life Expectancy)</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row8_col2\" class=\"data row8 col2\" >Health (Life Expectancy)</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row8_col3\" class=\"data row8 col3\" >Health (Life Expectancy)</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row8_col4\" class=\"data row8 col4\" >Health (Life Expectancy)</td>\n </tr>\n <tr>\n <th id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202level0_row9\" class=\"row_heading level0 row9\" >Lower Confidence Interval</th>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row9_col0\" class=\"data row9 col0\" >nan</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row9_col1\" class=\"data row9 col1\" >nan</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row9_col2\" class=\"data row9 col2\" >Lower Confidence Interval</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row9_col3\" class=\"data row9 col3\" >Lower Confidence Interval</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row9_col4\" class=\"data row9 col4\" >nan</td>\n </tr>\n <tr>\n <th id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202level0_row10\" class=\"row_heading level0 row10\" >Region</th>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row10_col0\" class=\"data row10 col0\" >Region</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row10_col1\" class=\"data row10 col1\" >Region</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row10_col2\" class=\"data row10 col2\" >Region</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row10_col3\" class=\"data row10 col3\" >nan</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row10_col4\" class=\"data row10 col4\" >nan</td>\n </tr>\n <tr>\n <th id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202level0_row11\" class=\"row_heading level0 row11\" >Standard Error</th>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row11_col0\" class=\"data row11 col0\" >Standard Error</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row11_col1\" class=\"data row11 col1\" >Standard Error</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row11_col2\" class=\"data row11 col2\" >nan</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row11_col3\" class=\"data row11 col3\" >nan</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row11_col4\" class=\"data row11 col4\" >nan</td>\n </tr>\n <tr>\n <th id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202level0_row12\" class=\"row_heading level0 row12\" >Trust (Government Corruption)</th>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row12_col0\" class=\"data row12 col0\" >Trust (Government Corruption)</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row12_col1\" class=\"data row12 col1\" >Trust (Government Corruption)</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row12_col2\" class=\"data row12 col2\" >Trust (Government Corruption)</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row12_col3\" class=\"data row12 col3\" >Trust (Government Corruption)</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row12_col4\" class=\"data row12 col4\" >Trust (Government Corruption)</td>\n </tr>\n <tr>\n <th id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202level0_row13\" class=\"row_heading level0 row13\" >Upper Confidence Interval</th>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row13_col0\" class=\"data row13 col0\" >nan</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row13_col1\" class=\"data row13 col1\" >nan</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row13_col2\" class=\"data row13 col2\" >Upper Confidence Interval</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row13_col3\" class=\"data row13 col3\" >Upper Confidence Interval</td>\n <td id=\"T_7d6b8744_2a57_11eb_9339_0242ac130202row13_col4\" class=\"data row13 col4\" >nan</td>\n </tr>\n </tbody></table>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"raw_2015['Year'] = 2015\nraw_2016['Year'] = 2016\nraw_2017['Year'] = 2017\nraw_2018['Year'] = 2018\nraw_2019['Year'] = 2019","execution_count":34,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## 2015-2019 years in one dataframe with unified country names"},{"metadata":{"trusted":true},"cell_type":"code","source":"append_data = raw_2015.append((raw_2016, raw_2017, raw_2018, raw_2019), ignore_index=True)\n\nappend_data = append_data.replace(['Hong Kong S.A.R., China','Somaliland Region' ,'Somaliland region','Taiwan Province of China','Trinidad & Tobago'],\n ['Hong Kong','Somalia','Somalia','Taiwan','Trinidad and Tobago'])\nappend_data","execution_count":35,"outputs":[{"output_type":"execute_result","execution_count":35,"data":{"text/plain":" Country Region Happiness Rank \\\n0 Switzerland Western Europe 1 \n1 Iceland Western Europe 2 \n2 Denmark Western Europe 3 \n3 Norway Western Europe 4 \n4 Canada North America 5 \n.. ... ... ... \n779 Rwanda NaN 152 \n780 Tanzania NaN 153 \n781 Afghanistan NaN 154 \n782 Central African Republic NaN 155 \n783 South Sudan NaN 156 \n\n Happiness Score Standard Error Economy (GDP per Capita) Family \\\n0 7.587 0.03411 1.39651 1.34951 \n1 7.561 0.04884 1.30232 1.40223 \n2 7.527 0.03328 1.32548 1.36058 \n3 7.522 0.03880 1.45900 1.33095 \n4 7.427 0.03553 1.32629 1.32261 \n.. ... ... ... ... \n779 3.334 NaN 0.35900 0.71100 \n780 3.231 NaN 0.47600 0.88500 \n781 3.203 NaN 0.35000 0.51700 \n782 3.083 NaN 0.02600 0.00000 \n783 2.853 NaN 0.30600 0.57500 \n\n Health (Life Expectancy) Freedom Trust (Government Corruption) \\\n0 0.94143 0.66557 0.41978 \n1 0.94784 0.62877 0.14145 \n2 0.87464 0.64938 0.48357 \n3 0.88521 0.66973 0.36503 \n4 0.90563 0.63297 0.32957 \n.. ... ... ... \n779 0.61400 0.55500 0.41100 \n780 0.49900 0.41700 0.14700 \n781 0.36100 0.00000 0.02500 \n782 0.10500 0.22500 0.03500 \n783 0.29500 0.01000 0.09100 \n\n Generosity Dystopia Residual Year Lower Confidence Interval \\\n0 0.29678 2.51738 2015 NaN \n1 0.43630 2.70201 2015 NaN \n2 0.34139 2.49204 2015 NaN \n3 0.34699 2.46531 2015 NaN \n4 0.45811 2.45176 2015 NaN \n.. ... ... ... ... \n779 0.21700 NaN 2019 NaN \n780 0.27600 NaN 2019 NaN \n781 0.15800 NaN 2019 NaN \n782 0.23500 NaN 2019 NaN \n783 0.20200 NaN 2019 NaN \n\n Upper Confidence Interval \n0 NaN \n1 NaN \n2 NaN \n3 NaN \n4 NaN \n.. ... \n779 NaN \n780 NaN \n781 NaN \n782 NaN \n783 NaN \n\n[784 rows x 15 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Country</th>\n <th>Region</th>\n <th>Happiness Rank</th>\n <th>Happiness Score</th>\n <th>Standard Error</th>\n <th>Economy (GDP per Capita)</th>\n <th>Family</th>\n <th>Health (Life Expectancy)</th>\n <th>Freedom</th>\n <th>Trust (Government Corruption)</th>\n <th>Generosity</th>\n <th>Dystopia Residual</th>\n <th>Year</th>\n <th>Lower Confidence Interval</th>\n <th>Upper Confidence Interval</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Switzerland</td>\n <td>Western Europe</td>\n <td>1</td>\n <td>7.587</td>\n <td>0.03411</td>\n <td>1.39651</td>\n <td>1.34951</td>\n <td>0.94143</td>\n <td>0.66557</td>\n <td>0.41978</td>\n <td>0.29678</td>\n <td>2.51738</td>\n <td>2015</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Iceland</td>\n <td>Western Europe</td>\n <td>2</td>\n <td>7.561</td>\n <td>0.04884</td>\n <td>1.30232</td>\n <td>1.40223</td>\n <td>0.94784</td>\n <td>0.62877</td>\n <td>0.14145</td>\n <td>0.43630</td>\n <td>2.70201</td>\n <td>2015</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Denmark</td>\n <td>Western Europe</td>\n <td>3</td>\n <td>7.527</td>\n <td>0.03328</td>\n <td>1.32548</td>\n <td>1.36058</td>\n <td>0.87464</td>\n <td>0.64938</td>\n <td>0.48357</td>\n <td>0.34139</td>\n <td>2.49204</td>\n <td>2015</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Norway</td>\n <td>Western Europe</td>\n <td>4</td>\n <td>7.522</td>\n <td>0.03880</td>\n <td>1.45900</td>\n <td>1.33095</td>\n <td>0.88521</td>\n <td>0.66973</td>\n <td>0.36503</td>\n <td>0.34699</td>\n <td>2.46531</td>\n <td>2015</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Canada</td>\n <td>North America</td>\n <td>5</td>\n <td>7.427</td>\n <td>0.03553</td>\n <td>1.32629</td>\n <td>1.32261</td>\n <td>0.90563</td>\n <td>0.63297</td>\n <td>0.32957</td>\n <td>0.45811</td>\n <td>2.45176</td>\n <td>2015</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>779</th>\n <td>Rwanda</td>\n <td>NaN</td>\n <td>152</td>\n <td>3.334</td>\n <td>NaN</td>\n <td>0.35900</td>\n <td>0.71100</td>\n <td>0.61400</td>\n <td>0.55500</td>\n <td>0.41100</td>\n <td>0.21700</td>\n <td>NaN</td>\n <td>2019</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>780</th>\n <td>Tanzania</td>\n <td>NaN</td>\n <td>153</td>\n <td>3.231</td>\n <td>NaN</td>\n <td>0.47600</td>\n <td>0.88500</td>\n <td>0.49900</td>\n <td>0.41700</td>\n <td>0.14700</td>\n <td>0.27600</td>\n <td>NaN</td>\n <td>2019</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>781</th>\n <td>Afghanistan</td>\n <td>NaN</td>\n <td>154</td>\n <td>3.203</td>\n <td>NaN</td>\n <td>0.35000</td>\n <td>0.51700</td>\n <td>0.36100</td>\n <td>0.00000</td>\n <td>0.02500</td>\n <td>0.15800</td>\n <td>NaN</td>\n <td>2019</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>782</th>\n <td>Central African Republic</td>\n <td>NaN</td>\n <td>155</td>\n <td>3.083</td>\n <td>NaN</td>\n <td>0.02600</td>\n <td>0.00000</td>\n <td>0.10500</td>\n <td>0.22500</td>\n <td>0.03500</td>\n <td>0.23500</td>\n <td>NaN</td>\n <td>2019</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>783</th>\n <td>South Sudan</td>\n <td>NaN</td>\n <td>156</td>\n <td>2.853</td>\n <td>NaN</td>\n <td>0.30600</td>\n <td>0.57500</td>\n <td>0.29500</td>\n <td>0.01000</td>\n <td>0.09100</td>\n <td>0.20200</td>\n <td>NaN</td>\n <td>2019</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n </tbody>\n</table>\n<p>784 rows × 15 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"index_country = append_data.set_index('Country')\nindex_country","execution_count":36,"outputs":[{"output_type":"execute_result","execution_count":36,"data":{"text/plain":" Region Happiness Rank Happiness Score \\\nCountry \nSwitzerland Western Europe 1 7.587 \nIceland Western Europe 2 7.561 \nDenmark Western Europe 3 7.527 \nNorway Western Europe 4 7.522 \nCanada North America 5 7.427 \n... ... ... ... \nRwanda NaN 152 3.334 \nTanzania NaN 153 3.231 \nAfghanistan NaN 154 3.203 \nCentral African Republic NaN 155 3.083 \nSouth Sudan NaN 156 2.853 \n\n Standard Error Economy (GDP per Capita) Family \\\nCountry \nSwitzerland 0.03411 1.39651 1.34951 \nIceland 0.04884 1.30232 1.40223 \nDenmark 0.03328 1.32548 1.36058 \nNorway 0.03880 1.45900 1.33095 \nCanada 0.03553 1.32629 1.32261 \n... ... ... ... \nRwanda NaN 0.35900 0.71100 \nTanzania NaN 0.47600 0.88500 \nAfghanistan NaN 0.35000 0.51700 \nCentral African Republic NaN 0.02600 0.00000 \nSouth Sudan NaN 0.30600 0.57500 \n\n Health (Life Expectancy) Freedom \\\nCountry \nSwitzerland 0.94143 0.66557 \nIceland 0.94784 0.62877 \nDenmark 0.87464 0.64938 \nNorway 0.88521 0.66973 \nCanada 0.90563 0.63297 \n... ... ... \nRwanda 0.61400 0.55500 \nTanzania 0.49900 0.41700 \nAfghanistan 0.36100 0.00000 \nCentral African Republic 0.10500 0.22500 \nSouth Sudan 0.29500 0.01000 \n\n Trust (Government Corruption) Generosity \\\nCountry \nSwitzerland 0.41978 0.29678 \nIceland 0.14145 0.43630 \nDenmark 0.48357 0.34139 \nNorway 0.36503 0.34699 \nCanada 0.32957 0.45811 \n... ... ... \nRwanda 0.41100 0.21700 \nTanzania 0.14700 0.27600 \nAfghanistan 0.02500 0.15800 \nCentral African Republic 0.03500 0.23500 \nSouth Sudan 0.09100 0.20200 \n\n Dystopia Residual Year Lower Confidence Interval \\\nCountry \nSwitzerland 2.51738 2015 NaN \nIceland 2.70201 2015 NaN \nDenmark 2.49204 2015 NaN \nNorway 2.46531 2015 NaN \nCanada 2.45176 2015 NaN \n... ... ... ... \nRwanda NaN 2019 NaN \nTanzania NaN 2019 NaN \nAfghanistan NaN 2019 NaN \nCentral African Republic NaN 2019 NaN \nSouth Sudan NaN 2019 NaN \n\n Upper Confidence Interval \nCountry \nSwitzerland NaN \nIceland NaN \nDenmark NaN \nNorway NaN \nCanada NaN \n... ... \nRwanda NaN \nTanzania NaN \nAfghanistan NaN \nCentral African Republic NaN \nSouth Sudan NaN \n\n[784 rows x 14 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Region</th>\n <th>Happiness Rank</th>\n <th>Happiness Score</th>\n <th>Standard Error</th>\n <th>Economy (GDP per Capita)</th>\n <th>Family</th>\n <th>Health (Life Expectancy)</th>\n <th>Freedom</th>\n <th>Trust (Government Corruption)</th>\n <th>Generosity</th>\n <th>Dystopia Residual</th>\n <th>Year</th>\n <th>Lower Confidence Interval</th>\n <th>Upper Confidence Interval</th>\n </tr>\n <tr>\n <th>Country</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>Switzerland</th>\n <td>Western Europe</td>\n <td>1</td>\n <td>7.587</td>\n <td>0.03411</td>\n <td>1.39651</td>\n <td>1.34951</td>\n <td>0.94143</td>\n <td>0.66557</td>\n <td>0.41978</td>\n <td>0.29678</td>\n <td>2.51738</td>\n <td>2015</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>Iceland</th>\n <td>Western Europe</td>\n <td>2</td>\n <td>7.561</td>\n <td>0.04884</td>\n <td>1.30232</td>\n <td>1.40223</td>\n <td>0.94784</td>\n <td>0.62877</td>\n <td>0.14145</td>\n <td>0.43630</td>\n <td>2.70201</td>\n <td>2015</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>Denmark</th>\n <td>Western Europe</td>\n <td>3</td>\n <td>7.527</td>\n <td>0.03328</td>\n <td>1.32548</td>\n <td>1.36058</td>\n <td>0.87464</td>\n <td>0.64938</td>\n <td>0.48357</td>\n <td>0.34139</td>\n <td>2.49204</td>\n <td>2015</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>Norway</th>\n <td>Western Europe</td>\n <td>4</td>\n <td>7.522</td>\n <td>0.03880</td>\n <td>1.45900</td>\n <td>1.33095</td>\n <td>0.88521</td>\n <td>0.66973</td>\n <td>0.36503</td>\n <td>0.34699</td>\n <td>2.46531</td>\n <td>2015</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>Canada</th>\n <td>North America</td>\n <td>5</td>\n <td>7.427</td>\n <td>0.03553</td>\n <td>1.32629</td>\n <td>1.32261</td>\n <td>0.90563</td>\n <td>0.63297</td>\n <td>0.32957</td>\n <td>0.45811</td>\n <td>2.45176</td>\n <td>2015</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>Rwanda</th>\n <td>NaN</td>\n <td>152</td>\n <td>3.334</td>\n <td>NaN</td>\n <td>0.35900</td>\n <td>0.71100</td>\n <td>0.61400</td>\n <td>0.55500</td>\n <td>0.41100</td>\n <td>0.21700</td>\n <td>NaN</td>\n <td>2019</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>Tanzania</th>\n <td>NaN</td>\n <td>153</td>\n <td>3.231</td>\n <td>NaN</td>\n <td>0.47600</td>\n <td>0.88500</td>\n <td>0.49900</td>\n <td>0.41700</td>\n <td>0.14700</td>\n <td>0.27600</td>\n <td>NaN</td>\n <td>2019</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>Afghanistan</th>\n <td>NaN</td>\n <td>154</td>\n <td>3.203</td>\n <td>NaN</td>\n <td>0.35000</td>\n <td>0.51700</td>\n <td>0.36100</td>\n <td>0.00000</td>\n <td>0.02500</td>\n <td>0.15800</td>\n <td>NaN</td>\n <td>2019</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>Central African Republic</th>\n <td>NaN</td>\n <td>155</td>\n <td>3.083</td>\n <td>NaN</td>\n <td>0.02600</td>\n <td>0.00000</td>\n <td>0.10500</td>\n <td>0.22500</td>\n <td>0.03500</td>\n <td>0.23500</td>\n <td>NaN</td>\n <td>2019</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>South Sudan</th>\n <td>NaN</td>\n <td>156</td>\n <td>2.853</td>\n <td>NaN</td>\n <td>0.30600</td>\n <td>0.57500</td>\n <td>0.29500</td>\n <td>0.01000</td>\n <td>0.09100</td>\n <td>0.20200</td>\n <td>NaN</td>\n <td>2019</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n </tbody>\n</table>\n<p>784 rows × 14 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"region_2015= raw_2015.set_index('Country')\nindex_country = index_country.join(region_2015['Region'], how='left', rsuffix =\"_2\")\nindex_country","execution_count":37,"outputs":[{"output_type":"execute_result","execution_count":37,"data":{"text/plain":" Region Happiness Rank Happiness Score \\\nCountry \nAfghanistan Southern Asia 153 3.575 \nAfghanistan Southern Asia 153 3.575 \nAfghanistan Southern Asia 154 3.360 \nAfghanistan NaN 141 3.794 \nAfghanistan NaN 154 3.203 \n... ... ... ... \nZimbabwe Sub-Saharan Africa 115 4.610 \nZimbabwe Sub-Saharan Africa 115 4.610 \nZimbabwe Sub-Saharan Africa 131 4.193 \nZimbabwe NaN 138 3.875 \nZimbabwe NaN 146 3.663 \n\n Standard Error Economy (GDP per Capita) Family \\\nCountry \nAfghanistan 0.03084 0.319820 0.302850 \nAfghanistan 0.03084 0.319820 0.302850 \nAfghanistan NaN 0.382270 0.110370 \nAfghanistan NaN 0.401477 0.581543 \nAfghanistan NaN 0.350000 0.517000 \n... ... ... ... \nZimbabwe 0.04290 0.271000 1.032760 \nZimbabwe 0.04290 0.271000 1.032760 \nZimbabwe NaN 0.350410 0.714780 \nZimbabwe NaN 0.375847 1.083096 \nZimbabwe NaN 0.366000 1.114000 \n\n Health (Life Expectancy) Freedom \\\nCountry \nAfghanistan 0.303350 0.234140 \nAfghanistan 0.303350 0.234140 \nAfghanistan 0.173440 0.164300 \nAfghanistan 0.180747 0.106180 \nAfghanistan 0.361000 0.000000 \n... ... ... \nZimbabwe 0.334750 0.258610 \nZimbabwe 0.334750 0.258610 \nZimbabwe 0.159500 0.254290 \nZimbabwe 0.196764 0.336384 \nZimbabwe 0.433000 0.361000 \n\n Trust (Government Corruption) Generosity Dystopia Residual \\\nCountry \nAfghanistan 0.097190 0.365100 1.952100 \nAfghanistan 0.097190 0.365100 1.952100 \nAfghanistan 0.071120 0.312680 2.145580 \nAfghanistan 0.061158 0.311871 2.150801 \nAfghanistan 0.025000 0.158000 NaN \n... ... ... ... \nZimbabwe 0.080790 0.189870 2.441910 \nZimbabwe 0.080790 0.189870 2.441910 \nZimbabwe 0.085820 0.185030 2.442700 \nZimbabwe 0.095375 0.189143 1.597970 \nZimbabwe 0.089000 0.151000 NaN \n\n Year Lower Confidence Interval Upper Confidence Interval \\\nCountry \nAfghanistan 2015 NaN NaN \nAfghanistan 2016 NaN NaN \nAfghanistan 2017 3.288000 3.432000 \nAfghanistan 2018 3.714338 3.873661 \nAfghanistan 2019 NaN NaN \n... ... ... ... \nZimbabwe 2015 NaN NaN \nZimbabwe 2016 NaN NaN \nZimbabwe 2017 4.101000 4.285000 \nZimbabwe 2018 3.771300 3.978700 \nZimbabwe 2019 NaN NaN \n\n Region_2 \nCountry \nAfghanistan Southern Asia \nAfghanistan Southern Asia \nAfghanistan Southern Asia \nAfghanistan Southern Asia \nAfghanistan Southern Asia \n... ... \nZimbabwe Sub-Saharan Africa \nZimbabwe Sub-Saharan Africa \nZimbabwe Sub-Saharan Africa \nZimbabwe Sub-Saharan Africa \nZimbabwe Sub-Saharan Africa \n\n[784 rows x 15 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Region</th>\n <th>Happiness Rank</th>\n <th>Happiness Score</th>\n <th>Standard Error</th>\n <th>Economy (GDP per Capita)</th>\n <th>Family</th>\n <th>Health (Life Expectancy)</th>\n <th>Freedom</th>\n <th>Trust (Government Corruption)</th>\n <th>Generosity</th>\n <th>Dystopia Residual</th>\n <th>Year</th>\n <th>Lower Confidence Interval</th>\n <th>Upper Confidence Interval</th>\n <th>Region_2</th>\n </tr>\n <tr>\n <th>Country</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>Afghanistan</th>\n <td>Southern Asia</td>\n <td>153</td>\n <td>3.575</td>\n <td>0.03084</td>\n <td>0.319820</td>\n <td>0.302850</td>\n <td>0.303350</td>\n <td>0.234140</td>\n <td>0.097190</td>\n <td>0.365100</td>\n <td>1.952100</td>\n <td>2015</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Southern Asia</td>\n </tr>\n <tr>\n <th>Afghanistan</th>\n <td>Southern Asia</td>\n <td>153</td>\n <td>3.575</td>\n <td>0.03084</td>\n <td>0.319820</td>\n <td>0.302850</td>\n <td>0.303350</td>\n <td>0.234140</td>\n <td>0.097190</td>\n <td>0.365100</td>\n <td>1.952100</td>\n <td>2016</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Southern Asia</td>\n </tr>\n <tr>\n <th>Afghanistan</th>\n <td>Southern Asia</td>\n <td>154</td>\n <td>3.360</td>\n <td>NaN</td>\n <td>0.382270</td>\n <td>0.110370</td>\n <td>0.173440</td>\n <td>0.164300</td>\n <td>0.071120</td>\n <td>0.312680</td>\n <td>2.145580</td>\n <td>2017</td>\n <td>3.288000</td>\n <td>3.432000</td>\n <td>Southern Asia</td>\n </tr>\n <tr>\n <th>Afghanistan</th>\n <td>NaN</td>\n <td>141</td>\n <td>3.794</td>\n <td>NaN</td>\n <td>0.401477</td>\n <td>0.581543</td>\n <td>0.180747</td>\n <td>0.106180</td>\n <td>0.061158</td>\n <td>0.311871</td>\n <td>2.150801</td>\n <td>2018</td>\n <td>3.714338</td>\n <td>3.873661</td>\n <td>Southern Asia</td>\n </tr>\n <tr>\n <th>Afghanistan</th>\n <td>NaN</td>\n <td>154</td>\n <td>3.203</td>\n <td>NaN</td>\n <td>0.350000</td>\n <td>0.517000</td>\n <td>0.361000</td>\n <td>0.000000</td>\n <td>0.025000</td>\n <td>0.158000</td>\n <td>NaN</td>\n <td>2019</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Southern Asia</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>Zimbabwe</th>\n <td>Sub-Saharan Africa</td>\n <td>115</td>\n <td>4.610</td>\n <td>0.04290</td>\n <td>0.271000</td>\n <td>1.032760</td>\n <td>0.334750</td>\n <td>0.258610</td>\n <td>0.080790</td>\n <td>0.189870</td>\n <td>2.441910</td>\n <td>2015</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Sub-Saharan Africa</td>\n </tr>\n <tr>\n <th>Zimbabwe</th>\n <td>Sub-Saharan Africa</td>\n <td>115</td>\n <td>4.610</td>\n <td>0.04290</td>\n <td>0.271000</td>\n <td>1.032760</td>\n <td>0.334750</td>\n <td>0.258610</td>\n <td>0.080790</td>\n <td>0.189870</td>\n <td>2.441910</td>\n <td>2016</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Sub-Saharan Africa</td>\n </tr>\n <tr>\n <th>Zimbabwe</th>\n <td>Sub-Saharan Africa</td>\n <td>131</td>\n <td>4.193</td>\n <td>NaN</td>\n <td>0.350410</td>\n <td>0.714780</td>\n <td>0.159500</td>\n <td>0.254290</td>\n <td>0.085820</td>\n <td>0.185030</td>\n <td>2.442700</td>\n <td>2017</td>\n <td>4.101000</td>\n <td>4.285000</td>\n <td>Sub-Saharan Africa</td>\n </tr>\n <tr>\n <th>Zimbabwe</th>\n <td>NaN</td>\n <td>138</td>\n <td>3.875</td>\n <td>NaN</td>\n <td>0.375847</td>\n <td>1.083096</td>\n <td>0.196764</td>\n <td>0.336384</td>\n <td>0.095375</td>\n <td>0.189143</td>\n <td>1.597970</td>\n <td>2018</td>\n <td>3.771300</td>\n <td>3.978700</td>\n <td>Sub-Saharan Africa</td>\n </tr>\n <tr>\n <th>Zimbabwe</th>\n <td>NaN</td>\n <td>146</td>\n <td>3.663</td>\n <td>NaN</td>\n <td>0.366000</td>\n <td>1.114000</td>\n <td>0.433000</td>\n <td>0.361000</td>\n <td>0.089000</td>\n <td>0.151000</td>\n <td>NaN</td>\n <td>2019</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Sub-Saharan Africa</td>\n </tr>\n </tbody>\n</table>\n<p>784 rows × 15 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"index_country.Region_2","execution_count":39,"outputs":[{"output_type":"execute_result","execution_count":39,"data":{"text/plain":"Country\nAfghanistan Southern Asia\nAfghanistan Southern Asia\nAfghanistan Southern Asia\nAfghanistan Southern Asia\nAfghanistan Southern Asia\n ... \nZimbabwe Sub-Saharan Africa\nZimbabwe Sub-Saharan Africa\nZimbabwe Sub-Saharan Africa\nZimbabwe Sub-Saharan Africa\nZimbabwe Sub-Saharan Africa\nName: Region_2, Length: 784, dtype: object"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"m = index_country[index_country['Region_2'].isna()]\nm.index.unique()","execution_count":15,"outputs":[{"output_type":"execute_result","execution_count":15,"data":{"text/plain":"Index(['Belize', 'Gambia', 'Namibia', 'North Macedonia', 'Northern Cyprus',\n 'Puerto Rico', 'Somalia', 'South Sudan'],\n dtype='object', name='Country')"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"adding Region to each row"},{"metadata":{"trusted":true},"cell_type":"code","source":"index_country.columns","execution_count":40,"outputs":[{"output_type":"execute_result","execution_count":40,"data":{"text/plain":"Index(['Region', 'Happiness Rank', 'Happiness Score', 'Standard Error',\n 'Economy (GDP per Capita)', 'Family', 'Health (Life Expectancy)',\n 'Freedom', 'Trust (Government Corruption)', 'Generosity',\n 'Dystopia Residual', 'Year', 'Lower Confidence Interval',\n 'Upper Confidence Interval', 'Region_2'],\n dtype='object')"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#index_country = \nindex_country.Region_2.loc['Belize'] = 'Latin America and Caribbean'","execution_count":42,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n iloc._setitem_with_indexer(indexer, value)\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"index_country.Region_2.loc['Belize']","execution_count":43,"outputs":[{"output_type":"execute_result","execution_count":43,"data":{"text/plain":"Country\nBelize Latin America and Caribbean\nBelize Latin America and Caribbean\nName: Region_2, dtype: object"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"index_country.Region_2.loc['Gambia'] = 'Sub-Saharan Africa'\nindex_country.Region_2.loc['Namibia'] = 'Sub-Saharan Africa'\nindex_country.Region_2.loc['North Macedonia'] = 'Central and Eastern Europe'\nindex_country.Region_2.loc['Northern Cyprus'] = 'Western Europe'\nindex_country.Region_2.loc['Puerto Rico'] = 'Latin America and Caribbean'\nindex_country.Region_2.loc['Somalia'] = 'Sub-Saharan Africa'\nindex_country.Region_2.loc['South Sudan'] = 'Sub-Saharan Africa'","execution_count":44,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"index_country[index_country['Region_2'].isna()]","execution_count":45,"outputs":[{"output_type":"execute_result","execution_count":45,"data":{"text/plain":"Empty DataFrame\nColumns: [Region, Happiness Rank, Happiness Score, Standard Error, Economy (GDP per Capita), Family, Health (Life Expectancy), Freedom, Trust (Government Corruption), Generosity, Dystopia Residual, Year, Lower Confidence Interval, Upper Confidence Interval, Region_2]\nIndex: []","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Region</th>\n <th>Happiness Rank</th>\n <th>Happiness Score</th>\n <th>Standard Error</th>\n <th>Economy (GDP per Capita)</th>\n <th>Family</th>\n <th>Health (Life Expectancy)</th>\n <th>Freedom</th>\n <th>Trust (Government Corruption)</th>\n <th>Generosity</th>\n <th>Dystopia Residual</th>\n <th>Year</th>\n <th>Lower Confidence Interval</th>\n <th>Upper Confidence Interval</th>\n <th>Region_2</th>\n </tr>\n <tr>\n <th>Country</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## all regions are filled now"},{"metadata":{"trusted":true},"cell_type":"code","source":"index_country = index_country.drop('Region', axis =1)\n\nindex_country = index_country.replace(to_replace='Southeastern Asia', value='Southern Asia')\nindex_country","execution_count":47,"outputs":[{"output_type":"execute_result","execution_count":47,"data":{"text/plain":" Happiness Rank Happiness Score Standard Error \\\nCountry \nAfghanistan 153 3.575 0.03084 \nAfghanistan 153 3.575 0.03084 \nAfghanistan 154 3.360 NaN \nAfghanistan 141 3.794 NaN \nAfghanistan 154 3.203 NaN \n... ... ... ... \nZimbabwe 115 4.610 0.04290 \nZimbabwe 115 4.610 0.04290 \nZimbabwe 131 4.193 NaN \nZimbabwe 138 3.875 NaN \nZimbabwe 146 3.663 NaN \n\n Economy (GDP per Capita) Family Health (Life Expectancy) \\\nCountry \nAfghanistan 0.319820 0.302850 0.303350 \nAfghanistan 0.319820 0.302850 0.303350 \nAfghanistan 0.382270 0.110370 0.173440 \nAfghanistan 0.401477 0.581543 0.180747 \nAfghanistan 0.350000 0.517000 0.361000 \n... ... ... ... \nZimbabwe 0.271000 1.032760 0.334750 \nZimbabwe 0.271000 1.032760 0.334750 \nZimbabwe 0.350410 0.714780 0.159500 \nZimbabwe 0.375847 1.083096 0.196764 \nZimbabwe 0.366000 1.114000 0.433000 \n\n Freedom Trust (Government Corruption) Generosity \\\nCountry \nAfghanistan 0.234140 0.097190 0.365100 \nAfghanistan 0.234140 0.097190 0.365100 \nAfghanistan 0.164300 0.071120 0.312680 \nAfghanistan 0.106180 0.061158 0.311871 \nAfghanistan 0.000000 0.025000 0.158000 \n... ... ... ... \nZimbabwe 0.258610 0.080790 0.189870 \nZimbabwe 0.258610 0.080790 0.189870 \nZimbabwe 0.254290 0.085820 0.185030 \nZimbabwe 0.336384 0.095375 0.189143 \nZimbabwe 0.361000 0.089000 0.151000 \n\n Dystopia Residual Year Lower Confidence Interval \\\nCountry \nAfghanistan 1.952100 2015 NaN \nAfghanistan 1.952100 2016 NaN \nAfghanistan 2.145580 2017 3.288000 \nAfghanistan 2.150801 2018 3.714338 \nAfghanistan NaN 2019 NaN \n... ... ... ... \nZimbabwe 2.441910 2015 NaN \nZimbabwe 2.441910 2016 NaN \nZimbabwe 2.442700 2017 4.101000 \nZimbabwe 1.597970 2018 3.771300 \nZimbabwe NaN 2019 NaN \n\n Upper Confidence Interval Region_2 \nCountry \nAfghanistan NaN Southern Asia \nAfghanistan NaN Southern Asia \nAfghanistan 3.432000 Southern Asia \nAfghanistan 3.873661 Southern Asia \nAfghanistan NaN Southern Asia \n... ... ... \nZimbabwe NaN Sub-Saharan Africa \nZimbabwe NaN Sub-Saharan Africa \nZimbabwe 4.285000 Sub-Saharan Africa \nZimbabwe 3.978700 Sub-Saharan Africa \nZimbabwe NaN Sub-Saharan Africa \n\n[784 rows x 14 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Happiness Rank</th>\n <th>Happiness Score</th>\n <th>Standard Error</th>\n <th>Economy (GDP per Capita)</th>\n <th>Family</th>\n <th>Health (Life Expectancy)</th>\n <th>Freedom</th>\n <th>Trust (Government Corruption)</th>\n <th>Generosity</th>\n <th>Dystopia Residual</th>\n <th>Year</th>\n <th>Lower Confidence Interval</th>\n <th>Upper Confidence Interval</th>\n <th>Region_2</th>\n </tr>\n <tr>\n <th>Country</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>Afghanistan</th>\n <td>153</td>\n <td>3.575</td>\n <td>0.03084</td>\n <td>0.319820</td>\n <td>0.302850</td>\n <td>0.303350</td>\n <td>0.234140</td>\n <td>0.097190</td>\n <td>0.365100</td>\n <td>1.952100</td>\n <td>2015</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Southern Asia</td>\n </tr>\n <tr>\n <th>Afghanistan</th>\n <td>153</td>\n <td>3.575</td>\n <td>0.03084</td>\n <td>0.319820</td>\n <td>0.302850</td>\n <td>0.303350</td>\n <td>0.234140</td>\n <td>0.097190</td>\n <td>0.365100</td>\n <td>1.952100</td>\n <td>2016</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Southern Asia</td>\n </tr>\n <tr>\n <th>Afghanistan</th>\n <td>154</td>\n <td>3.360</td>\n <td>NaN</td>\n <td>0.382270</td>\n <td>0.110370</td>\n <td>0.173440</td>\n <td>0.164300</td>\n <td>0.071120</td>\n <td>0.312680</td>\n <td>2.145580</td>\n <td>2017</td>\n <td>3.288000</td>\n <td>3.432000</td>\n <td>Southern Asia</td>\n </tr>\n <tr>\n <th>Afghanistan</th>\n <td>141</td>\n <td>3.794</td>\n <td>NaN</td>\n <td>0.401477</td>\n <td>0.581543</td>\n <td>0.180747</td>\n <td>0.106180</td>\n <td>0.061158</td>\n <td>0.311871</td>\n <td>2.150801</td>\n <td>2018</td>\n <td>3.714338</td>\n <td>3.873661</td>\n <td>Southern Asia</td>\n </tr>\n <tr>\n <th>Afghanistan</th>\n <td>154</td>\n <td>3.203</td>\n <td>NaN</td>\n <td>0.350000</td>\n <td>0.517000</td>\n <td>0.361000</td>\n <td>0.000000</td>\n <td>0.025000</td>\n <td>0.158000</td>\n <td>NaN</td>\n <td>2019</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Southern Asia</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>Zimbabwe</th>\n <td>115</td>\n <td>4.610</td>\n <td>0.04290</td>\n <td>0.271000</td>\n <td>1.032760</td>\n <td>0.334750</td>\n <td>0.258610</td>\n <td>0.080790</td>\n <td>0.189870</td>\n <td>2.441910</td>\n <td>2015</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Sub-Saharan Africa</td>\n </tr>\n <tr>\n <th>Zimbabwe</th>\n <td>115</td>\n <td>4.610</td>\n <td>0.04290</td>\n <td>0.271000</td>\n <td>1.032760</td>\n <td>0.334750</td>\n <td>0.258610</td>\n <td>0.080790</td>\n <td>0.189870</td>\n <td>2.441910</td>\n <td>2016</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Sub-Saharan Africa</td>\n </tr>\n <tr>\n <th>Zimbabwe</th>\n <td>131</td>\n <td>4.193</td>\n <td>NaN</td>\n <td>0.350410</td>\n <td>0.714780</td>\n <td>0.159500</td>\n <td>0.254290</td>\n <td>0.085820</td>\n <td>0.185030</td>\n <td>2.442700</td>\n <td>2017</td>\n <td>4.101000</td>\n <td>4.285000</td>\n <td>Sub-Saharan Africa</td>\n </tr>\n <tr>\n <th>Zimbabwe</th>\n <td>138</td>\n <td>3.875</td>\n <td>NaN</td>\n <td>0.375847</td>\n <td>1.083096</td>\n <td>0.196764</td>\n <td>0.336384</td>\n <td>0.095375</td>\n <td>0.189143</td>\n <td>1.597970</td>\n <td>2018</td>\n <td>3.771300</td>\n <td>3.978700</td>\n <td>Sub-Saharan Africa</td>\n </tr>\n <tr>\n <th>Zimbabwe</th>\n <td>146</td>\n <td>3.663</td>\n <td>NaN</td>\n <td>0.366000</td>\n <td>1.114000</td>\n <td>0.433000</td>\n <td>0.361000</td>\n <td>0.089000</td>\n <td>0.151000</td>\n <td>NaN</td>\n <td>2019</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Sub-Saharan Africa</td>\n </tr>\n </tbody>\n</table>\n<p>784 rows × 14 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"splitting preprocessed dataset "},{"metadata":{"trusted":true},"cell_type":"code","source":"data = index_country.reset_index()\n\ndf_15 = data[data['Year'] == 2015]\ndf_16 = data[data['Year'] == 2016]\ndf_17 = data[data['Year'] == 2017]\ndf_18 = data[data['Year'] == 2018]\ndf_19 = data[data['Year'] == 2019]","execution_count":49,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"avg_score_15 = df_15[['Region_2','Happiness Score', 'Year']].groupby(['Region_2', 'Year'])['Happiness Score'].mean()\navg_score_16 = df_16[['Region_2','Happiness Score', 'Year']].groupby(['Region_2', 'Year'])['Happiness Score'].mean()\navg_score_17 = df_17[['Region_2','Happiness Score', 'Year']].groupby(['Region_2', 'Year'])['Happiness Score'].mean()\navg_score_18 = df_18[['Region_2','Happiness Score', 'Year']].groupby(['Region_2', 'Year'])['Happiness Score'].mean()\navg_score_19 = df_19[['Region_2','Happiness Score', 'Year']].groupby(['Region_2', 'Year'])['Happiness Score'].mean()\n\navg_scores = avg_score_15.append([avg_score_16, avg_score_17, avg_score_18, avg_score_19]).to_frame()\n\navg_scores = avg_scores.reset_index()\navg_scores = avg_scores.set_index('Region_2')\n\navg_scores","execution_count":52,"outputs":[{"output_type":"execute_result","execution_count":52,"data":{"text/plain":" Year Happiness Score\nRegion_2 \nAustralia and New Zealand 2015 7.285000\nCentral and Eastern Europe 2015 5.332931\nEastern Asia 2015 5.626167\nLatin America and Caribbean 2015 6.144682\nMiddle East and Northern Africa 2015 5.406900\nNorth America 2015 7.273000\nSouthern Asia 2015 4.995187\nSub-Saharan Africa 2015 4.202800\nWestern Europe 2015 6.689619\nAustralia and New Zealand 2016 7.285000\nCentral and Eastern Europe 2016 5.332931\nEastern Asia 2016 5.626167\nLatin America and Caribbean 2016 6.144682\nMiddle East and Northern Africa 2016 5.406900\nNorth America 2016 7.273000\nSouthern Asia 2016 4.995187\nSub-Saharan Africa 2016 4.202800\nWestern Europe 2016 6.689619\nAustralia and New Zealand 2017 7.323500\nCentral and Eastern Europe 2017 5.370690\nEastern Asia 2017 5.624167\nLatin America and Caribbean 2017 6.101750\nMiddle East and Northern Africa 2017 5.386053\nNorth America 2017 7.254000\nSouthern Asia 2017 4.999563\nSub-Saharan Africa 2017 4.136421\nWestern Europe 2017 6.685667\nAustralia and New Zealand 2018 7.299000\nCentral and Eastern Europe 2018 5.409931\nEastern Asia 2018 5.646667\nLatin America and Caribbean 2018 5.957818\nMiddle East and Northern Africa 2018 5.369684\nNorth America 2018 7.154500\nSouthern Asia 2018 5.063867\nSub-Saharan Africa 2018 4.111949\nWestern Europe 2018 6.703714\nAustralia and New Zealand 2019 7.267500\nCentral and Eastern Europe 2019 5.561517\nEastern Asia 2019 5.688833\nLatin America and Caribbean 2019 5.954429\nMiddle East and Northern Africa 2019 5.237000\nNorth America 2019 7.085000\nSouthern Asia 2019 4.946937\nSub-Saharan Africa 2019 4.300050\nWestern Europe 2019 6.842190","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Year</th>\n <th>Happiness Score</th>\n </tr>\n <tr>\n <th>Region_2</th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>Australia and New Zealand</th>\n <td>2015</td>\n <td>7.285000</td>\n </tr>\n <tr>\n <th>Central and Eastern Europe</th>\n <td>2015</td>\n <td>5.332931</td>\n </tr>\n <tr>\n <th>Eastern Asia</th>\n <td>2015</td>\n <td>5.626167</td>\n </tr>\n <tr>\n <th>Latin America and Caribbean</th>\n <td>2015</td>\n <td>6.144682</td>\n </tr>\n <tr>\n <th>Middle East and Northern Africa</th>\n <td>2015</td>\n <td>5.406900</td>\n </tr>\n <tr>\n <th>North America</th>\n <td>2015</td>\n <td>7.273000</td>\n </tr>\n <tr>\n <th>Southern Asia</th>\n <td>2015</td>\n <td>4.995187</td>\n </tr>\n <tr>\n <th>Sub-Saharan Africa</th>\n <td>2015</td>\n <td>4.202800</td>\n </tr>\n <tr>\n <th>Western Europe</th>\n <td>2015</td>\n <td>6.689619</td>\n </tr>\n <tr>\n <th>Australia and New Zealand</th>\n <td>2016</td>\n <td>7.285000</td>\n </tr>\n <tr>\n <th>Central and Eastern Europe</th>\n <td>2016</td>\n <td>5.332931</td>\n </tr>\n <tr>\n <th>Eastern Asia</th>\n <td>2016</td>\n <td>5.626167</td>\n </tr>\n <tr>\n <th>Latin America and Caribbean</th>\n <td>2016</td>\n <td>6.144682</td>\n </tr>\n <tr>\n <th>Middle East and Northern Africa</th>\n <td>2016</td>\n <td>5.406900</td>\n </tr>\n <tr>\n <th>North America</th>\n <td>2016</td>\n <td>7.273000</td>\n </tr>\n <tr>\n <th>Southern Asia</th>\n <td>2016</td>\n <td>4.995187</td>\n </tr>\n <tr>\n <th>Sub-Saharan Africa</th>\n <td>2016</td>\n <td>4.202800</td>\n </tr>\n <tr>\n <th>Western Europe</th>\n <td>2016</td>\n <td>6.689619</td>\n </tr>\n <tr>\n <th>Australia and New Zealand</th>\n <td>2017</td>\n <td>7.323500</td>\n </tr>\n <tr>\n <th>Central and Eastern Europe</th>\n <td>2017</td>\n <td>5.370690</td>\n </tr>\n <tr>\n <th>Eastern Asia</th>\n <td>2017</td>\n <td>5.624167</td>\n </tr>\n <tr>\n <th>Latin America and Caribbean</th>\n <td>2017</td>\n <td>6.101750</td>\n </tr>\n <tr>\n <th>Middle East and Northern Africa</th>\n <td>2017</td>\n <td>5.386053</td>\n </tr>\n <tr>\n <th>North America</th>\n <td>2017</td>\n <td>7.254000</td>\n </tr>\n <tr>\n <th>Southern Asia</th>\n <td>2017</td>\n <td>4.999563</td>\n </tr>\n <tr>\n <th>Sub-Saharan Africa</th>\n <td>2017</td>\n <td>4.136421</td>\n </tr>\n <tr>\n <th>Western Europe</th>\n <td>2017</td>\n <td>6.685667</td>\n </tr>\n <tr>\n <th>Australia and New Zealand</th>\n <td>2018</td>\n <td>7.299000</td>\n </tr>\n <tr>\n <th>Central and Eastern Europe</th>\n <td>2018</td>\n <td>5.409931</td>\n </tr>\n <tr>\n <th>Eastern Asia</th>\n <td>2018</td>\n <td>5.646667</td>\n </tr>\n <tr>\n <th>Latin America and Caribbean</th>\n <td>2018</td>\n <td>5.957818</td>\n </tr>\n <tr>\n <th>Middle East and Northern Africa</th>\n <td>2018</td>\n <td>5.369684</td>\n </tr>\n <tr>\n <th>North America</th>\n <td>2018</td>\n <td>7.154500</td>\n </tr>\n <tr>\n <th>Southern Asia</th>\n <td>2018</td>\n <td>5.063867</td>\n </tr>\n <tr>\n <th>Sub-Saharan Africa</th>\n <td>2018</td>\n <td>4.111949</td>\n </tr>\n <tr>\n <th>Western Europe</th>\n <td>2018</td>\n <td>6.703714</td>\n </tr>\n <tr>\n <th>Australia and New Zealand</th>\n <td>2019</td>\n <td>7.267500</td>\n </tr>\n <tr>\n <th>Central and Eastern Europe</th>\n <td>2019</td>\n <td>5.561517</td>\n </tr>\n <tr>\n <th>Eastern Asia</th>\n <td>2019</td>\n <td>5.688833</td>\n </tr>\n <tr>\n <th>Latin America and Caribbean</th>\n <td>2019</td>\n <td>5.954429</td>\n </tr>\n <tr>\n <th>Middle East and Northern Africa</th>\n <td>2019</td>\n <td>5.237000</td>\n </tr>\n <tr>\n <th>North America</th>\n <td>2019</td>\n <td>7.085000</td>\n </tr>\n <tr>\n <th>Southern Asia</th>\n <td>2019</td>\n <td>4.946937</td>\n </tr>\n <tr>\n <th>Sub-Saharan Africa</th>\n <td>2019</td>\n <td>4.300050</td>\n </tr>\n <tr>\n <th>Western Europe</th>\n <td>2019</td>\n <td>6.842190</td>\n </tr>\n </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\na = avg_scores.index.unique()\nb = (['ANZ', 'CEEU', 'EA', 'AMS', 'MENA', 'US', 'SA','S_SA', 'WEU'])","execution_count":53,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for i,e in zip(a,b):\n print(e+\"_avg_hap = avg_scores.loc['\"+i+\"']\")","execution_count":54,"outputs":[{"output_type":"stream","text":"ANZ_avg_hap = avg_scores.loc['Australia and New Zealand']\nCEEU_avg_hap = avg_scores.loc['Central and Eastern Europe']\nEA_avg_hap = avg_scores.loc['Eastern Asia']\nAMS_avg_hap = avg_scores.loc['Latin America and Caribbean']\nMENA_avg_hap = avg_scores.loc['Middle East and Northern Africa']\nUS_avg_hap = avg_scores.loc['North America']\nSA_avg_hap = avg_scores.loc['Southern Asia']\nS_SA_avg_hap = avg_scores.loc['Sub-Saharan Africa']\nWEU_avg_hap = avg_scores.loc['Western Europe']\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"ANZ_avg_hap = avg_scores.loc['Australia and New Zealand']\nCEEU_avg_hap = avg_scores.loc['Central and Eastern Europe']\nEA_avg_hap = avg_scores.loc['Eastern Asia']\nAMS_avg_hap = avg_scores.loc['Latin America and Caribbean']\nMENA_avg_hap = avg_scores.loc['Middle East and Northern Africa']\nUS_avg_hap = avg_scores.loc['North America']\nSA_avg_hap = avg_scores.loc['Southern Asia']\nS_SA_avg_hap = avg_scores.loc['Sub-Saharan Africa']\nWEU_avg_hap = avg_scores.loc['Western Europe']","execution_count":55,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for i in b:\n print(\"x_\"+i,\" = \"+i+\"_avg_hap['Year']\")","execution_count":56,"outputs":[{"output_type":"stream","text":"x_ANZ = ANZ_avg_hap['Year']\nx_CEEU = CEEU_avg_hap['Year']\nx_EA = EA_avg_hap['Year']\nx_AMS = AMS_avg_hap['Year']\nx_MENA = MENA_avg_hap['Year']\nx_US = US_avg_hap['Year']\nx_SA = SA_avg_hap['Year']\nx_S_SA = S_SA_avg_hap['Year']\nx_WEU = WEU_avg_hap['Year']\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"for i in b:\n print(\"y_\"+i,\" = \"+i+\"_avg_hap['Happiness Score']\")","execution_count":57,"outputs":[{"output_type":"stream","text":"y_ANZ = ANZ_avg_hap['Happiness Score']\ny_CEEU = CEEU_avg_hap['Happiness Score']\ny_EA = EA_avg_hap['Happiness Score']\ny_AMS = AMS_avg_hap['Happiness Score']\ny_MENA = MENA_avg_hap['Happiness Score']\ny_US = US_avg_hap['Happiness Score']\ny_SA = SA_avg_hap['Happiness Score']\ny_S_SA = S_SA_avg_hap['Happiness Score']\ny_WEU = WEU_avg_hap['Happiness Score']\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"ANZ_avg_hap = ANZ_avg_hap.set_index('Year')\nCEEU_avg_hap = CEEU_avg_hap.set_index('Year')\nEA_avg_hap = EA_avg_hap.set_index('Year')\nAMS_avg_hap = AMS_avg_hap.set_index('Year')\nMENA_avg_hap = MENA_avg_hap.set_index('Year')\nUS_avg_hap = US_avg_hap.set_index('Year')\nSA_avg_hap = SA_avg_hap.set_index('Year')\nS_SA_avg_hap = S_SA_avg_hap.set_index('Year')\nWEU_avg_hap = WEU_avg_hap.set_index('Year')\n\n#x_ANZ = ANZ_avg_hap.index\n#x_CEEU = CEEU_avg_hap.index\n#x_EA = EA_avg_hap.index\n#x_AMS = AMS_avg_hap.index\n#x_MENA = MENA_avg_hap.index\n#x_US = US_avg_hap.index\n#x_SA = SA_avg_hap.index\n#x_S_SA = S_SA_avg_hap.index\n#x_WEU = WEU_avg_hap.index\n\ny_ANZ = ANZ_avg_hap['Happiness Score']\ny_CEEU = CEEU_avg_hap['Happiness Score']\ny_EA = EA_avg_hap['Happiness Score']\ny_AMS = AMS_avg_hap['Happiness Score']\ny_MENA = MENA_avg_hap['Happiness Score']\ny_US = US_avg_hap['Happiness Score']\ny_SA = SA_avg_hap['Happiness Score']\ny_S_SA = S_SA_avg_hap['Happiness Score']\ny_WEU = WEU_avg_hap['Happiness Score']","execution_count":60,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig, (ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8, ax9) = plt.subplots(1, 9, sharey=True, figsize=(35,10))\n#fig.suptitle('Horizontally stacked subplots')\n\n#plot #1\ndf_ANZ_shifted = ANZ_avg_hap.shift()\ndf_ANZ_shifted.iloc[0] = df_ANZ_shifted.iloc[1]\nmask_inc = ANZ_avg_hap >= df_ANZ_shifted\nANZ_avg_hap['Increase'] = mask_inc['Happiness Score'].astype(int)\n\n\ncolor = {True: 'green', False: 'red'}\nfor index, row in ANZ_avg_hap.iterrows():\n ax1.plot(index, row['Happiness Score'], 'o', color=color[row['Increase']])\nax1.set_title('Australia and New Zealand', fontsize=15)\n\n#plot #2\ndf_CEEU_shifted = CEEU_avg_hap.shift()\ndf_CEEU_shifted.iloc[0] = df_CEEU_shifted.iloc[1]\nmask_inc = CEEU_avg_hap >= df_CEEU_shifted\nCEEU_avg_hap['Increase'] = mask_inc['Happiness Score'].astype(int)\n\n\ncolor = {True: 'green', False: 'red'}\nfor index, row in CEEU_avg_hap.iterrows():\n ax7.plot(index, row['Happiness Score'], 'o', color=color[row['Increase']])\nax7.set_title('Central and Eastern Europe', fontsize=15)\n\n#plot #3\ndf_EA_shifted = EA_avg_hap.shift()\ndf_EA_shifted.iloc[0] = df_EA_shifted.iloc[1]\nmask_inc = EA_avg_hap >= df_EA_shifted\nEA_avg_hap['Increase'] = mask_inc['Happiness Score'].astype(int)\n\n\ncolor = {True: 'green', False: 'red'}\nfor index, row in EA_avg_hap.iterrows():\n ax5.plot(index, row['Happiness Score'], 'o', color=color[row['Increase']])\nax5.set_title('Eastern Asia', fontsize=15)\n\n\n#plot #4\ndf_AMS_shifted = AMS_avg_hap.shift()\ndf_AMS_shifted.iloc[0] = df_AMS_shifted.iloc[1]\nmask_inc = AMS_avg_hap >= df_AMS_shifted\nAMS_avg_hap['Increase'] = mask_inc['Happiness Score'].astype(int)\n\n\ncolor = {True: 'green', False: 'red'}\nfor index, row in AMS_avg_hap.iterrows():\n ax4.plot(index, row['Happiness Score'], 'o', color=color[row['Increase']])\nax4.set_title('LATAM', fontsize=15)\n\n#plot #5\ndf_MENA_shifted = MENA_avg_hap.shift()\ndf_MENA_shifted.iloc[0] = df_MENA_shifted.iloc[1]\nmask_inc = MENA_avg_hap >= df_MENA_shifted\nMENA_avg_hap['Increase'] = mask_inc['Happiness Score'].astype(int)\n\n\ncolor = {True: 'green', False: 'red'}\nfor index, row in MENA_avg_hap.iterrows():\n ax6.plot(index, row['Happiness Score'], 'o', color=color[row['Increase']])\nax6.set_title('MENA', fontsize=15)\n\n#plot #6\ndf_US_shifted = US_avg_hap.shift()\ndf_US_shifted.iloc[0] = df_US_shifted.iloc[1]\nmask_inc =US_avg_hap >= df_US_shifted\nUS_avg_hap['Increase'] = mask_inc['Happiness Score'].astype(int)\n\n\ncolor = {True: 'green', False: 'red'}\nfor index, row in US_avg_hap.iterrows():\n ax2.plot(index, row['Happiness Score'], 'o', color=color[row['Increase']])\nax2.set_title('North America', fontsize=15)\n\n#plot #7\ndf_SA_shifted = SA_avg_hap.shift()\ndf_SA_shifted.iloc[0] = df_SA_shifted.iloc[1]\nmask_inc = SA_avg_hap >= df_SA_shifted\nSA_avg_hap['Increase'] = mask_inc['Happiness Score'].astype(int)\n\n\ncolor = {True: 'green', False: 'red'}\nfor index, row in SA_avg_hap.iterrows():\n ax8.plot(index, row['Happiness Score'], 'o', color=color[row['Increase']])\nax8.set_title('Southern Asia', fontsize=15)\n\n#plot #8\ndf_S_SA_shifted = S_SA_avg_hap.shift()\ndf_S_SA_shifted.iloc[0] = df_S_SA_shifted.iloc[1]\nmask_inc = S_SA_avg_hap >= df_S_SA_shifted\nS_SA_avg_hap['Increase'] = mask_inc['Happiness Score'].astype(int)\n\n\ncolor = {True: 'green', False: 'red'}\nfor index, row in S_SA_avg_hap.iterrows():\n ax9.plot(index, row['Happiness Score'], 'o', color=color[row['Increase']])\nax9.set_title('Sub-Saharan Africa', fontsize=15)\n\n#plot #9\ndf_WEU_shifted = WEU_avg_hap.shift()\ndf_WEU_shifted.iloc[0] = df_WEU_shifted.iloc[1]\nmask_inc = WEU_avg_hap >= df_WEU_shifted\nWEU_avg_hap['Increase'] = mask_inc['Happiness Score'].astype(int)\n\n\ncolor = {True: 'green', False: 'red'}\nfor index, row in WEU_avg_hap.iterrows():\n ax3.plot(index, row['Happiness Score'], 'o', color=color[row['Increase']])\nax3.set_title('Western Europe', fontsize=15)\n\n\nplt.ylim(0,10)\n","execution_count":62,"outputs":[{"output_type":"execute_result","execution_count":62,"data":{"text/plain":"(0.0, 10.0)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 2520x720 with 9 Axes>","image/png":"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Deep Dive - Regions one by one"},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4} | 0047/234/47234867.ipynb | s3://data-agents/kaggle-outputs/sharded/010_00047.jsonl.gz |
{"cells":[{"metadata":{},"cell_type":"markdown","source":"# An V1-like Image Preprocessing Algorithm\n\nThe idea and parameters are taken from [\"Robust Object Recognition with Cortex-Like Mechanisms\" (Serre et al.)](https://mcgovern.mit.edu/wp-content/uploads/2019/01/04069258.pdf).\nThe idea is converting a single gray-scaled image (of size 256X256 for example) to distinct texture features by using Gabor filters convolution and MaxPooling (resulting in a matrix of 256X256X8X4).\nNotice that the algorithm in the article includes two more layers.\n\nThis script is converting a Cat and Dog dataset into zip files containing numpy files, each with a single processed image.\nThese files weight way too much, so please read the instructions if you want to use it.\n\nThis script is part of [another project.](https://www.kaggle.com/royurbach/doodling-l5pc-model-and-cats-vs-dogs)"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import keras\nimport tensorflow as tf\nimport keras.layers as layers\nimport numpy as np\nfrom numpy import save, load\nfrom tensorflow.python.ops import math_ops\nfrom tensorflow import math, random, shape\nimport os\nfrom keras.losses import MeanSquaredError, BinaryCrossentropy\nfrom keras.optimizers import Nadam, SGD, Adam\nfrom keras.activations import sigmoid\nfrom tensorflow import convert_to_tensor as tens\nfrom keras import backend as K\nfrom cv2 import getGaborKernel as Gabor\nfrom functools import reduce\nfrom matplotlib import pyplot as plt\nfrom math import sqrt\nimport itertools\nimport re\nfrom PIL import Image\nfrom random import choice, shuffle\nimport shutil\n","execution_count":25,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Gabor Filters"},{"metadata":{},"cell_type":"markdown","source":"Parameters:"},{"metadata":{"trusted":true},"cell_type":"code","source":"n_filters = 64\nn_orientations = 4\n\nksizes = [(i, i) for i in range(7,38, 2)]\nthetas = [0 , 45, 90, 135]\ngammas = [0.3] * 16\nsigmas = [2.8, 3.6, 4.5, 5.4, 6.3, 7.3, 8.2, 9.2, 10.2, 11.3, 12.3, 13.4, 14.6, 15.8, 17., 18.2]\nlambdas = [3.5, 4.6, 5.6, 6.8, 7.9, 9.1, 10.3, 11.5, 12.7, 14.1, 15.4, 16.8, 18.2, 19.7, 21.2, 22.8]\n\nall_filters = [[(size, sigma, theta, lambd, gamma) for theta in thetas] \n for size, gamma, sigma, lambd in zip(ksizes, gammas, sigmas, lambdas)]\nall_filters = reduce(lambda x,y: x+y, all_filters, [])","execution_count":26,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class GaborInitializer(tf.keras.initializers.Initializer):\n def __init__(self, size, sigma, theta, lambd, gamma):\n self.ksize = size\n self.sigma = sigma\n self.theta = theta\n self.lambd = lambd\n self.gamma = gamma\n\n def __call__(self, dtype=None):\n return tens(Gabor(self.ksize, self.sigma, self.theta, self.lambd, self.gamma))\n\n def get_config(self): # To support serialization\n return {'ksize': self.ksize, 'sigma': self.sigma, 'theta': self.theta, 'lambda': self.lambd, 'gamma': self.gamma}","execution_count":27,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Creates a Conv2D layer with a Gabor filter kernel"},{"metadata":{"trusted":true},"cell_type":"code","source":"def conv2d_with_gabor(filters, trainable=False, shape=(1,256,256,1)):\n layer = layers.Conv2D(len(filters), filters[0][0], kernel_initializer='zeros', padding='same')\n initalize = layer(np.zeros(shape))\n new_weights = np.stack([GaborInitializer(*filt)() for filt in filters]).transpose((1,2,0))[:,:,np.newaxis,:]\n layer.set_weights([new_weights, layer.get_weights()[-1]])\n layer.trainable = trainable\n return layer","execution_count":28,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"How they look:"},{"metadata":{"trusted":true},"cell_type":"code","source":"for i, filt in enumerate(all_filters):\n plt.subplot(8,8,i+1)\n plt.imshow(GaborInitializer(*filt)(), cmap='gray')\n plt.axis(\"off\")","execution_count":29,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 64 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"# MaxPooling sizes"},{"metadata":{"trusted":true},"cell_type":"code","source":"max_pooling_size = list(range(8,23,2))","execution_count":30,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# A Preprocessing Model"},{"metadata":{"trusted":true},"cell_type":"code","source":"def preprocess_model(filters, n_filters, n_orientations, max_pooling_size, shape=(256,256,1)):\n inp = keras.Input(shape=shape)\n pre_process_s1 = [layers.Concatenate(axis=-2)\n ([K.expand_dims(\n conv2d_with_gabor(filters[n_orientations*j:n_orientations*(j+1)], trainable=False)(inp),\n axis=-2) \n for j in (i, i+1)]) \n for i in range(0,n_filters//n_orientations,2)]\n pre_process_c1 = layers.Concatenate(axis=-2)([(layers.MaxPooling3D(pool_size=(ksize, ksize, 2), strides=(1,1,2), padding=\"same\")(i)) \n for i, ksize in zip(pre_process_s1, max_pooling_size)])\n processor = keras.Model(inp, pre_process_c1)\n return processor","execution_count":31,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"processor = preprocess_model(all_filters, n_filters, n_orientations, max_pooling_size)","execution_count":32,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# List of files to convert"},{"metadata":{"trusted":true},"cell_type":"code","source":"directory = '../input/cat-and-dog'","execution_count":33,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_photos_dogs = sorted([i for i in os.listdir(directory+'/test_set/test_set/dogs') if re.findall('.jpg', i)])","execution_count":34,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_photos_cats = sorted([i for i in os.listdir(directory+'/test_set/test_set/cats') if re.findall('.jpg', i)])","execution_count":35,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_photos_dogs = sorted([i for i in os.listdir(directory+'/training_set/training_set/dogs') if re.findall('.jpg', i)])","execution_count":36,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_photos_cats = sorted([i for i in os.listdir(directory+'/training_set/training_set/cats') if re.findall('.jpg', i)])","execution_count":37,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(len(test_photos_dogs), len(test_photos_cats), len(train_photos_dogs), len(train_photos_cats))","execution_count":38,"outputs":[{"output_type":"stream","text":"1012 1011 4005 4000\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"# Example of a converted image"},{"metadata":{"trusted":true},"cell_type":"code","source":"filename = choice(test_photos_dogs)\nimage = np.array(Image.open(directory + '/test_set/test_set/dogs/'+filename))\nimage = tf.image.rgb_to_grayscale(image)\nimage = tf.image.resize(image, (256,256))\nimage = image * 1/255\nnew_image = processor(image[np.newaxis]).numpy()[0]\nplt.figure(figsize=(10,10))\nfor i in range(8):\n plt.subplot(8, 5, 5*i+1)\n plt.imshow(image[:,:,0], cmap='gray')\n plt.title('original')\n plt.axis('off')\n for j in range(4):\n plt.subplot(8, 5, 5*i + j + 2)\n plt.imshow(new_image[:,:,i,j], cmap='gray')\n plt.axis('off')","execution_count":40,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 720x720 with 40 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"# Convert"},{"metadata":{"trusted":true},"cell_type":"code","source":"os.makedirs('../working/cat-and-dog-processed')","execution_count":12,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def convert_some_pictures(processor, path, files, out_dirc):\n for filename in files:\n image = np.array(Image.open(path + filename))\n image = tf.image.rgb_to_grayscale(image)\n image = tf.image.resize(image, (256,256))\n image = image * 1/255\n new_filename = out_dirc + re.findall('(.*).jpg', filename)[0] + '.npy'\n new_image = processor(image[np.newaxis]).numpy()[0]\n with open(new_filename, 'wb') as f:\n save(f, new_image)\n print('Converted:', new_filename)","execution_count":14,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def delete_files_in_directory(directory):\n for filename in os.listdir(directory):\n os.remove(directory + filename)","execution_count":15,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def call_convert(processor, directory, filenames, output, zip_name):\n convert_some_pictures(processor, directory, filenames, output)\n print('Done converting!')\n shutil.make_archive(zip_name, 'zip', output)\n print(f'Done Zipping! Check for {zip_name}.')\n delete_files_in_directory(output)\n print('Done erasing photos!')","execution_count":16,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Convert\nThe files weight too much, so after finishing each batch, download the created zip files (might take some time), restart the kernel, and run the next batch."},{"metadata":{"trusted":true},"cell_type":"code","source":"for i in range(len(test_photos_dogs)//500 + 1):\n call_convert(processor, directory+'/test_set/test_set/dogs/', test_photos_dogs[i*500:(i+1)*500], '../working/cat-and-dog-processed/', f'test_dog_{i*500}_{(i+1)*500}')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"collapsed":true},"cell_type":"code","source":"for i in range(len(test_photos_cats)//500 + 1):\n call_convert(processor, directory+'/test_set/test_set/cats/', test_photos_cats[i*500:(i+1)*500], '../working/cat-and-dog-processed/', f'test_cat_{i*500}_{(i+1)*500}')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"collapsed":true},"cell_type":"code","source":"for i in range(len(train_photos_dogs)//500 + 1):\n call_convert(processor, directory+'/training_set/training_set/dogs/', train_photos_dogs[i*500:(i+1)*500], '../working/cat-and-dog-processed/', f'train_dog_{i*500}_{(i+1)*500}')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for i in range(len(train_photos_cats)//500 + 1):\n call_convert(processor, directory+'/training_set/training_set/cats/', train_photos_cats[i*500:(i+1)*500], '../working/cat-and-dog-processed/', f'train_cat_{i*500}_{(i+1)*500}')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Generator to Read Dataset\n\nWon't work here because files aren't uploaded to input.\nCopy to your script."},{"metadata":{"trusted":true},"cell_type":"code","source":"class NumpyDirectoryGenerator:\n def __init__(self, dct_label, dtype='.npy', randomize=True, random_seed=1331, validation_split=None, is_validation=False):\n self.directories = dct_label\n self.dtype = dtype\n self.files = []\n self.randomize = randomize\n self.seed = random_seed\n self.validation_split = validation_split\n self.is_validation = is_validation\n self._create_files()\n \n def _create_files(self):\n for directory,label in self.directories.items():\n self.files += [(directory+ '' if directory[-1] == '/' else '/' +i, label) for i in os.listdir(directory) if re.findall(dtype, i)]\n if randomize:\n random.seed(self.seed)\n self.files = self.files.randomize()\n if self.validation_split:\n if self.is_validation:\n self.files = self.files[len(self.files) - len(self.files)*self.validation_split:]\n else:\n self.files = self.files[:len(self.files) - len(self.files)*self.validation_split]\n \n def __call__(self):\n return iter(self)\n \n def __iter__(self):\n for file, label in self.files:\n with open(file) as data:\n yield (load(data), label)","execution_count":20,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"directory = '../input/cat-and-dog-processed'\ntraining_directory = directory + '/training_set'\ntest_directory = directory + '/test_set'\n\ntraining_gen = NumpyDirectoryGenerator({training_directory+'/dogs': 1, training_directory+'/cats': 0}, validation_split=0.2)\nvalidation_gen = NumpyDirectoryGenerator({training_directory+'/dogs': 1, training_directory+'/cats': 0}, validation_split=0.2, is_validation=True)\ntest_gen = NumpyDirectoryGenerator({test_directory+'/dogs': 1, test_directory+'/cats': 0})\n\ntraining_ds = tf.data.Dataset.from_generator(training_gen, output_types=(tf.int64, tf.int64)).batch(32)\nvalidation_ds = tf.data.Dataset.from_generator(validation_gen, output_types=(tf.int64, tf.int64)).batch(32)\ntest_ds = tf.data.Dataset.from_generator(test_gen, output_types=(tf.int64, tf.int64)).batch(32)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4} | 0047/234/47234932.ipynb | s3://data-agents/kaggle-outputs/sharded/010_00047.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"execution": {
"iopub.execute_input": "2020-11-19T11:43:05.146587Z",
"iopub.status.busy": "2020-11-19T11:43:05.145702Z",
"iopub.status.idle": "2020-11-19T11:43:06.170595Z",
"shell.execute_reply": "2020-11-19T11:43:06.169925Z"
},
"papermill": {
"duration": 1.054403,
"end_time": "2020-11-19T11:43:06.170768",
"exception": false,
"start_time": "2020-11-19T11:43:05.116365",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/kaggle/input/cancer-mortality-us/cancer.csv\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load\n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import pickle\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import re\n",
"# Input data files are available in the read-only \"../input/\" directory\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
"\n",
"import os\n",
"for dirname, _, filenames in os.walk('/kaggle/input'):\n",
" for filename in filenames:\n",
" print(os.path.join(dirname, filename))\n",
"pd.set_option('display.max_rows', None)\n",
"pd.set_option('display.max_columns', None)\n",
"\n",
"# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
"# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
"_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
"execution": {
"iopub.execute_input": "2020-11-19T11:43:06.214420Z",
"iopub.status.busy": "2020-11-19T11:43:06.213591Z",
"iopub.status.idle": "2020-11-19T11:43:06.300865Z",
"shell.execute_reply": "2020-11-19T11:43:06.301463Z"
},
"papermill": {
"duration": 0.111559,
"end_time": "2020-11-19T11:43:06.301614",
"exception": false,
"start_time": "2020-11-19T11:43:06.190055",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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" <th>avgAnnCount</th>\n",
" <th>avgDeathsPerYear</th>\n",
" <th>TARGET_deathRate</th>\n",
" <th>incidenceRate</th>\n",
" <th>medIncome</th>\n",
" <th>popEst2015</th>\n",
" <th>povertyPercent</th>\n",
" <th>studyPerCap</th>\n",
" <th>binnedInc</th>\n",
" <th>MedianAge</th>\n",
" <th>MedianAgeMale</th>\n",
" <th>MedianAgeFemale</th>\n",
" <th>Geography</th>\n",
" <th>AvgHouseholdSize</th>\n",
" <th>PercentMarried</th>\n",
" <th>PctNoHS18_24</th>\n",
" <th>PctHS18_24</th>\n",
" <th>PctSomeCol18_24</th>\n",
" <th>PctBachDeg18_24</th>\n",
" <th>PctHS25_Over</th>\n",
" <th>PctBachDeg25_Over</th>\n",
" <th>PctEmployed16_Over</th>\n",
" <th>PctUnemployed16_Over</th>\n",
" <th>PctPrivateCoverage</th>\n",
" <th>PctPrivateCoverageAlone</th>\n",
" <th>PctEmpPrivCoverage</th>\n",
" <th>PctPublicCoverage</th>\n",
" <th>PctPublicCoverageAlone</th>\n",
" <th>PctWhite</th>\n",
" <th>PctBlack</th>\n",
" <th>PctAsian</th>\n",
" <th>PctOtherRace</th>\n",
" <th>PctMarriedHouseholds</th>\n",
" <th>BirthRate</th>\n",
" </tr>\n",
" </thead>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>1397.0</td>\n",
" <td>469</td>\n",
" <td>164.9</td>\n",
" <td>489.8</td>\n",
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" <td>260131</td>\n",
" <td>11.2</td>\n",
" <td>499.748204</td>\n",
" <td>(61494.5, 125635]</td>\n",
" <td>39.3</td>\n",
" <td>36.9</td>\n",
" <td>41.7</td>\n",
" <td>Kitsap County, Washington</td>\n",
" <td>2.54</td>\n",
" <td>52.5</td>\n",
" <td>11.5</td>\n",
" <td>39.5</td>\n",
" <td>42.1</td>\n",
" <td>6.9</td>\n",
" <td>23.2</td>\n",
" <td>19.6</td>\n",
" <td>51.9</td>\n",
" <td>8.0</td>\n",
" <td>75.1</td>\n",
" <td>NaN</td>\n",
" <td>41.6</td>\n",
" <td>32.9</td>\n",
" <td>14.0</td>\n",
" <td>81.780529</td>\n",
" <td>2.594728</td>\n",
" <td>4.821857</td>\n",
" <td>1.843479</td>\n",
" <td>52.856076</td>\n",
" <td>6.118831</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>173.0</td>\n",
" <td>70</td>\n",
" <td>161.3</td>\n",
" <td>411.6</td>\n",
" <td>48127</td>\n",
" <td>43269</td>\n",
" <td>18.6</td>\n",
" <td>23.111234</td>\n",
" <td>(48021.6, 51046.4]</td>\n",
" <td>33.0</td>\n",
" <td>32.2</td>\n",
" <td>33.7</td>\n",
" <td>Kittitas County, Washington</td>\n",
" <td>2.34</td>\n",
" <td>44.5</td>\n",
" <td>6.1</td>\n",
" <td>22.4</td>\n",
" <td>64.0</td>\n",
" <td>7.5</td>\n",
" <td>26.0</td>\n",
" <td>22.7</td>\n",
" <td>55.9</td>\n",
" <td>7.8</td>\n",
" <td>70.2</td>\n",
" <td>53.8</td>\n",
" <td>43.6</td>\n",
" <td>31.1</td>\n",
" <td>15.3</td>\n",
" <td>89.228509</td>\n",
" <td>0.969102</td>\n",
" <td>2.246233</td>\n",
" <td>3.741352</td>\n",
" <td>45.372500</td>\n",
" <td>4.333096</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>102.0</td>\n",
" <td>50</td>\n",
" <td>174.7</td>\n",
" <td>349.7</td>\n",
" <td>49348</td>\n",
" <td>21026</td>\n",
" <td>14.6</td>\n",
" <td>47.560164</td>\n",
" <td>(48021.6, 51046.4]</td>\n",
" <td>45.0</td>\n",
" <td>44.0</td>\n",
" <td>45.8</td>\n",
" <td>Klickitat County, Washington</td>\n",
" <td>2.62</td>\n",
" <td>54.2</td>\n",
" <td>24.0</td>\n",
" <td>36.6</td>\n",
" <td>NaN</td>\n",
" <td>9.5</td>\n",
" <td>29.0</td>\n",
" <td>16.0</td>\n",
" <td>45.9</td>\n",
" <td>7.0</td>\n",
" <td>63.7</td>\n",
" <td>43.5</td>\n",
" <td>34.9</td>\n",
" <td>42.1</td>\n",
" <td>21.1</td>\n",
" <td>90.922190</td>\n",
" <td>0.739673</td>\n",
" <td>0.465898</td>\n",
" <td>2.747358</td>\n",
" <td>54.444868</td>\n",
" <td>3.729488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>427.0</td>\n",
" <td>202</td>\n",
" <td>194.8</td>\n",
" <td>430.4</td>\n",
" <td>44243</td>\n",
" <td>75882</td>\n",
" <td>17.1</td>\n",
" <td>342.637253</td>\n",
" <td>(42724.4, 45201]</td>\n",
" <td>42.8</td>\n",
" <td>42.2</td>\n",
" <td>43.4</td>\n",
" <td>Lewis County, Washington</td>\n",
" <td>2.52</td>\n",
" <td>52.7</td>\n",
" <td>20.2</td>\n",
" <td>41.2</td>\n",
" <td>36.1</td>\n",
" <td>2.5</td>\n",
" <td>31.6</td>\n",
" <td>9.3</td>\n",
" <td>48.3</td>\n",
" <td>12.1</td>\n",
" <td>58.4</td>\n",
" <td>40.3</td>\n",
" <td>35.0</td>\n",
" <td>45.3</td>\n",
" <td>25.0</td>\n",
" <td>91.744686</td>\n",
" <td>0.782626</td>\n",
" <td>1.161359</td>\n",
" <td>1.362643</td>\n",
" <td>51.021514</td>\n",
" <td>4.603841</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>57.0</td>\n",
" <td>26</td>\n",
" <td>144.4</td>\n",
" <td>350.1</td>\n",
" <td>49955</td>\n",
" <td>10321</td>\n",
" <td>12.5</td>\n",
" <td>0.000000</td>\n",
" <td>(48021.6, 51046.4]</td>\n",
" <td>48.3</td>\n",
" <td>47.8</td>\n",
" <td>48.9</td>\n",
" <td>Lincoln County, Washington</td>\n",
" <td>2.34</td>\n",
" <td>57.8</td>\n",
" <td>14.9</td>\n",
" <td>43.0</td>\n",
" <td>40.0</td>\n",
" <td>2.0</td>\n",
" <td>33.4</td>\n",
" <td>15.0</td>\n",
" <td>48.2</td>\n",
" <td>4.8</td>\n",
" <td>61.6</td>\n",
" <td>43.9</td>\n",
" <td>35.1</td>\n",
" <td>44.0</td>\n",
" <td>22.7</td>\n",
" <td>94.104024</td>\n",
" <td>0.270192</td>\n",
" <td>0.665830</td>\n",
" <td>0.492135</td>\n",
" <td>54.027460</td>\n",
" <td>6.796657</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" avgAnnCount avgDeathsPerYear TARGET_deathRate incidenceRate medIncome \\\n",
"0 1397.0 469 164.9 489.8 61898 \n",
"1 173.0 70 161.3 411.6 48127 \n",
"2 102.0 50 174.7 349.7 49348 \n",
"3 427.0 202 194.8 430.4 44243 \n",
"4 57.0 26 144.4 350.1 49955 \n",
"\n",
" popEst2015 povertyPercent studyPerCap binnedInc MedianAge \\\n",
"0 260131 11.2 499.748204 (61494.5, 125635] 39.3 \n",
"1 43269 18.6 23.111234 (48021.6, 51046.4] 33.0 \n",
"2 21026 14.6 47.560164 (48021.6, 51046.4] 45.0 \n",
"3 75882 17.1 342.637253 (42724.4, 45201] 42.8 \n",
"4 10321 12.5 0.000000 (48021.6, 51046.4] 48.3 \n",
"\n",
" MedianAgeMale MedianAgeFemale Geography \\\n",
"0 36.9 41.7 Kitsap County, Washington \n",
"1 32.2 33.7 Kittitas County, Washington \n",
"2 44.0 45.8 Klickitat County, Washington \n",
"3 42.2 43.4 Lewis County, Washington \n",
"4 47.8 48.9 Lincoln County, Washington \n",
"\n",
" AvgHouseholdSize PercentMarried PctNoHS18_24 PctHS18_24 \\\n",
"0 2.54 52.5 11.5 39.5 \n",
"1 2.34 44.5 6.1 22.4 \n",
"2 2.62 54.2 24.0 36.6 \n",
"3 2.52 52.7 20.2 41.2 \n",
"4 2.34 57.8 14.9 43.0 \n",
"\n",
" PctSomeCol18_24 PctBachDeg18_24 PctHS25_Over PctBachDeg25_Over \\\n",
"0 42.1 6.9 23.2 19.6 \n",
"1 64.0 7.5 26.0 22.7 \n",
"2 NaN 9.5 29.0 16.0 \n",
"3 36.1 2.5 31.6 9.3 \n",
"4 40.0 2.0 33.4 15.0 \n",
"\n",
" PctEmployed16_Over PctUnemployed16_Over PctPrivateCoverage \\\n",
"0 51.9 8.0 75.1 \n",
"1 55.9 7.8 70.2 \n",
"2 45.9 7.0 63.7 \n",
"3 48.3 12.1 58.4 \n",
"4 48.2 4.8 61.6 \n",
"\n",
" PctPrivateCoverageAlone PctEmpPrivCoverage PctPublicCoverage \\\n",
"0 NaN 41.6 32.9 \n",
"1 53.8 43.6 31.1 \n",
"2 43.5 34.9 42.1 \n",
"3 40.3 35.0 45.3 \n",
"4 43.9 35.1 44.0 \n",
"\n",
" PctPublicCoverageAlone PctWhite PctBlack PctAsian PctOtherRace \\\n",
"0 14.0 81.780529 2.594728 4.821857 1.843479 \n",
"1 15.3 89.228509 0.969102 2.246233 3.741352 \n",
"2 21.1 90.922190 0.739673 0.465898 2.747358 \n",
"3 25.0 91.744686 0.782626 1.161359 1.362643 \n",
"4 22.7 94.104024 0.270192 0.665830 0.492135 \n",
"\n",
" PctMarriedHouseholds BirthRate \n",
"0 52.856076 6.118831 \n",
"1 45.372500 4.333096 \n",
"2 54.444868 3.729488 \n",
"3 51.021514 4.603841 \n",
"4 54.027460 6.796657 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('/kaggle/input/cancer-mortality-us/cancer.csv', encoding = 'latin-1')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:06.346176Z",
"iopub.status.busy": "2020-11-19T11:43:06.345296Z",
"iopub.status.idle": "2020-11-19T11:43:06.349639Z",
"shell.execute_reply": "2020-11-19T11:43:06.348926Z"
},
"papermill": {
"duration": 0.028743,
"end_time": "2020-11-19T11:43:06.349780",
"exception": false,
"start_time": "2020-11-19T11:43:06.321037",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"(3047, 34)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:06.395777Z",
"iopub.status.busy": "2020-11-19T11:43:06.394718Z",
"iopub.status.idle": "2020-11-19T11:43:06.400540Z",
"shell.execute_reply": "2020-11-19T11:43:06.399818Z"
},
"papermill": {
"duration": 0.030865,
"end_time": "2020-11-19T11:43:06.400661",
"exception": false,
"start_time": "2020-11-19T11:43:06.369796",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"3047"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(df.Geography.unique())"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.019948,
"end_time": "2020-11-19T11:43:06.441572",
"exception": false,
"start_time": "2020-11-19T11:43:06.421624",
"status": "completed"
},
"tags": []
},
"source": [
"Each value in Geography is unique value, we dont need to use this attribute to analyse or build model"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:06.487326Z",
"iopub.status.busy": "2020-11-19T11:43:06.486494Z",
"iopub.status.idle": "2020-11-19T11:43:06.491469Z",
"shell.execute_reply": "2020-11-19T11:43:06.490740Z"
},
"papermill": {
"duration": 0.029679,
"end_time": "2020-11-19T11:43:06.491585",
"exception": false,
"start_time": "2020-11-19T11:43:06.461906",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"df = df.set_index('Geography')"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.020189,
"end_time": "2020-11-19T11:43:06.532256",
"exception": false,
"start_time": "2020-11-19T11:43:06.512067",
"status": "completed"
},
"tags": []
},
"source": [
"## Check Missing Values in Dataset"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:06.580410Z",
"iopub.status.busy": "2020-11-19T11:43:06.579645Z",
"iopub.status.idle": "2020-11-19T11:43:06.595890Z",
"shell.execute_reply": "2020-11-19T11:43:06.595092Z"
},
"papermill": {
"duration": 0.043246,
"end_time": "2020-11-19T11:43:06.596019",
"exception": false,
"start_time": "2020-11-19T11:43:06.552773",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 3047 entries, Kitsap County, Washington to Geary County, Kansas\n",
"Data columns (total 33 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 avgAnnCount 3047 non-null float64\n",
" 1 avgDeathsPerYear 3047 non-null int64 \n",
" 2 TARGET_deathRate 3047 non-null float64\n",
" 3 incidenceRate 3047 non-null float64\n",
" 4 medIncome 3047 non-null int64 \n",
" 5 popEst2015 3047 non-null int64 \n",
" 6 povertyPercent 3047 non-null float64\n",
" 7 studyPerCap 3047 non-null float64\n",
" 8 binnedInc 3047 non-null object \n",
" 9 MedianAge 3047 non-null float64\n",
" 10 MedianAgeMale 3047 non-null float64\n",
" 11 MedianAgeFemale 3047 non-null float64\n",
" 12 AvgHouseholdSize 3047 non-null float64\n",
" 13 PercentMarried 3047 non-null float64\n",
" 14 PctNoHS18_24 3047 non-null float64\n",
" 15 PctHS18_24 3047 non-null float64\n",
" 16 PctSomeCol18_24 762 non-null float64\n",
" 17 PctBachDeg18_24 3047 non-null float64\n",
" 18 PctHS25_Over 3047 non-null float64\n",
" 19 PctBachDeg25_Over 3047 non-null float64\n",
" 20 PctEmployed16_Over 2895 non-null float64\n",
" 21 PctUnemployed16_Over 3047 non-null float64\n",
" 22 PctPrivateCoverage 3047 non-null float64\n",
" 23 PctPrivateCoverageAlone 2438 non-null float64\n",
" 24 PctEmpPrivCoverage 3047 non-null float64\n",
" 25 PctPublicCoverage 3047 non-null float64\n",
" 26 PctPublicCoverageAlone 3047 non-null float64\n",
" 27 PctWhite 3047 non-null float64\n",
" 28 PctBlack 3047 non-null float64\n",
" 29 PctAsian 3047 non-null float64\n",
" 30 PctOtherRace 3047 non-null float64\n",
" 31 PctMarriedHouseholds 3047 non-null float64\n",
" 32 BirthRate 3047 non-null float64\n",
"dtypes: float64(29), int64(3), object(1)\n",
"memory usage: 809.4+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.021165,
"end_time": "2020-11-19T11:43:06.643588",
"exception": false,
"start_time": "2020-11-19T11:43:06.622423",
"status": "completed"
},
"tags": []
},
"source": [
"## Represent Missing Values by -999\n",
"https://www.lexjansen.com/nesug/nesug01/ps/ps8009.pdf"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:06.704559Z",
"iopub.status.busy": "2020-11-19T11:43:06.701467Z",
"iopub.status.idle": "2020-11-19T11:43:06.708668Z",
"shell.execute_reply": "2020-11-19T11:43:06.707915Z"
},
"papermill": {
"duration": 0.041886,
"end_time": "2020-11-19T11:43:06.708834",
"exception": false,
"start_time": "2020-11-19T11:43:06.666948",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"df['Rep_PctSomeCol18_24'] = df.PctSomeCol18_24.replace(np.nan, -999)\n",
"df['Rep_PctPrivateCoverageAlone'] = df.PctPrivateCoverageAlone.replace(np.nan, -999)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:06.785881Z",
"iopub.status.busy": "2020-11-19T11:43:06.784792Z",
"iopub.status.idle": "2020-11-19T11:43:06.834466Z",
"shell.execute_reply": "2020-11-19T11:43:06.835166Z"
},
"papermill": {
"duration": 0.103078,
"end_time": "2020-11-19T11:43:06.835346",
"exception": false,
"start_time": "2020-11-19T11:43:06.732268",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>avgAnnCount</th>\n",
" <th>avgDeathsPerYear</th>\n",
" <th>TARGET_deathRate</th>\n",
" <th>incidenceRate</th>\n",
" <th>medIncome</th>\n",
" <th>popEst2015</th>\n",
" <th>povertyPercent</th>\n",
" <th>studyPerCap</th>\n",
" <th>binnedInc</th>\n",
" <th>MedianAge</th>\n",
" <th>MedianAgeMale</th>\n",
" <th>MedianAgeFemale</th>\n",
" <th>AvgHouseholdSize</th>\n",
" <th>PercentMarried</th>\n",
" <th>PctNoHS18_24</th>\n",
" <th>PctHS18_24</th>\n",
" <th>PctSomeCol18_24</th>\n",
" <th>PctBachDeg18_24</th>\n",
" <th>PctHS25_Over</th>\n",
" <th>PctBachDeg25_Over</th>\n",
" <th>PctEmployed16_Over</th>\n",
" <th>PctUnemployed16_Over</th>\n",
" <th>PctPrivateCoverage</th>\n",
" <th>PctPrivateCoverageAlone</th>\n",
" <th>PctEmpPrivCoverage</th>\n",
" <th>PctPublicCoverage</th>\n",
" <th>PctPublicCoverageAlone</th>\n",
" <th>PctWhite</th>\n",
" <th>PctBlack</th>\n",
" <th>PctAsian</th>\n",
" <th>PctOtherRace</th>\n",
" <th>PctMarriedHouseholds</th>\n",
" <th>BirthRate</th>\n",
" <th>Rep_PctSomeCol18_24</th>\n",
" <th>Rep_PctPrivateCoverageAlone</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Geography</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Kitsap County, Washington</th>\n",
" <td>1397.0</td>\n",
" <td>469</td>\n",
" <td>164.9</td>\n",
" <td>489.8</td>\n",
" <td>61898</td>\n",
" <td>260131</td>\n",
" <td>11.2</td>\n",
" <td>499.748204</td>\n",
" <td>(61494.5, 125635]</td>\n",
" <td>39.3</td>\n",
" <td>36.9</td>\n",
" <td>41.7</td>\n",
" <td>2.54</td>\n",
" <td>52.5</td>\n",
" <td>11.5</td>\n",
" <td>39.5</td>\n",
" <td>42.1</td>\n",
" <td>6.9</td>\n",
" <td>23.2</td>\n",
" <td>19.6</td>\n",
" <td>51.9</td>\n",
" <td>8.0</td>\n",
" <td>75.1</td>\n",
" <td>NaN</td>\n",
" <td>41.6</td>\n",
" <td>32.9</td>\n",
" <td>14.0</td>\n",
" <td>81.780529</td>\n",
" <td>2.594728</td>\n",
" <td>4.821857</td>\n",
" <td>1.843479</td>\n",
" <td>52.856076</td>\n",
" <td>6.118831</td>\n",
" <td>42.1</td>\n",
" <td>-999.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kittitas County, Washington</th>\n",
" <td>173.0</td>\n",
" <td>70</td>\n",
" <td>161.3</td>\n",
" <td>411.6</td>\n",
" <td>48127</td>\n",
" <td>43269</td>\n",
" <td>18.6</td>\n",
" <td>23.111234</td>\n",
" <td>(48021.6, 51046.4]</td>\n",
" <td>33.0</td>\n",
" <td>32.2</td>\n",
" <td>33.7</td>\n",
" <td>2.34</td>\n",
" <td>44.5</td>\n",
" <td>6.1</td>\n",
" <td>22.4</td>\n",
" <td>64.0</td>\n",
" <td>7.5</td>\n",
" <td>26.0</td>\n",
" <td>22.7</td>\n",
" <td>55.9</td>\n",
" <td>7.8</td>\n",
" <td>70.2</td>\n",
" <td>53.8</td>\n",
" <td>43.6</td>\n",
" <td>31.1</td>\n",
" <td>15.3</td>\n",
" <td>89.228509</td>\n",
" <td>0.969102</td>\n",
" <td>2.246233</td>\n",
" <td>3.741352</td>\n",
" <td>45.372500</td>\n",
" <td>4.333096</td>\n",
" <td>64.0</td>\n",
" <td>53.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Klickitat County, Washington</th>\n",
" <td>102.0</td>\n",
" <td>50</td>\n",
" <td>174.7</td>\n",
" <td>349.7</td>\n",
" <td>49348</td>\n",
" <td>21026</td>\n",
" <td>14.6</td>\n",
" <td>47.560164</td>\n",
" <td>(48021.6, 51046.4]</td>\n",
" <td>45.0</td>\n",
" <td>44.0</td>\n",
" <td>45.8</td>\n",
" <td>2.62</td>\n",
" <td>54.2</td>\n",
" <td>24.0</td>\n",
" <td>36.6</td>\n",
" <td>NaN</td>\n",
" <td>9.5</td>\n",
" <td>29.0</td>\n",
" <td>16.0</td>\n",
" <td>45.9</td>\n",
" <td>7.0</td>\n",
" <td>63.7</td>\n",
" <td>43.5</td>\n",
" <td>34.9</td>\n",
" <td>42.1</td>\n",
" <td>21.1</td>\n",
" <td>90.922190</td>\n",
" <td>0.739673</td>\n",
" <td>0.465898</td>\n",
" <td>2.747358</td>\n",
" <td>54.444868</td>\n",
" <td>3.729488</td>\n",
" <td>-999.0</td>\n",
" <td>43.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lewis County, Washington</th>\n",
" <td>427.0</td>\n",
" <td>202</td>\n",
" <td>194.8</td>\n",
" <td>430.4</td>\n",
" <td>44243</td>\n",
" <td>75882</td>\n",
" <td>17.1</td>\n",
" <td>342.637253</td>\n",
" <td>(42724.4, 45201]</td>\n",
" <td>42.8</td>\n",
" <td>42.2</td>\n",
" <td>43.4</td>\n",
" <td>2.52</td>\n",
" <td>52.7</td>\n",
" <td>20.2</td>\n",
" <td>41.2</td>\n",
" <td>36.1</td>\n",
" <td>2.5</td>\n",
" <td>31.6</td>\n",
" <td>9.3</td>\n",
" <td>48.3</td>\n",
" <td>12.1</td>\n",
" <td>58.4</td>\n",
" <td>40.3</td>\n",
" <td>35.0</td>\n",
" <td>45.3</td>\n",
" <td>25.0</td>\n",
" <td>91.744686</td>\n",
" <td>0.782626</td>\n",
" <td>1.161359</td>\n",
" <td>1.362643</td>\n",
" <td>51.021514</td>\n",
" <td>4.603841</td>\n",
" <td>36.1</td>\n",
" <td>40.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lincoln County, Washington</th>\n",
" <td>57.0</td>\n",
" <td>26</td>\n",
" <td>144.4</td>\n",
" <td>350.1</td>\n",
" <td>49955</td>\n",
" <td>10321</td>\n",
" <td>12.5</td>\n",
" <td>0.000000</td>\n",
" <td>(48021.6, 51046.4]</td>\n",
" <td>48.3</td>\n",
" <td>47.8</td>\n",
" <td>48.9</td>\n",
" <td>2.34</td>\n",
" <td>57.8</td>\n",
" <td>14.9</td>\n",
" <td>43.0</td>\n",
" <td>40.0</td>\n",
" <td>2.0</td>\n",
" <td>33.4</td>\n",
" <td>15.0</td>\n",
" <td>48.2</td>\n",
" <td>4.8</td>\n",
" <td>61.6</td>\n",
" <td>43.9</td>\n",
" <td>35.1</td>\n",
" <td>44.0</td>\n",
" <td>22.7</td>\n",
" <td>94.104024</td>\n",
" <td>0.270192</td>\n",
" <td>0.665830</td>\n",
" <td>0.492135</td>\n",
" <td>54.027460</td>\n",
" <td>6.796657</td>\n",
" <td>40.0</td>\n",
" <td>43.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" avgAnnCount avgDeathsPerYear TARGET_deathRate \\\n",
"Geography \n",
"Kitsap County, Washington 1397.0 469 164.9 \n",
"Kittitas County, Washington 173.0 70 161.3 \n",
"Klickitat County, Washington 102.0 50 174.7 \n",
"Lewis County, Washington 427.0 202 194.8 \n",
"Lincoln County, Washington 57.0 26 144.4 \n",
"\n",
" incidenceRate medIncome popEst2015 \\\n",
"Geography \n",
"Kitsap County, Washington 489.8 61898 260131 \n",
"Kittitas County, Washington 411.6 48127 43269 \n",
"Klickitat County, Washington 349.7 49348 21026 \n",
"Lewis County, Washington 430.4 44243 75882 \n",
"Lincoln County, Washington 350.1 49955 10321 \n",
"\n",
" povertyPercent studyPerCap binnedInc \\\n",
"Geography \n",
"Kitsap County, Washington 11.2 499.748204 (61494.5, 125635] \n",
"Kittitas County, Washington 18.6 23.111234 (48021.6, 51046.4] \n",
"Klickitat County, Washington 14.6 47.560164 (48021.6, 51046.4] \n",
"Lewis County, Washington 17.1 342.637253 (42724.4, 45201] \n",
"Lincoln County, Washington 12.5 0.000000 (48021.6, 51046.4] \n",
"\n",
" MedianAge MedianAgeMale MedianAgeFemale \\\n",
"Geography \n",
"Kitsap County, Washington 39.3 36.9 41.7 \n",
"Kittitas County, Washington 33.0 32.2 33.7 \n",
"Klickitat County, Washington 45.0 44.0 45.8 \n",
"Lewis County, Washington 42.8 42.2 43.4 \n",
"Lincoln County, Washington 48.3 47.8 48.9 \n",
"\n",
" AvgHouseholdSize PercentMarried PctNoHS18_24 \\\n",
"Geography \n",
"Kitsap County, Washington 2.54 52.5 11.5 \n",
"Kittitas County, Washington 2.34 44.5 6.1 \n",
"Klickitat County, Washington 2.62 54.2 24.0 \n",
"Lewis County, Washington 2.52 52.7 20.2 \n",
"Lincoln County, Washington 2.34 57.8 14.9 \n",
"\n",
" PctHS18_24 PctSomeCol18_24 PctBachDeg18_24 \\\n",
"Geography \n",
"Kitsap County, Washington 39.5 42.1 6.9 \n",
"Kittitas County, Washington 22.4 64.0 7.5 \n",
"Klickitat County, Washington 36.6 NaN 9.5 \n",
"Lewis County, Washington 41.2 36.1 2.5 \n",
"Lincoln County, Washington 43.0 40.0 2.0 \n",
"\n",
" PctHS25_Over PctBachDeg25_Over \\\n",
"Geography \n",
"Kitsap County, Washington 23.2 19.6 \n",
"Kittitas County, Washington 26.0 22.7 \n",
"Klickitat County, Washington 29.0 16.0 \n",
"Lewis County, Washington 31.6 9.3 \n",
"Lincoln County, Washington 33.4 15.0 \n",
"\n",
" PctEmployed16_Over PctUnemployed16_Over \\\n",
"Geography \n",
"Kitsap County, Washington 51.9 8.0 \n",
"Kittitas County, Washington 55.9 7.8 \n",
"Klickitat County, Washington 45.9 7.0 \n",
"Lewis County, Washington 48.3 12.1 \n",
"Lincoln County, Washington 48.2 4.8 \n",
"\n",
" PctPrivateCoverage PctPrivateCoverageAlone \\\n",
"Geography \n",
"Kitsap County, Washington 75.1 NaN \n",
"Kittitas County, Washington 70.2 53.8 \n",
"Klickitat County, Washington 63.7 43.5 \n",
"Lewis County, Washington 58.4 40.3 \n",
"Lincoln County, Washington 61.6 43.9 \n",
"\n",
" PctEmpPrivCoverage PctPublicCoverage \\\n",
"Geography \n",
"Kitsap County, Washington 41.6 32.9 \n",
"Kittitas County, Washington 43.6 31.1 \n",
"Klickitat County, Washington 34.9 42.1 \n",
"Lewis County, Washington 35.0 45.3 \n",
"Lincoln County, Washington 35.1 44.0 \n",
"\n",
" PctPublicCoverageAlone PctWhite PctBlack \\\n",
"Geography \n",
"Kitsap County, Washington 14.0 81.780529 2.594728 \n",
"Kittitas County, Washington 15.3 89.228509 0.969102 \n",
"Klickitat County, Washington 21.1 90.922190 0.739673 \n",
"Lewis County, Washington 25.0 91.744686 0.782626 \n",
"Lincoln County, Washington 22.7 94.104024 0.270192 \n",
"\n",
" PctAsian PctOtherRace PctMarriedHouseholds \\\n",
"Geography \n",
"Kitsap County, Washington 4.821857 1.843479 52.856076 \n",
"Kittitas County, Washington 2.246233 3.741352 45.372500 \n",
"Klickitat County, Washington 0.465898 2.747358 54.444868 \n",
"Lewis County, Washington 1.161359 1.362643 51.021514 \n",
"Lincoln County, Washington 0.665830 0.492135 54.027460 \n",
"\n",
" BirthRate Rep_PctSomeCol18_24 \\\n",
"Geography \n",
"Kitsap County, Washington 6.118831 42.1 \n",
"Kittitas County, Washington 4.333096 64.0 \n",
"Klickitat County, Washington 3.729488 -999.0 \n",
"Lewis County, Washington 4.603841 36.1 \n",
"Lincoln County, Washington 6.796657 40.0 \n",
"\n",
" Rep_PctPrivateCoverageAlone \n",
"Geography \n",
"Kitsap County, Washington -999.0 \n",
"Kittitas County, Washington 53.8 \n",
"Klickitat County, Washington 43.5 \n",
"Lewis County, Washington 40.3 \n",
"Lincoln County, Washington 43.9 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.022828,
"end_time": "2020-11-19T11:43:06.881394",
"exception": false,
"start_time": "2020-11-19T11:43:06.858566",
"status": "completed"
},
"tags": []
},
"source": [
"## Get Average of binnedInc"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:06.965057Z",
"iopub.status.busy": "2020-11-19T11:43:06.960021Z",
"iopub.status.idle": "2020-11-19T11:43:07.072109Z",
"shell.execute_reply": "2020-11-19T11:43:07.072887Z"
},
"papermill": {
"duration": 0.165809,
"end_time": "2020-11-19T11:43:07.073090",
"exception": false,
"start_time": "2020-11-19T11:43:06.907281",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>avgAnnCount</th>\n",
" <th>avgDeathsPerYear</th>\n",
" <th>TARGET_deathRate</th>\n",
" <th>incidenceRate</th>\n",
" <th>medIncome</th>\n",
" <th>popEst2015</th>\n",
" <th>povertyPercent</th>\n",
" <th>studyPerCap</th>\n",
" <th>binnedInc</th>\n",
" <th>MedianAge</th>\n",
" <th>MedianAgeMale</th>\n",
" <th>MedianAgeFemale</th>\n",
" <th>AvgHouseholdSize</th>\n",
" <th>PercentMarried</th>\n",
" <th>PctNoHS18_24</th>\n",
" <th>PctHS18_24</th>\n",
" <th>PctSomeCol18_24</th>\n",
" <th>PctBachDeg18_24</th>\n",
" <th>PctHS25_Over</th>\n",
" <th>PctBachDeg25_Over</th>\n",
" <th>PctEmployed16_Over</th>\n",
" <th>PctUnemployed16_Over</th>\n",
" <th>PctPrivateCoverage</th>\n",
" <th>PctPrivateCoverageAlone</th>\n",
" <th>PctEmpPrivCoverage</th>\n",
" <th>PctPublicCoverage</th>\n",
" <th>PctPublicCoverageAlone</th>\n",
" <th>PctWhite</th>\n",
" <th>PctBlack</th>\n",
" <th>PctAsian</th>\n",
" <th>PctOtherRace</th>\n",
" <th>PctMarriedHouseholds</th>\n",
" <th>BirthRate</th>\n",
" <th>Rep_PctSomeCol18_24</th>\n",
" <th>Rep_PctPrivateCoverageAlone</th>\n",
" <th>avgbinnedInc</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Geography</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Kitsap County, Washington</th>\n",
" <td>1397.0</td>\n",
" <td>469</td>\n",
" <td>164.9</td>\n",
" <td>489.8</td>\n",
" <td>61898</td>\n",
" <td>260131</td>\n",
" <td>11.2</td>\n",
" <td>499.748204</td>\n",
" <td>61494.5,125635</td>\n",
" <td>39.3</td>\n",
" <td>36.9</td>\n",
" <td>41.7</td>\n",
" <td>2.54</td>\n",
" <td>52.5</td>\n",
" <td>11.5</td>\n",
" <td>39.5</td>\n",
" <td>42.1</td>\n",
" <td>6.9</td>\n",
" <td>23.2</td>\n",
" <td>19.6</td>\n",
" <td>51.9</td>\n",
" <td>8.0</td>\n",
" <td>75.1</td>\n",
" <td>NaN</td>\n",
" <td>41.6</td>\n",
" <td>32.9</td>\n",
" <td>14.0</td>\n",
" <td>81.780529</td>\n",
" <td>2.594728</td>\n",
" <td>4.821857</td>\n",
" <td>1.843479</td>\n",
" <td>52.856076</td>\n",
" <td>6.118831</td>\n",
" <td>42.1</td>\n",
" <td>-999.0</td>\n",
" <td>93564.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kittitas County, Washington</th>\n",
" <td>173.0</td>\n",
" <td>70</td>\n",
" <td>161.3</td>\n",
" <td>411.6</td>\n",
" <td>48127</td>\n",
" <td>43269</td>\n",
" <td>18.6</td>\n",
" <td>23.111234</td>\n",
" <td>48021.6,51046.4</td>\n",
" <td>33.0</td>\n",
" <td>32.2</td>\n",
" <td>33.7</td>\n",
" <td>2.34</td>\n",
" <td>44.5</td>\n",
" <td>6.1</td>\n",
" <td>22.4</td>\n",
" <td>64.0</td>\n",
" <td>7.5</td>\n",
" <td>26.0</td>\n",
" <td>22.7</td>\n",
" <td>55.9</td>\n",
" <td>7.8</td>\n",
" <td>70.2</td>\n",
" <td>53.8</td>\n",
" <td>43.6</td>\n",
" <td>31.1</td>\n",
" <td>15.3</td>\n",
" <td>89.228509</td>\n",
" <td>0.969102</td>\n",
" <td>2.246233</td>\n",
" <td>3.741352</td>\n",
" <td>45.372500</td>\n",
" <td>4.333096</td>\n",
" <td>64.0</td>\n",
" <td>53.8</td>\n",
" <td>49534.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Klickitat County, Washington</th>\n",
" <td>102.0</td>\n",
" <td>50</td>\n",
" <td>174.7</td>\n",
" <td>349.7</td>\n",
" <td>49348</td>\n",
" <td>21026</td>\n",
" <td>14.6</td>\n",
" <td>47.560164</td>\n",
" <td>48021.6,51046.4</td>\n",
" <td>45.0</td>\n",
" <td>44.0</td>\n",
" <td>45.8</td>\n",
" <td>2.62</td>\n",
" <td>54.2</td>\n",
" <td>24.0</td>\n",
" <td>36.6</td>\n",
" <td>NaN</td>\n",
" <td>9.5</td>\n",
" <td>29.0</td>\n",
" <td>16.0</td>\n",
" <td>45.9</td>\n",
" <td>7.0</td>\n",
" <td>63.7</td>\n",
" <td>43.5</td>\n",
" <td>34.9</td>\n",
" <td>42.1</td>\n",
" <td>21.1</td>\n",
" <td>90.922190</td>\n",
" <td>0.739673</td>\n",
" <td>0.465898</td>\n",
" <td>2.747358</td>\n",
" <td>54.444868</td>\n",
" <td>3.729488</td>\n",
" <td>-999.0</td>\n",
" <td>43.5</td>\n",
" <td>49534.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lewis County, Washington</th>\n",
" <td>427.0</td>\n",
" <td>202</td>\n",
" <td>194.8</td>\n",
" <td>430.4</td>\n",
" <td>44243</td>\n",
" <td>75882</td>\n",
" <td>17.1</td>\n",
" <td>342.637253</td>\n",
" <td>42724.4,45201</td>\n",
" <td>42.8</td>\n",
" <td>42.2</td>\n",
" <td>43.4</td>\n",
" <td>2.52</td>\n",
" <td>52.7</td>\n",
" <td>20.2</td>\n",
" <td>41.2</td>\n",
" <td>36.1</td>\n",
" <td>2.5</td>\n",
" <td>31.6</td>\n",
" <td>9.3</td>\n",
" <td>48.3</td>\n",
" <td>12.1</td>\n",
" <td>58.4</td>\n",
" <td>40.3</td>\n",
" <td>35.0</td>\n",
" <td>45.3</td>\n",
" <td>25.0</td>\n",
" <td>91.744686</td>\n",
" <td>0.782626</td>\n",
" <td>1.161359</td>\n",
" <td>1.362643</td>\n",
" <td>51.021514</td>\n",
" <td>4.603841</td>\n",
" <td>36.1</td>\n",
" <td>40.3</td>\n",
" <td>43962.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lincoln County, Washington</th>\n",
" <td>57.0</td>\n",
" <td>26</td>\n",
" <td>144.4</td>\n",
" <td>350.1</td>\n",
" <td>49955</td>\n",
" <td>10321</td>\n",
" <td>12.5</td>\n",
" <td>0.000000</td>\n",
" <td>48021.6,51046.4</td>\n",
" <td>48.3</td>\n",
" <td>47.8</td>\n",
" <td>48.9</td>\n",
" <td>2.34</td>\n",
" <td>57.8</td>\n",
" <td>14.9</td>\n",
" <td>43.0</td>\n",
" <td>40.0</td>\n",
" <td>2.0</td>\n",
" <td>33.4</td>\n",
" <td>15.0</td>\n",
" <td>48.2</td>\n",
" <td>4.8</td>\n",
" <td>61.6</td>\n",
" <td>43.9</td>\n",
" <td>35.1</td>\n",
" <td>44.0</td>\n",
" <td>22.7</td>\n",
" <td>94.104024</td>\n",
" <td>0.270192</td>\n",
" <td>0.665830</td>\n",
" <td>0.492135</td>\n",
" <td>54.027460</td>\n",
" <td>6.796657</td>\n",
" <td>40.0</td>\n",
" <td>43.9</td>\n",
" <td>49534.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" avgAnnCount avgDeathsPerYear TARGET_deathRate \\\n",
"Geography \n",
"Kitsap County, Washington 1397.0 469 164.9 \n",
"Kittitas County, Washington 173.0 70 161.3 \n",
"Klickitat County, Washington 102.0 50 174.7 \n",
"Lewis County, Washington 427.0 202 194.8 \n",
"Lincoln County, Washington 57.0 26 144.4 \n",
"\n",
" incidenceRate medIncome popEst2015 \\\n",
"Geography \n",
"Kitsap County, Washington 489.8 61898 260131 \n",
"Kittitas County, Washington 411.6 48127 43269 \n",
"Klickitat County, Washington 349.7 49348 21026 \n",
"Lewis County, Washington 430.4 44243 75882 \n",
"Lincoln County, Washington 350.1 49955 10321 \n",
"\n",
" povertyPercent studyPerCap binnedInc \\\n",
"Geography \n",
"Kitsap County, Washington 11.2 499.748204 61494.5,125635 \n",
"Kittitas County, Washington 18.6 23.111234 48021.6,51046.4 \n",
"Klickitat County, Washington 14.6 47.560164 48021.6,51046.4 \n",
"Lewis County, Washington 17.1 342.637253 42724.4,45201 \n",
"Lincoln County, Washington 12.5 0.000000 48021.6,51046.4 \n",
"\n",
" MedianAge MedianAgeMale MedianAgeFemale \\\n",
"Geography \n",
"Kitsap County, Washington 39.3 36.9 41.7 \n",
"Kittitas County, Washington 33.0 32.2 33.7 \n",
"Klickitat County, Washington 45.0 44.0 45.8 \n",
"Lewis County, Washington 42.8 42.2 43.4 \n",
"Lincoln County, Washington 48.3 47.8 48.9 \n",
"\n",
" AvgHouseholdSize PercentMarried PctNoHS18_24 \\\n",
"Geography \n",
"Kitsap County, Washington 2.54 52.5 11.5 \n",
"Kittitas County, Washington 2.34 44.5 6.1 \n",
"Klickitat County, Washington 2.62 54.2 24.0 \n",
"Lewis County, Washington 2.52 52.7 20.2 \n",
"Lincoln County, Washington 2.34 57.8 14.9 \n",
"\n",
" PctHS18_24 PctSomeCol18_24 PctBachDeg18_24 \\\n",
"Geography \n",
"Kitsap County, Washington 39.5 42.1 6.9 \n",
"Kittitas County, Washington 22.4 64.0 7.5 \n",
"Klickitat County, Washington 36.6 NaN 9.5 \n",
"Lewis County, Washington 41.2 36.1 2.5 \n",
"Lincoln County, Washington 43.0 40.0 2.0 \n",
"\n",
" PctHS25_Over PctBachDeg25_Over \\\n",
"Geography \n",
"Kitsap County, Washington 23.2 19.6 \n",
"Kittitas County, Washington 26.0 22.7 \n",
"Klickitat County, Washington 29.0 16.0 \n",
"Lewis County, Washington 31.6 9.3 \n",
"Lincoln County, Washington 33.4 15.0 \n",
"\n",
" PctEmployed16_Over PctUnemployed16_Over \\\n",
"Geography \n",
"Kitsap County, Washington 51.9 8.0 \n",
"Kittitas County, Washington 55.9 7.8 \n",
"Klickitat County, Washington 45.9 7.0 \n",
"Lewis County, Washington 48.3 12.1 \n",
"Lincoln County, Washington 48.2 4.8 \n",
"\n",
" PctPrivateCoverage PctPrivateCoverageAlone \\\n",
"Geography \n",
"Kitsap County, Washington 75.1 NaN \n",
"Kittitas County, Washington 70.2 53.8 \n",
"Klickitat County, Washington 63.7 43.5 \n",
"Lewis County, Washington 58.4 40.3 \n",
"Lincoln County, Washington 61.6 43.9 \n",
"\n",
" PctEmpPrivCoverage PctPublicCoverage \\\n",
"Geography \n",
"Kitsap County, Washington 41.6 32.9 \n",
"Kittitas County, Washington 43.6 31.1 \n",
"Klickitat County, Washington 34.9 42.1 \n",
"Lewis County, Washington 35.0 45.3 \n",
"Lincoln County, Washington 35.1 44.0 \n",
"\n",
" PctPublicCoverageAlone PctWhite PctBlack \\\n",
"Geography \n",
"Kitsap County, Washington 14.0 81.780529 2.594728 \n",
"Kittitas County, Washington 15.3 89.228509 0.969102 \n",
"Klickitat County, Washington 21.1 90.922190 0.739673 \n",
"Lewis County, Washington 25.0 91.744686 0.782626 \n",
"Lincoln County, Washington 22.7 94.104024 0.270192 \n",
"\n",
" PctAsian PctOtherRace PctMarriedHouseholds \\\n",
"Geography \n",
"Kitsap County, Washington 4.821857 1.843479 52.856076 \n",
"Kittitas County, Washington 2.246233 3.741352 45.372500 \n",
"Klickitat County, Washington 0.465898 2.747358 54.444868 \n",
"Lewis County, Washington 1.161359 1.362643 51.021514 \n",
"Lincoln County, Washington 0.665830 0.492135 54.027460 \n",
"\n",
" BirthRate Rep_PctSomeCol18_24 \\\n",
"Geography \n",
"Kitsap County, Washington 6.118831 42.1 \n",
"Kittitas County, Washington 4.333096 64.0 \n",
"Klickitat County, Washington 3.729488 -999.0 \n",
"Lewis County, Washington 4.603841 36.1 \n",
"Lincoln County, Washington 6.796657 40.0 \n",
"\n",
" Rep_PctPrivateCoverageAlone avgbinnedInc \n",
"Geography \n",
"Kitsap County, Washington -999.0 93564.75 \n",
"Kittitas County, Washington 53.8 49534.00 \n",
"Klickitat County, Washington 43.5 49534.00 \n",
"Lewis County, Washington 40.3 43962.70 \n",
"Lincoln County, Washington 43.9 49534.00 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Use regular expression to remove ( and ] \n",
"df['binnedInc'] = df['binnedInc'].apply(lambda x: re.sub('[^0-9a-zA-Z:,.]+', '', x))\n",
"samples = df['binnedInc'].str.split(',',expand=True).astype(float)\n",
"avg = (samples[0] + samples[1])/2\n",
"df['avgbinnedInc'] = avg\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.03321,
"end_time": "2020-11-19T11:43:07.143946",
"exception": false,
"start_time": "2020-11-19T11:43:07.110736",
"status": "completed"
},
"tags": []
},
"source": [
"## Save the pre-processed dataset"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:07.224317Z",
"iopub.status.busy": "2020-11-19T11:43:07.223244Z",
"iopub.status.idle": "2020-11-19T11:43:07.227049Z",
"shell.execute_reply": "2020-11-19T11:43:07.228019Z"
},
"papermill": {
"duration": 0.05058,
"end_time": "2020-11-19T11:43:07.228185",
"exception": false,
"start_time": "2020-11-19T11:43:07.177605",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"with open('raw_preprocess.p', 'wb') as file:\n",
" pickle.dump(df, file)"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.023914,
"end_time": "2020-11-19T11:43:07.277834",
"exception": false,
"start_time": "2020-11-19T11:43:07.253920",
"status": "completed"
},
"tags": []
},
"source": [
"## Exploratory Data Analysis"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:07.334631Z",
"iopub.status.busy": "2020-11-19T11:43:07.333931Z",
"iopub.status.idle": "2020-11-19T11:43:07.339553Z",
"shell.execute_reply": "2020-11-19T11:43:07.338956Z"
},
"papermill": {
"duration": 0.036281,
"end_time": "2020-11-19T11:43:07.339667",
"exception": false,
"start_time": "2020-11-19T11:43:07.303386",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"data = df.drop(['binnedInc', 'PctPrivateCoverageAlone', 'PctSomeCol18_24'], axis = 1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.023671,
"end_time": "2020-11-19T11:43:07.387611",
"exception": false,
"start_time": "2020-11-19T11:43:07.363940",
"status": "completed"
},
"tags": []
},
"source": [
"#### Check data type of all attributes"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:07.444184Z",
"iopub.status.busy": "2020-11-19T11:43:07.443194Z",
"iopub.status.idle": "2020-11-19T11:43:07.448013Z",
"shell.execute_reply": "2020-11-19T11:43:07.447364Z"
},
"papermill": {
"duration": 0.036563,
"end_time": "2020-11-19T11:43:07.448126",
"exception": false,
"start_time": "2020-11-19T11:43:07.411563",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"avgAnnCount float64\n",
"avgDeathsPerYear int64\n",
"TARGET_deathRate float64\n",
"incidenceRate float64\n",
"medIncome int64\n",
"popEst2015 int64\n",
"povertyPercent float64\n",
"studyPerCap float64\n",
"MedianAge float64\n",
"MedianAgeMale float64\n",
"MedianAgeFemale float64\n",
"AvgHouseholdSize float64\n",
"PercentMarried float64\n",
"PctNoHS18_24 float64\n",
"PctHS18_24 float64\n",
"PctBachDeg18_24 float64\n",
"PctHS25_Over float64\n",
"PctBachDeg25_Over float64\n",
"PctEmployed16_Over float64\n",
"PctUnemployed16_Over float64\n",
"PctPrivateCoverage float64\n",
"PctEmpPrivCoverage float64\n",
"PctPublicCoverage float64\n",
"PctPublicCoverageAlone float64\n",
"PctWhite float64\n",
"PctBlack float64\n",
"PctAsian float64\n",
"PctOtherRace float64\n",
"PctMarriedHouseholds float64\n",
"BirthRate float64\n",
"Rep_PctSomeCol18_24 float64\n",
"Rep_PctPrivateCoverageAlone float64\n",
"avgbinnedInc float64\n",
"dtype: object"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.024687,
"end_time": "2020-11-19T11:43:07.497525",
"exception": false,
"start_time": "2020-11-19T11:43:07.472838",
"status": "completed"
},
"tags": []
},
"source": [
"#### Summary stats for all attributes"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:07.554988Z",
"iopub.status.busy": "2020-11-19T11:43:07.553929Z",
"iopub.status.idle": "2020-11-19T11:43:07.677260Z",
"shell.execute_reply": "2020-11-19T11:43:07.676622Z"
},
"papermill": {
"duration": 0.154558,
"end_time": "2020-11-19T11:43:07.677380",
"exception": false,
"start_time": "2020-11-19T11:43:07.522822",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>avgAnnCount</th>\n",
" <th>avgDeathsPerYear</th>\n",
" <th>TARGET_deathRate</th>\n",
" <th>incidenceRate</th>\n",
" <th>medIncome</th>\n",
" <th>popEst2015</th>\n",
" <th>povertyPercent</th>\n",
" <th>studyPerCap</th>\n",
" <th>MedianAge</th>\n",
" <th>MedianAgeMale</th>\n",
" <th>MedianAgeFemale</th>\n",
" <th>AvgHouseholdSize</th>\n",
" <th>PercentMarried</th>\n",
" <th>PctNoHS18_24</th>\n",
" <th>PctHS18_24</th>\n",
" <th>PctBachDeg18_24</th>\n",
" <th>PctHS25_Over</th>\n",
" <th>PctBachDeg25_Over</th>\n",
" <th>PctEmployed16_Over</th>\n",
" <th>PctUnemployed16_Over</th>\n",
" <th>PctPrivateCoverage</th>\n",
" <th>PctEmpPrivCoverage</th>\n",
" <th>PctPublicCoverage</th>\n",
" <th>PctPublicCoverageAlone</th>\n",
" <th>PctWhite</th>\n",
" <th>PctBlack</th>\n",
" <th>PctAsian</th>\n",
" <th>PctOtherRace</th>\n",
" <th>PctMarriedHouseholds</th>\n",
" <th>BirthRate</th>\n",
" <th>Rep_PctSomeCol18_24</th>\n",
" <th>Rep_PctPrivateCoverageAlone</th>\n",
" <th>avgbinnedInc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3.047000e+03</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>2895.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" <td>3047.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>606.338544</td>\n",
" <td>185.965868</td>\n",
" <td>178.664063</td>\n",
" <td>448.268586</td>\n",
" <td>47063.281917</td>\n",
" <td>1.026374e+05</td>\n",
" <td>16.878175</td>\n",
" <td>155.399415</td>\n",
" <td>45.272333</td>\n",
" <td>39.570725</td>\n",
" <td>42.145323</td>\n",
" <td>2.479662</td>\n",
" <td>51.773679</td>\n",
" <td>18.224450</td>\n",
" <td>35.002068</td>\n",
" <td>6.158287</td>\n",
" <td>34.804660</td>\n",
" <td>13.282015</td>\n",
" <td>54.152642</td>\n",
" <td>7.852412</td>\n",
" <td>64.354939</td>\n",
" <td>41.196324</td>\n",
" <td>36.252642</td>\n",
" <td>19.240072</td>\n",
" <td>83.645286</td>\n",
" <td>9.107978</td>\n",
" <td>1.253965</td>\n",
" <td>1.983523</td>\n",
" <td>51.243872</td>\n",
" <td>5.640306</td>\n",
" <td>-738.920414</td>\n",
" <td>-160.899475</td>\n",
" <td>48878.118280</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1416.356223</td>\n",
" <td>504.134286</td>\n",
" <td>27.751511</td>\n",
" <td>54.560733</td>\n",
" <td>12040.090836</td>\n",
" <td>3.290592e+05</td>\n",
" <td>6.409087</td>\n",
" <td>529.628366</td>\n",
" <td>45.304480</td>\n",
" <td>5.226017</td>\n",
" <td>5.292849</td>\n",
" <td>0.429174</td>\n",
" <td>6.896928</td>\n",
" <td>8.093064</td>\n",
" <td>9.069722</td>\n",
" <td>4.529059</td>\n",
" <td>7.034924</td>\n",
" <td>5.394756</td>\n",
" <td>8.315064</td>\n",
" <td>3.452371</td>\n",
" <td>10.647057</td>\n",
" <td>9.447687</td>\n",
" <td>7.841741</td>\n",
" <td>6.113041</td>\n",
" <td>16.380025</td>\n",
" <td>14.534538</td>\n",
" <td>2.610276</td>\n",
" <td>3.517710</td>\n",
" <td>6.572814</td>\n",
" <td>1.985816</td>\n",
" <td>450.480706</td>\n",
" <td>419.044163</td>\n",
" <td>16889.719362</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>6.000000</td>\n",
" <td>3.000000</td>\n",
" <td>59.700000</td>\n",
" <td>201.300000</td>\n",
" <td>22640.000000</td>\n",
" <td>8.270000e+02</td>\n",
" <td>3.200000</td>\n",
" <td>0.000000</td>\n",
" <td>22.300000</td>\n",
" <td>22.400000</td>\n",
" <td>22.300000</td>\n",
" <td>0.022100</td>\n",
" <td>23.100000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7.500000</td>\n",
" <td>2.500000</td>\n",
" <td>17.600000</td>\n",
" <td>0.400000</td>\n",
" <td>22.300000</td>\n",
" <td>13.500000</td>\n",
" <td>11.200000</td>\n",
" <td>2.600000</td>\n",
" <td>10.199155</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>22.992490</td>\n",
" <td>0.000000</td>\n",
" <td>-999.000000</td>\n",
" <td>-999.000000</td>\n",
" <td>28429.050000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>76.000000</td>\n",
" <td>28.000000</td>\n",
" <td>161.200000</td>\n",
" <td>420.300000</td>\n",
" <td>38882.500000</td>\n",
" <td>1.168400e+04</td>\n",
" <td>12.150000</td>\n",
" <td>0.000000</td>\n",
" <td>37.700000</td>\n",
" <td>36.350000</td>\n",
" <td>39.100000</td>\n",
" <td>2.370000</td>\n",
" <td>47.750000</td>\n",
" <td>12.800000</td>\n",
" <td>29.200000</td>\n",
" <td>3.100000</td>\n",
" <td>30.400000</td>\n",
" <td>9.400000</td>\n",
" <td>48.600000</td>\n",
" <td>5.500000</td>\n",
" <td>57.200000</td>\n",
" <td>34.500000</td>\n",
" <td>30.900000</td>\n",
" <td>14.850000</td>\n",
" <td>77.296180</td>\n",
" <td>0.620675</td>\n",
" <td>0.254199</td>\n",
" <td>0.295172</td>\n",
" <td>47.763063</td>\n",
" <td>4.521419</td>\n",
" <td>-999.000000</td>\n",
" <td>33.200000</td>\n",
" <td>38888.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>171.000000</td>\n",
" <td>61.000000</td>\n",
" <td>178.100000</td>\n",
" <td>453.549422</td>\n",
" <td>45207.000000</td>\n",
" <td>2.664300e+04</td>\n",
" <td>15.900000</td>\n",
" <td>0.000000</td>\n",
" <td>41.000000</td>\n",
" <td>39.600000</td>\n",
" <td>42.400000</td>\n",
" <td>2.500000</td>\n",
" <td>52.400000</td>\n",
" <td>17.100000</td>\n",
" <td>34.700000</td>\n",
" <td>5.400000</td>\n",
" <td>35.300000</td>\n",
" <td>12.300000</td>\n",
" <td>54.500000</td>\n",
" <td>7.600000</td>\n",
" <td>65.100000</td>\n",
" <td>41.100000</td>\n",
" <td>36.300000</td>\n",
" <td>18.800000</td>\n",
" <td>90.059774</td>\n",
" <td>2.247576</td>\n",
" <td>0.549812</td>\n",
" <td>0.826185</td>\n",
" <td>51.669941</td>\n",
" <td>5.381478</td>\n",
" <td>-999.000000</td>\n",
" <td>45.100000</td>\n",
" <td>46611.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>518.000000</td>\n",
" <td>149.000000</td>\n",
" <td>195.200000</td>\n",
" <td>480.850000</td>\n",
" <td>52492.000000</td>\n",
" <td>6.867100e+04</td>\n",
" <td>20.400000</td>\n",
" <td>83.650776</td>\n",
" <td>44.000000</td>\n",
" <td>42.500000</td>\n",
" <td>45.300000</td>\n",
" <td>2.630000</td>\n",
" <td>56.400000</td>\n",
" <td>22.700000</td>\n",
" <td>40.700000</td>\n",
" <td>8.200000</td>\n",
" <td>39.650000</td>\n",
" <td>16.100000</td>\n",
" <td>60.300000</td>\n",
" <td>9.700000</td>\n",
" <td>72.100000</td>\n",
" <td>47.700000</td>\n",
" <td>41.550000</td>\n",
" <td>23.100000</td>\n",
" <td>95.451693</td>\n",
" <td>10.509732</td>\n",
" <td>1.221037</td>\n",
" <td>2.177960</td>\n",
" <td>55.395132</td>\n",
" <td>6.493677</td>\n",
" <td>-495.950000</td>\n",
" <td>53.800000</td>\n",
" <td>52796.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>38150.000000</td>\n",
" <td>14010.000000</td>\n",
" <td>362.800000</td>\n",
" <td>1206.900000</td>\n",
" <td>125635.000000</td>\n",
" <td>1.017029e+07</td>\n",
" <td>47.400000</td>\n",
" <td>9762.308998</td>\n",
" <td>624.000000</td>\n",
" <td>64.700000</td>\n",
" <td>65.700000</td>\n",
" <td>3.970000</td>\n",
" <td>72.500000</td>\n",
" <td>64.100000</td>\n",
" <td>72.500000</td>\n",
" <td>51.800000</td>\n",
" <td>54.800000</td>\n",
" <td>42.200000</td>\n",
" <td>80.100000</td>\n",
" <td>29.400000</td>\n",
" <td>92.300000</td>\n",
" <td>70.700000</td>\n",
" <td>65.100000</td>\n",
" <td>46.600000</td>\n",
" <td>100.000000</td>\n",
" <td>85.947799</td>\n",
" <td>42.619425</td>\n",
" <td>41.930251</td>\n",
" <td>78.075397</td>\n",
" <td>21.326165</td>\n",
" <td>79.000000</td>\n",
" <td>78.900000</td>\n",
" <td>93564.750000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" avgAnnCount avgDeathsPerYear TARGET_deathRate incidenceRate \\\n",
"count 3047.000000 3047.000000 3047.000000 3047.000000 \n",
"mean 606.338544 185.965868 178.664063 448.268586 \n",
"std 1416.356223 504.134286 27.751511 54.560733 \n",
"min 6.000000 3.000000 59.700000 201.300000 \n",
"25% 76.000000 28.000000 161.200000 420.300000 \n",
"50% 171.000000 61.000000 178.100000 453.549422 \n",
"75% 518.000000 149.000000 195.200000 480.850000 \n",
"max 38150.000000 14010.000000 362.800000 1206.900000 \n",
"\n",
" medIncome popEst2015 povertyPercent studyPerCap MedianAge \\\n",
"count 3047.000000 3.047000e+03 3047.000000 3047.000000 3047.000000 \n",
"mean 47063.281917 1.026374e+05 16.878175 155.399415 45.272333 \n",
"std 12040.090836 3.290592e+05 6.409087 529.628366 45.304480 \n",
"min 22640.000000 8.270000e+02 3.200000 0.000000 22.300000 \n",
"25% 38882.500000 1.168400e+04 12.150000 0.000000 37.700000 \n",
"50% 45207.000000 2.664300e+04 15.900000 0.000000 41.000000 \n",
"75% 52492.000000 6.867100e+04 20.400000 83.650776 44.000000 \n",
"max 125635.000000 1.017029e+07 47.400000 9762.308998 624.000000 \n",
"\n",
" MedianAgeMale MedianAgeFemale AvgHouseholdSize PercentMarried \\\n",
"count 3047.000000 3047.000000 3047.000000 3047.000000 \n",
"mean 39.570725 42.145323 2.479662 51.773679 \n",
"std 5.226017 5.292849 0.429174 6.896928 \n",
"min 22.400000 22.300000 0.022100 23.100000 \n",
"25% 36.350000 39.100000 2.370000 47.750000 \n",
"50% 39.600000 42.400000 2.500000 52.400000 \n",
"75% 42.500000 45.300000 2.630000 56.400000 \n",
"max 64.700000 65.700000 3.970000 72.500000 \n",
"\n",
" PctNoHS18_24 PctHS18_24 PctBachDeg18_24 PctHS25_Over \\\n",
"count 3047.000000 3047.000000 3047.000000 3047.000000 \n",
"mean 18.224450 35.002068 6.158287 34.804660 \n",
"std 8.093064 9.069722 4.529059 7.034924 \n",
"min 0.000000 0.000000 0.000000 7.500000 \n",
"25% 12.800000 29.200000 3.100000 30.400000 \n",
"50% 17.100000 34.700000 5.400000 35.300000 \n",
"75% 22.700000 40.700000 8.200000 39.650000 \n",
"max 64.100000 72.500000 51.800000 54.800000 \n",
"\n",
" PctBachDeg25_Over PctEmployed16_Over PctUnemployed16_Over \\\n",
"count 3047.000000 2895.000000 3047.000000 \n",
"mean 13.282015 54.152642 7.852412 \n",
"std 5.394756 8.315064 3.452371 \n",
"min 2.500000 17.600000 0.400000 \n",
"25% 9.400000 48.600000 5.500000 \n",
"50% 12.300000 54.500000 7.600000 \n",
"75% 16.100000 60.300000 9.700000 \n",
"max 42.200000 80.100000 29.400000 \n",
"\n",
" PctPrivateCoverage PctEmpPrivCoverage PctPublicCoverage \\\n",
"count 3047.000000 3047.000000 3047.000000 \n",
"mean 64.354939 41.196324 36.252642 \n",
"std 10.647057 9.447687 7.841741 \n",
"min 22.300000 13.500000 11.200000 \n",
"25% 57.200000 34.500000 30.900000 \n",
"50% 65.100000 41.100000 36.300000 \n",
"75% 72.100000 47.700000 41.550000 \n",
"max 92.300000 70.700000 65.100000 \n",
"\n",
" PctPublicCoverageAlone PctWhite PctBlack PctAsian \\\n",
"count 3047.000000 3047.000000 3047.000000 3047.000000 \n",
"mean 19.240072 83.645286 9.107978 1.253965 \n",
"std 6.113041 16.380025 14.534538 2.610276 \n",
"min 2.600000 10.199155 0.000000 0.000000 \n",
"25% 14.850000 77.296180 0.620675 0.254199 \n",
"50% 18.800000 90.059774 2.247576 0.549812 \n",
"75% 23.100000 95.451693 10.509732 1.221037 \n",
"max 46.600000 100.000000 85.947799 42.619425 \n",
"\n",
" PctOtherRace PctMarriedHouseholds BirthRate Rep_PctSomeCol18_24 \\\n",
"count 3047.000000 3047.000000 3047.000000 3047.000000 \n",
"mean 1.983523 51.243872 5.640306 -738.920414 \n",
"std 3.517710 6.572814 1.985816 450.480706 \n",
"min 0.000000 22.992490 0.000000 -999.000000 \n",
"25% 0.295172 47.763063 4.521419 -999.000000 \n",
"50% 0.826185 51.669941 5.381478 -999.000000 \n",
"75% 2.177960 55.395132 6.493677 -495.950000 \n",
"max 41.930251 78.075397 21.326165 79.000000 \n",
"\n",
" Rep_PctPrivateCoverageAlone avgbinnedInc \n",
"count 3047.000000 3047.000000 \n",
"mean -160.899475 48878.118280 \n",
"std 419.044163 16889.719362 \n",
"min -999.000000 28429.050000 \n",
"25% 33.200000 38888.250000 \n",
"50% 45.100000 46611.300000 \n",
"75% 53.800000 52796.000000 \n",
"max 78.900000 93564.750000 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.describe(include = 'all')"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.02644,
"end_time": "2020-11-19T11:43:07.730166",
"exception": false,
"start_time": "2020-11-19T11:43:07.703726",
"status": "completed"
},
"tags": []
},
"source": [
"#### Correlation between numerical attributes"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:07.789387Z",
"iopub.status.busy": "2020-11-19T11:43:07.788580Z",
"iopub.status.idle": "2020-11-19T11:43:12.139997Z",
"shell.execute_reply": "2020-11-19T11:43:12.140758Z"
},
"papermill": {
"duration": 4.38455,
"end_time": "2020-11-19T11:43:12.140940",
"exception": false,
"start_time": "2020-11-19T11:43:07.756390",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f1d096906d0>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 2160x1440 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (30, 20))\n",
"sns.heatmap(data.corr(), annot = True)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:12.260534Z",
"iopub.status.busy": "2020-11-19T11:43:12.259784Z",
"iopub.status.idle": "2020-11-19T11:43:12.264861Z",
"shell.execute_reply": "2020-11-19T11:43:12.265421Z"
},
"papermill": {
"duration": 0.08504,
"end_time": "2020-11-19T11:43:12.265601",
"exception": false,
"start_time": "2020-11-19T11:43:12.180561",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>avgAnnCount</th>\n",
" <th>avgDeathsPerYear</th>\n",
" <th>TARGET_deathRate</th>\n",
" <th>incidenceRate</th>\n",
" <th>medIncome</th>\n",
" <th>popEst2015</th>\n",
" <th>povertyPercent</th>\n",
" <th>studyPerCap</th>\n",
" <th>MedianAge</th>\n",
" <th>MedianAgeMale</th>\n",
" <th>MedianAgeFemale</th>\n",
" <th>AvgHouseholdSize</th>\n",
" <th>PercentMarried</th>\n",
" <th>PctNoHS18_24</th>\n",
" <th>PctHS18_24</th>\n",
" <th>PctBachDeg18_24</th>\n",
" <th>PctHS25_Over</th>\n",
" <th>PctBachDeg25_Over</th>\n",
" <th>PctEmployed16_Over</th>\n",
" <th>PctUnemployed16_Over</th>\n",
" <th>PctPrivateCoverage</th>\n",
" <th>PctEmpPrivCoverage</th>\n",
" <th>PctPublicCoverage</th>\n",
" <th>PctPublicCoverageAlone</th>\n",
" <th>PctWhite</th>\n",
" <th>PctBlack</th>\n",
" <th>PctAsian</th>\n",
" <th>PctOtherRace</th>\n",
" <th>PctMarriedHouseholds</th>\n",
" <th>BirthRate</th>\n",
" <th>Rep_PctSomeCol18_24</th>\n",
" <th>Rep_PctPrivateCoverageAlone</th>\n",
" <th>avgbinnedInc</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Geography</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Kitsap County, Washington</th>\n",
" <td>1397.0</td>\n",
" <td>469</td>\n",
" <td>164.9</td>\n",
" <td>489.8</td>\n",
" <td>61898</td>\n",
" <td>260131</td>\n",
" <td>11.2</td>\n",
" <td>499.748204</td>\n",
" <td>39.3</td>\n",
" <td>36.9</td>\n",
" <td>41.7</td>\n",
" <td>2.54</td>\n",
" <td>52.5</td>\n",
" <td>11.5</td>\n",
" <td>39.5</td>\n",
" <td>6.9</td>\n",
" <td>23.2</td>\n",
" <td>19.6</td>\n",
" <td>51.9</td>\n",
" <td>8.0</td>\n",
" <td>75.1</td>\n",
" <td>41.6</td>\n",
" <td>32.9</td>\n",
" <td>14.0</td>\n",
" <td>81.780529</td>\n",
" <td>2.594728</td>\n",
" <td>4.821857</td>\n",
" <td>1.843479</td>\n",
" <td>52.856076</td>\n",
" <td>6.118831</td>\n",
" <td>42.1</td>\n",
" <td>-999.0</td>\n",
" <td>93564.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kittitas County, Washington</th>\n",
" <td>173.0</td>\n",
" <td>70</td>\n",
" <td>161.3</td>\n",
" <td>411.6</td>\n",
" <td>48127</td>\n",
" <td>43269</td>\n",
" <td>18.6</td>\n",
" <td>23.111234</td>\n",
" <td>33.0</td>\n",
" <td>32.2</td>\n",
" <td>33.7</td>\n",
" <td>2.34</td>\n",
" <td>44.5</td>\n",
" <td>6.1</td>\n",
" <td>22.4</td>\n",
" <td>7.5</td>\n",
" <td>26.0</td>\n",
" <td>22.7</td>\n",
" <td>55.9</td>\n",
" <td>7.8</td>\n",
" <td>70.2</td>\n",
" <td>43.6</td>\n",
" <td>31.1</td>\n",
" <td>15.3</td>\n",
" <td>89.228509</td>\n",
" <td>0.969102</td>\n",
" <td>2.246233</td>\n",
" <td>3.741352</td>\n",
" <td>45.372500</td>\n",
" <td>4.333096</td>\n",
" <td>64.0</td>\n",
" <td>53.8</td>\n",
" <td>49534.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Klickitat County, Washington</th>\n",
" <td>102.0</td>\n",
" <td>50</td>\n",
" <td>174.7</td>\n",
" <td>349.7</td>\n",
" <td>49348</td>\n",
" <td>21026</td>\n",
" <td>14.6</td>\n",
" <td>47.560164</td>\n",
" <td>45.0</td>\n",
" <td>44.0</td>\n",
" <td>45.8</td>\n",
" <td>2.62</td>\n",
" <td>54.2</td>\n",
" <td>24.0</td>\n",
" <td>36.6</td>\n",
" <td>9.5</td>\n",
" <td>29.0</td>\n",
" <td>16.0</td>\n",
" <td>45.9</td>\n",
" <td>7.0</td>\n",
" <td>63.7</td>\n",
" <td>34.9</td>\n",
" <td>42.1</td>\n",
" <td>21.1</td>\n",
" <td>90.922190</td>\n",
" <td>0.739673</td>\n",
" <td>0.465898</td>\n",
" <td>2.747358</td>\n",
" <td>54.444868</td>\n",
" <td>3.729488</td>\n",
" <td>-999.0</td>\n",
" <td>43.5</td>\n",
" <td>49534.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lewis County, Washington</th>\n",
" <td>427.0</td>\n",
" <td>202</td>\n",
" <td>194.8</td>\n",
" <td>430.4</td>\n",
" <td>44243</td>\n",
" <td>75882</td>\n",
" <td>17.1</td>\n",
" <td>342.637253</td>\n",
" <td>42.8</td>\n",
" <td>42.2</td>\n",
" <td>43.4</td>\n",
" <td>2.52</td>\n",
" <td>52.7</td>\n",
" <td>20.2</td>\n",
" <td>41.2</td>\n",
" <td>2.5</td>\n",
" <td>31.6</td>\n",
" <td>9.3</td>\n",
" <td>48.3</td>\n",
" <td>12.1</td>\n",
" <td>58.4</td>\n",
" <td>35.0</td>\n",
" <td>45.3</td>\n",
" <td>25.0</td>\n",
" <td>91.744686</td>\n",
" <td>0.782626</td>\n",
" <td>1.161359</td>\n",
" <td>1.362643</td>\n",
" <td>51.021514</td>\n",
" <td>4.603841</td>\n",
" <td>36.1</td>\n",
" <td>40.3</td>\n",
" <td>43962.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lincoln County, Washington</th>\n",
" <td>57.0</td>\n",
" <td>26</td>\n",
" <td>144.4</td>\n",
" <td>350.1</td>\n",
" <td>49955</td>\n",
" <td>10321</td>\n",
" <td>12.5</td>\n",
" <td>0.000000</td>\n",
" <td>48.3</td>\n",
" <td>47.8</td>\n",
" <td>48.9</td>\n",
" <td>2.34</td>\n",
" <td>57.8</td>\n",
" <td>14.9</td>\n",
" <td>43.0</td>\n",
" <td>2.0</td>\n",
" <td>33.4</td>\n",
" <td>15.0</td>\n",
" <td>48.2</td>\n",
" <td>4.8</td>\n",
" <td>61.6</td>\n",
" <td>35.1</td>\n",
" <td>44.0</td>\n",
" <td>22.7</td>\n",
" <td>94.104024</td>\n",
" <td>0.270192</td>\n",
" <td>0.665830</td>\n",
" <td>0.492135</td>\n",
" <td>54.027460</td>\n",
" <td>6.796657</td>\n",
" <td>40.0</td>\n",
" <td>43.9</td>\n",
" <td>49534.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" avgAnnCount avgDeathsPerYear TARGET_deathRate \\\n",
"Geography \n",
"Kitsap County, Washington 1397.0 469 164.9 \n",
"Kittitas County, Washington 173.0 70 161.3 \n",
"Klickitat County, Washington 102.0 50 174.7 \n",
"Lewis County, Washington 427.0 202 194.8 \n",
"Lincoln County, Washington 57.0 26 144.4 \n",
"\n",
" incidenceRate medIncome popEst2015 \\\n",
"Geography \n",
"Kitsap County, Washington 489.8 61898 260131 \n",
"Kittitas County, Washington 411.6 48127 43269 \n",
"Klickitat County, Washington 349.7 49348 21026 \n",
"Lewis County, Washington 430.4 44243 75882 \n",
"Lincoln County, Washington 350.1 49955 10321 \n",
"\n",
" povertyPercent studyPerCap MedianAge \\\n",
"Geography \n",
"Kitsap County, Washington 11.2 499.748204 39.3 \n",
"Kittitas County, Washington 18.6 23.111234 33.0 \n",
"Klickitat County, Washington 14.6 47.560164 45.0 \n",
"Lewis County, Washington 17.1 342.637253 42.8 \n",
"Lincoln County, Washington 12.5 0.000000 48.3 \n",
"\n",
" MedianAgeMale MedianAgeFemale \\\n",
"Geography \n",
"Kitsap County, Washington 36.9 41.7 \n",
"Kittitas County, Washington 32.2 33.7 \n",
"Klickitat County, Washington 44.0 45.8 \n",
"Lewis County, Washington 42.2 43.4 \n",
"Lincoln County, Washington 47.8 48.9 \n",
"\n",
" AvgHouseholdSize PercentMarried PctNoHS18_24 \\\n",
"Geography \n",
"Kitsap County, Washington 2.54 52.5 11.5 \n",
"Kittitas County, Washington 2.34 44.5 6.1 \n",
"Klickitat County, Washington 2.62 54.2 24.0 \n",
"Lewis County, Washington 2.52 52.7 20.2 \n",
"Lincoln County, Washington 2.34 57.8 14.9 \n",
"\n",
" PctHS18_24 PctBachDeg18_24 PctHS25_Over \\\n",
"Geography \n",
"Kitsap County, Washington 39.5 6.9 23.2 \n",
"Kittitas County, Washington 22.4 7.5 26.0 \n",
"Klickitat County, Washington 36.6 9.5 29.0 \n",
"Lewis County, Washington 41.2 2.5 31.6 \n",
"Lincoln County, Washington 43.0 2.0 33.4 \n",
"\n",
" PctBachDeg25_Over PctEmployed16_Over \\\n",
"Geography \n",
"Kitsap County, Washington 19.6 51.9 \n",
"Kittitas County, Washington 22.7 55.9 \n",
"Klickitat County, Washington 16.0 45.9 \n",
"Lewis County, Washington 9.3 48.3 \n",
"Lincoln County, Washington 15.0 48.2 \n",
"\n",
" PctUnemployed16_Over PctPrivateCoverage \\\n",
"Geography \n",
"Kitsap County, Washington 8.0 75.1 \n",
"Kittitas County, Washington 7.8 70.2 \n",
"Klickitat County, Washington 7.0 63.7 \n",
"Lewis County, Washington 12.1 58.4 \n",
"Lincoln County, Washington 4.8 61.6 \n",
"\n",
" PctEmpPrivCoverage PctPublicCoverage \\\n",
"Geography \n",
"Kitsap County, Washington 41.6 32.9 \n",
"Kittitas County, Washington 43.6 31.1 \n",
"Klickitat County, Washington 34.9 42.1 \n",
"Lewis County, Washington 35.0 45.3 \n",
"Lincoln County, Washington 35.1 44.0 \n",
"\n",
" PctPublicCoverageAlone PctWhite PctBlack \\\n",
"Geography \n",
"Kitsap County, Washington 14.0 81.780529 2.594728 \n",
"Kittitas County, Washington 15.3 89.228509 0.969102 \n",
"Klickitat County, Washington 21.1 90.922190 0.739673 \n",
"Lewis County, Washington 25.0 91.744686 0.782626 \n",
"Lincoln County, Washington 22.7 94.104024 0.270192 \n",
"\n",
" PctAsian PctOtherRace PctMarriedHouseholds \\\n",
"Geography \n",
"Kitsap County, Washington 4.821857 1.843479 52.856076 \n",
"Kittitas County, Washington 2.246233 3.741352 45.372500 \n",
"Klickitat County, Washington 0.465898 2.747358 54.444868 \n",
"Lewis County, Washington 1.161359 1.362643 51.021514 \n",
"Lincoln County, Washington 0.665830 0.492135 54.027460 \n",
"\n",
" BirthRate Rep_PctSomeCol18_24 \\\n",
"Geography \n",
"Kitsap County, Washington 6.118831 42.1 \n",
"Kittitas County, Washington 4.333096 64.0 \n",
"Klickitat County, Washington 3.729488 -999.0 \n",
"Lewis County, Washington 4.603841 36.1 \n",
"Lincoln County, Washington 6.796657 40.0 \n",
"\n",
" Rep_PctPrivateCoverageAlone avgbinnedInc \n",
"Geography \n",
"Kitsap County, Washington -999.0 93564.75 \n",
"Kittitas County, Washington 53.8 49534.00 \n",
"Klickitat County, Washington 43.5 49534.00 \n",
"Lewis County, Washington 40.3 43962.70 \n",
"Lincoln County, Washington 43.9 49534.00 "
]
},
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| 0047/235/47235214.ipynb | s3://data-agents/kaggle-outputs/sharded/010_00047.jsonl.gz |
{"cells":[{"metadata":{},"cell_type":"markdown","source":"## Introduction:\n\nCredit fraud is the manipulation of unauthorized card that are stolen or theft from the cardholder. It can be the card itself or the personal details from the owner. In this kernel multiple solutions is demonstrated.\n\nThe datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.\n\nThe dataset has pre-processed with PCA transformation except Time, Amount and Class feature.\n\nDue to confidentiality issues, there are not provided the original features and more background information about the data.\n\n* Features V1, V2, … V28 are the principal components obtained with PCA.\n* Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount.\n* Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise."},{"metadata":{},"cell_type":"markdown","source":"In This kernel I will be showing the steps to detect frauds."},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd \nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport plotly.offline as py\npy.init_notebook_mode(connected=True)\nimport plotly.graph_objs as go\nimport plotly.tools as tls\nimport plotly.figure_factory as ff\n\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom keras.models import Sequential\nfrom keras.layers import Conv1D, MaxPooling1D, Dense, Flatten, Dropout, BatchNormalization\n\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.linear_model import RidgeClassifier\nfrom catboost import Pool, CatBoostClassifier, cv\n\nfrom sklearn.model_selection import GridSearchCV, RandomizedSearchCV\nfrom sklearn.preprocessing import RobustScaler\nfrom sklearn.metrics import precision_score,make_scorer,roc_auc_score, recall_score, f1_score, roc_auc_score, accuracy_score, classification_report, confusion_matrix\n\nfrom sklearn.model_selection import KFold, StratifiedKFold, StratifiedShuffleSplit\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input/creditcardfraud/creditcard.csv'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n","execution_count":1,"outputs":[{"output_type":"display_data","data":{"text/html":" <script type=\"text/javascript\">\n window.PlotlyConfig = {MathJaxConfig: 'local'};\n if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n if (typeof require !== 'undefined') {\n require.undef(\"plotly\");\n requirejs.config({\n paths: {\n 'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n }\n });\n require(['plotly'], function(Plotly) {\n window._Plotly = Plotly;\n });\n }\n </script>\n "},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Loading the Data"},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"df = pd.read_csv(\"../input/creditcardfraud/creditcard.csv\")\ndf.head()","execution_count":2,"outputs":[{"output_type":"execute_result","execution_count":2,"data":{"text/plain":" Time V1 V2 V3 V4 V5 V6 V7 \\\n0 0.0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 \n1 0.0 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 \n2 1.0 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 \n3 1.0 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 \n4 2.0 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 \n\n V8 V9 ... V21 V22 V23 V24 V25 \\\n0 0.098698 0.363787 ... -0.018307 0.277838 -0.110474 0.066928 0.128539 \n1 0.085102 -0.255425 ... -0.225775 -0.638672 0.101288 -0.339846 0.167170 \n2 0.247676 -1.514654 ... 0.247998 0.771679 0.909412 -0.689281 -0.327642 \n3 0.377436 -1.387024 ... -0.108300 0.005274 -0.190321 -1.175575 0.647376 \n4 -0.270533 0.817739 ... -0.009431 0.798278 -0.137458 0.141267 -0.206010 \n\n V26 V27 V28 Amount Class \n0 -0.189115 0.133558 -0.021053 149.62 0 \n1 0.125895 -0.008983 0.014724 2.69 0 \n2 -0.139097 -0.055353 -0.059752 378.66 0 \n3 -0.221929 0.062723 0.061458 123.50 0 \n4 0.502292 0.219422 0.215153 69.99 0 \n\n[5 rows x 31 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Time</th>\n <th>V1</th>\n <th>V2</th>\n <th>V3</th>\n <th>V4</th>\n <th>V5</th>\n <th>V6</th>\n <th>V7</th>\n <th>V8</th>\n <th>V9</th>\n <th>...</th>\n <th>V21</th>\n <th>V22</th>\n <th>V23</th>\n <th>V24</th>\n <th>V25</th>\n <th>V26</th>\n <th>V27</th>\n <th>V28</th>\n <th>Amount</th>\n <th>Class</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0.0</td>\n <td>-1.359807</td>\n <td>-0.072781</td>\n <td>2.536347</td>\n <td>1.378155</td>\n <td>-0.338321</td>\n <td>0.462388</td>\n <td>0.239599</td>\n <td>0.098698</td>\n <td>0.363787</td>\n <td>...</td>\n <td>-0.018307</td>\n <td>0.277838</td>\n <td>-0.110474</td>\n <td>0.066928</td>\n <td>0.128539</td>\n <td>-0.189115</td>\n <td>0.133558</td>\n <td>-0.021053</td>\n <td>149.62</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>0.0</td>\n <td>1.191857</td>\n <td>0.266151</td>\n <td>0.166480</td>\n <td>0.448154</td>\n <td>0.060018</td>\n <td>-0.082361</td>\n <td>-0.078803</td>\n <td>0.085102</td>\n <td>-0.255425</td>\n <td>...</td>\n <td>-0.225775</td>\n <td>-0.638672</td>\n <td>0.101288</td>\n <td>-0.339846</td>\n <td>0.167170</td>\n <td>0.125895</td>\n <td>-0.008983</td>\n <td>0.014724</td>\n <td>2.69</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1.0</td>\n <td>-1.358354</td>\n <td>-1.340163</td>\n <td>1.773209</td>\n <td>0.379780</td>\n <td>-0.503198</td>\n <td>1.800499</td>\n <td>0.791461</td>\n <td>0.247676</td>\n <td>-1.514654</td>\n <td>...</td>\n <td>0.247998</td>\n <td>0.771679</td>\n <td>0.909412</td>\n <td>-0.689281</td>\n <td>-0.327642</td>\n <td>-0.139097</td>\n <td>-0.055353</td>\n <td>-0.059752</td>\n <td>378.66</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1.0</td>\n <td>-0.966272</td>\n <td>-0.185226</td>\n <td>1.792993</td>\n <td>-0.863291</td>\n <td>-0.010309</td>\n <td>1.247203</td>\n <td>0.237609</td>\n <td>0.377436</td>\n <td>-1.387024</td>\n <td>...</td>\n <td>-0.108300</td>\n <td>0.005274</td>\n <td>-0.190321</td>\n <td>-1.175575</td>\n <td>0.647376</td>\n <td>-0.221929</td>\n <td>0.062723</td>\n <td>0.061458</td>\n <td>123.50</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2.0</td>\n <td>-1.158233</td>\n <td>0.877737</td>\n <td>1.548718</td>\n <td>0.403034</td>\n <td>-0.407193</td>\n <td>0.095921</td>\n <td>0.592941</td>\n <td>-0.270533</td>\n <td>0.817739</td>\n <td>...</td>\n <td>-0.009431</td>\n <td>0.798278</td>\n <td>-0.137458</td>\n <td>0.141267</td>\n <td>-0.206010</td>\n <td>0.502292</td>\n <td>0.219422</td>\n <td>0.215153</td>\n <td>69.99</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 31 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Exploratory Data Analysis"},{"metadata":{"trusted":true},"cell_type":"code","source":"pd.set_option(\"display.float\", \"{:.2f}\".format)\ndf.describe()","execution_count":3,"outputs":[{"output_type":"execute_result","execution_count":3,"data":{"text/plain":" Time V1 V2 V3 V4 V5 V6 \\\ncount 284807.00 284807.00 284807.00 284807.00 284807.00 284807.00 284807.00 \nmean 94813.86 0.00 0.00 -0.00 0.00 -0.00 0.00 \nstd 47488.15 1.96 1.65 1.52 1.42 1.38 1.33 \nmin 0.00 -56.41 -72.72 -48.33 -5.68 -113.74 -26.16 \n25% 54201.50 -0.92 -0.60 -0.89 -0.85 -0.69 -0.77 \n50% 84692.00 0.02 0.07 0.18 -0.02 -0.05 -0.27 \n75% 139320.50 1.32 0.80 1.03 0.74 0.61 0.40 \nmax 172792.00 2.45 22.06 9.38 16.88 34.80 73.30 \n\n V7 V8 V9 ... V21 V22 V23 \\\ncount 284807.00 284807.00 284807.00 ... 284807.00 284807.00 284807.00 \nmean -0.00 -0.00 -0.00 ... 0.00 0.00 0.00 \nstd 1.24 1.19 1.10 ... 0.73 0.73 0.62 \nmin -43.56 -73.22 -13.43 ... -34.83 -10.93 -44.81 \n25% -0.55 -0.21 -0.64 ... -0.23 -0.54 -0.16 \n50% 0.04 0.02 -0.05 ... -0.03 0.01 -0.01 \n75% 0.57 0.33 0.60 ... 0.19 0.53 0.15 \nmax 120.59 20.01 15.59 ... 27.20 10.50 22.53 \n\n V24 V25 V26 V27 V28 Amount Class \ncount 284807.00 284807.00 284807.00 284807.00 284807.00 284807.00 284807.00 \nmean 0.00 0.00 0.00 -0.00 -0.00 88.35 0.00 \nstd 0.61 0.52 0.48 0.40 0.33 250.12 0.04 \nmin -2.84 -10.30 -2.60 -22.57 -15.43 0.00 0.00 \n25% -0.35 -0.32 -0.33 -0.07 -0.05 5.60 0.00 \n50% 0.04 0.02 -0.05 0.00 0.01 22.00 0.00 \n75% 0.44 0.35 0.24 0.09 0.08 77.16 0.00 \nmax 4.58 7.52 3.52 31.61 33.85 25691.16 1.00 \n\n[8 rows x 31 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Time</th>\n <th>V1</th>\n <th>V2</th>\n <th>V3</th>\n <th>V4</th>\n <th>V5</th>\n <th>V6</th>\n <th>V7</th>\n <th>V8</th>\n <th>V9</th>\n <th>...</th>\n <th>V21</th>\n <th>V22</th>\n <th>V23</th>\n <th>V24</th>\n <th>V25</th>\n <th>V26</th>\n <th>V27</th>\n <th>V28</th>\n <th>Amount</th>\n <th>Class</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>284807.00</td>\n <td>284807.00</td>\n <td>284807.00</td>\n <td>284807.00</td>\n <td>284807.00</td>\n <td>284807.00</td>\n <td>284807.00</td>\n <td>284807.00</td>\n <td>284807.00</td>\n <td>284807.00</td>\n <td>...</td>\n <td>284807.00</td>\n <td>284807.00</td>\n <td>284807.00</td>\n <td>284807.00</td>\n <td>284807.00</td>\n <td>284807.00</td>\n <td>284807.00</td>\n <td>284807.00</td>\n <td>284807.00</td>\n <td>284807.00</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>94813.86</td>\n <td>0.00</td>\n <td>0.00</td>\n <td>-0.00</td>\n <td>0.00</td>\n <td>-0.00</td>\n <td>0.00</td>\n <td>-0.00</td>\n <td>-0.00</td>\n <td>-0.00</td>\n <td>...</td>\n <td>0.00</td>\n <td>0.00</td>\n <td>0.00</td>\n <td>0.00</td>\n <td>0.00</td>\n <td>0.00</td>\n <td>-0.00</td>\n <td>-0.00</td>\n <td>88.35</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>std</th>\n <td>47488.15</td>\n <td>1.96</td>\n <td>1.65</td>\n <td>1.52</td>\n <td>1.42</td>\n <td>1.38</td>\n <td>1.33</td>\n <td>1.24</td>\n <td>1.19</td>\n <td>1.10</td>\n <td>...</td>\n <td>0.73</td>\n <td>0.73</td>\n <td>0.62</td>\n <td>0.61</td>\n <td>0.52</td>\n <td>0.48</td>\n <td>0.40</td>\n <td>0.33</td>\n <td>250.12</td>\n <td>0.04</td>\n </tr>\n <tr>\n <th>min</th>\n <td>0.00</td>\n <td>-56.41</td>\n <td>-72.72</td>\n <td>-48.33</td>\n <td>-5.68</td>\n <td>-113.74</td>\n <td>-26.16</td>\n <td>-43.56</td>\n <td>-73.22</td>\n <td>-13.43</td>\n <td>...</td>\n <td>-34.83</td>\n <td>-10.93</td>\n <td>-44.81</td>\n <td>-2.84</td>\n <td>-10.30</td>\n <td>-2.60</td>\n <td>-22.57</td>\n <td>-15.43</td>\n <td>0.00</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>54201.50</td>\n <td>-0.92</td>\n <td>-0.60</td>\n <td>-0.89</td>\n <td>-0.85</td>\n <td>-0.69</td>\n <td>-0.77</td>\n <td>-0.55</td>\n <td>-0.21</td>\n <td>-0.64</td>\n <td>...</td>\n <td>-0.23</td>\n <td>-0.54</td>\n <td>-0.16</td>\n <td>-0.35</td>\n <td>-0.32</td>\n <td>-0.33</td>\n <td>-0.07</td>\n <td>-0.05</td>\n <td>5.60</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>84692.00</td>\n <td>0.02</td>\n <td>0.07</td>\n <td>0.18</td>\n <td>-0.02</td>\n <td>-0.05</td>\n <td>-0.27</td>\n <td>0.04</td>\n <td>0.02</td>\n <td>-0.05</td>\n <td>...</td>\n <td>-0.03</td>\n <td>0.01</td>\n <td>-0.01</td>\n <td>0.04</td>\n <td>0.02</td>\n <td>-0.05</td>\n <td>0.00</td>\n <td>0.01</td>\n <td>22.00</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>139320.50</td>\n <td>1.32</td>\n <td>0.80</td>\n <td>1.03</td>\n <td>0.74</td>\n <td>0.61</td>\n <td>0.40</td>\n <td>0.57</td>\n <td>0.33</td>\n <td>0.60</td>\n <td>...</td>\n <td>0.19</td>\n <td>0.53</td>\n <td>0.15</td>\n <td>0.44</td>\n <td>0.35</td>\n <td>0.24</td>\n <td>0.09</td>\n <td>0.08</td>\n <td>77.16</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>max</th>\n <td>172792.00</td>\n <td>2.45</td>\n <td>22.06</td>\n <td>9.38</td>\n <td>16.88</td>\n <td>34.80</td>\n <td>73.30</td>\n <td>120.59</td>\n <td>20.01</td>\n <td>15.59</td>\n <td>...</td>\n <td>27.20</td>\n <td>10.50</td>\n <td>22.53</td>\n <td>4.58</td>\n <td>7.52</td>\n <td>3.52</td>\n <td>31.61</td>\n <td>33.85</td>\n <td>25691.16</td>\n <td>1.00</td>\n </tr>\n </tbody>\n</table>\n<p>8 rows × 31 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Check for missing value"},{"metadata":{"trusted":true},"cell_type":"code","source":"miss = df.isnull().sum()\nmiss_percnt = (miss/len(df))*100\npd.concat([miss,miss_percnt], axis=1, keys=['Missing Values','Missing Percentage'])","execution_count":4,"outputs":[{"output_type":"execute_result","execution_count":4,"data":{"text/plain":" Missing Values Missing Percentage\nTime 0 0.00\nV1 0 0.00\nV2 0 0.00\nV3 0 0.00\nV4 0 0.00\nV5 0 0.00\nV6 0 0.00\nV7 0 0.00\nV8 0 0.00\nV9 0 0.00\nV10 0 0.00\nV11 0 0.00\nV12 0 0.00\nV13 0 0.00\nV14 0 0.00\nV15 0 0.00\nV16 0 0.00\nV17 0 0.00\nV18 0 0.00\nV19 0 0.00\nV20 0 0.00\nV21 0 0.00\nV22 0 0.00\nV23 0 0.00\nV24 0 0.00\nV25 0 0.00\nV26 0 0.00\nV27 0 0.00\nV28 0 0.00\nAmount 0 0.00\nClass 0 0.00","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Missing Values</th>\n <th>Missing Percentage</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>Time</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V1</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V2</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V3</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V4</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V5</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V6</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V7</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V8</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V9</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V10</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V11</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V12</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V13</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V14</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V15</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V16</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V17</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V18</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V19</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V20</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V21</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V22</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V23</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V24</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V25</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V26</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V27</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>V28</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>Amount</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n <tr>\n <th>Class</th>\n <td>0</td>\n <td>0.00</td>\n </tr>\n </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('There are 492 frauds and the rest are genuine transactions in the dataset. \\n1 represents fraud and 0 represents Genuine')\ndf['Class'].value_counts()","execution_count":5,"outputs":[{"output_type":"stream","text":"There are 492 frauds and the rest are genuine transactions in the dataset. \n1 represents fraud and 0 represents Genuine\n","name":"stdout"},{"output_type":"execute_result","execution_count":5,"data":{"text/plain":"0 284315\n1 492\nName: Class, dtype: int64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"fraud = df[(df['Class'] != 0)]\nnormal = df[(df['Class'] == 0)]\n\ntrace = go.Pie(labels = ['Normal', 'Fraud'], values = df['Class'].value_counts(), \n textfont=dict(size=15), opacity = 0.8,\n marker=dict(colors=['lightskyblue','gold'], \n line=dict(color='#000000', width=1.5)))\n\n\nlayout = dict(title = 'Distribution of normal and fraud tansactions')\n \nfig = dict(data = [trace], layout=layout)\npy.iplot(fig)","execution_count":6,"outputs":[{"output_type":"display_data","data":{"application/vnd.plotly.v1+json":{"config":{"linkText":"Export to 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the fraud transaction is very very small. So, here is the problem arises because the high disparity of classes gives low precision and recall score. We have to choose our path wisely so that our model can detect the frauds wisely"},{"metadata":{},"cell_type":"markdown","source":"#### Lets check how much frauds occur per hour and per minute"},{"metadata":{"trusted":true},"cell_type":"code","source":"timedelta = pd.to_timedelta(df['Time'], unit='s')\ndf['Time_min'] = (timedelta.dt.components.minutes).astype(int)\ndf['Time_hour'] = (timedelta.dt.components.hours).astype(int)","execution_count":7,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class_0 = df.loc[df['Class'] == 0][\"Time_hour\"]\nclass_1 = df.loc[df['Class'] == 1][\"Time_hour\"]\nhist_data = [class_0, class_1]\ngroup_labels = ['Not Fraud', 'Fraud']\n\nfig = ff.create_distplot(hist_data, group_labels, show_hist=False, show_rug=False)\nfig['layout'].update(title='Credit Card Transactions Time/Hour Density Plot', xaxis=dict(title='Time [Hr]'))\npy.iplot(fig, 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Card Transactions Time/Hour Density Plot\"}, \"xaxis\": {\"anchor\": \"y2\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Time [Min]\"}, \"zeroline\": false}, \"yaxis\": {\"anchor\": \"free\", \"domain\": [0.0, 1], \"position\": 0.0}}, {\"responsive\": true} ).then(function(){\n \nvar gd = document.getElementById('3b9e1c51-7fdd-430d-ba72-fcf10f122688');\nvar x = new MutationObserver(function (mutations, observer) {{\n var display = window.getComputedStyle(gd).display;\n if (!display || display === 'none') {{\n console.log([gd, 'removed!']);\n Plotly.purge(gd);\n observer.disconnect();\n }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n x.observe(outputEl, {childList: true});\n}}\n\n }) }; }); </script> </div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Distribution of Time and Amount"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig, ax = plt.subplots(1, 2, figsize=(18,4))\n\namount_val = df['Amount'].values\ntime_val = df['Time'].values\n\nsns.distplot(time_val, ax=ax[0], bins=200, color='darkslategray')\nax[0].set_title('Distribution of Transaction Time', fontsize=14)\nax[0].set_xlim([min(time_val), max(time_val)])\n\nsns.distplot(amount_val, ax=ax[1], bins=85, color='darkred')\nax[1].set_title('Distribution of Transaction Amount', fontsize=14)\nax[1].set_xlim([min(amount_val - 1000), max(amount_val)])\n\nplt.show()","execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1296x288 with 2 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se1PnXNFzrnJwEJ801vjX/4oMB6Yi2+669jwPUMRERFRUUFERETCZTpQ6pwrc85dBV4G5rdaZz7wW+ezHhhkZukdbeucuxiwfX/ABezrZedcvXOuHCj170dERETCREUFERERCZdM4GjA/Sr/Y8Gs0+G2ZvaUmR0Fvoi/pUKQx8PMHjezzWa2+dSpU9f1hEREROSTVFQQERGRcLE2HnNBrtPhts65v3bOZQMvAk9ex/Fwzj3rnCt2zhWnpqa2GVxERESCo6KCiIiIhEsVkB1wPws4FuQ6wWwL8BLwues4noiIiISQigoiIiISLpuAQjPLN7MEfIMovt1qnbeBL/tngZgJXHDOVXe0rZkVBmz/ILA/YF+PmlmimeXjG/xxY7ienIiIiECc1wFERESkZ3LONZrZk0AJEAs855zbY2ZP+Jc/AywC7sc3qGIN8NWOtvXv+idmNhpoBo4ALfvbY2avAnuBRuDbzrmm7nm2IiIivZOKCiIiIhI2zrlF+AoHgY89E/B/B3w72G39j3+ujdVblj0FPNXVvCIiInJ91P1BRERERERERLpERQURERERERER6RIVFURERERERESkS1RUEBEREREREZEuUVFBRERERERERLpERQURERERERER6RIVFURERERERESkS1RUEBEREREREZEuUVFBRERERERERLpERQURERERERER6RIVFURERERERESkS1RUEBEREREREZEuUVFBRERERERERLokzusAItFsQUnJR/9/+L77PEwiIiIiIiLS/TptqWBmz5nZSTPb3c5yM7NfmFmpme00symhjykiIiIiIiIikSaY7g/PA3M7WD4PKPTfHgd+deOxRERERERERCTSddr9wTm30szyOlhlPvBb55wD1pvZIDNLd85VhyijiPgFdrcQERERERHxWigGaswEjgbcr/I/dg0ze9zMNpvZ5lOnToXg0CIiIiIiIiLilVAUFayNx1xbKzrnnnXOFTvnilNTU0NwaBERERERERHxSiiKClVAdsD9LOBYCPYrIiIiIiIiIhEsFEWFt4Ev+2eBmAlc0HgKIiIiIiIiIj1fpwM1mtnvgduBoWZWBfwdEA/gnHsGWATcD5QCNcBXwxVWRERERERERCJHMLM/PNbJcgd8O2SJRERERERERCQqhKL7g4iEwYKSEk0hKSIiIiIiEU1FBRERERERERHpEhUVREREJGzMbK6ZHTCzUjP7QRvLzcx+4V++08ymdLatmf3UzPb713/DzAb5H88zs1oz2+6/PdM9z1JERKT3UlFBREREwsLMYoGngXnAOOAxMxvXarV5QKH/9jjwqyC2XQpMcM4VAQeBHwbs77BzbrL/9kR4npmIiIi06HSgRhHxVkfjKrQse/i++7orjojI9ZgOlDrnygDM7GVgPrA3YJ35wG/9Az+vN7NBZpYO5LW3rXNuScD264GHw/5MREREpE0qKohEGA3OKCI9SCZwNOB+FTAjiHUyg9wW4GvAKwH3881sG3AR+Bvn3KrWG5jZ4/haRZCTkxPUExEREZG2qfuDiIiIhIu18ZgLcp1OtzWzvwYagRf9D1UDOc65m4DvAS+Z2YBrduLcs865YudccWpqaidPQURERDqilgoiIiISLlVAdsD9LOBYkOskdLStmf0Z8ABwl7/rBM65eqDe//8tZnYYGAVsDsWTERERkWuppYKIiIiEyyag0MzyzSwBeBR4u9U6bwNf9s8CMRO44Jyr7mhbM5sLfB940DlX07IjM0v1D/CImRXgG/yxLLxPUUREpHdTSwUREREJC+dco5k9CZQAscBzzrk9ZvaEf/kzwCLgfqAUqAG+2tG2/l3/EkgElpoZwHr/TA9zgH8ws0agCXjCOXe2e56tiIhI76SigoiIRLXAwU01E0rkcc4twlc4CHzsmYD/O+DbwW7rf3xkO+u/Brx2I3lFRETk+qj7g4iIiIiIiIh0iYoKIiIiIiIiItIl6v4gEmGuNjRQdfw4x06eZPjQoYzIycHfZ1ikR1L3BREREZHopaKCSAS4dOUK//3666zfvp19ZWX4Z0cDID01ldnFxRTm5qq4ICIiIiIiEUVFBREPLSgp4VBFBYtWruRKbS1Txo3jlilTyE5PJz01lQPl5azZsoVXFy9m+NChzJ09m6zhw72OLSIiIiIiAqioIOKZcxcv8sayZew5dIi0IUN4ZO5cMtLSPrHOTWPHUjRqFLsPHWLFpk28ungxj3/+8yT16+dRahERERERkY9poEYRD6zfsYPPfec77Dt8mDnFxXz94YevKSi0iI2NZdKYMTz2qU9R39DAO8uXf6J7hIiIiIiIiFdUVBDpRs3Nzfz6D3/gL370IwYPHMg3Hn6YOdOmERsb2+m2qUOGcOeMGRw6coRt+/Z1Q1qRrltQUvKJARhFREREpGdSUUGkm1y8fJm//PGPefrFF5k7eza/+5d/IS0l5br2Mb2oiLzMTJauWcPZCxfClFTCTR+4RURERKSn0JgK0ilN93bjdh86xA9+9jOOnznDDx5/nM/Pm9elmRzMjAfvvJNnX3mFt5Yt488++1liYlQblMin3yMiIiIiPZOKCiJh1NTUxHOvv85/vfwyqUOG8NxTT1E0evQNfUs9ICmJeXPm8MayZazdto1bp04NYWKRznV0/qpgICIiItK7qKgg16Xlw4TXHxwiJUdHjp08yd/8+7+zde9e5s6ezf/3zW8yICkpJPseX1jI3sOHWbd9O9OLikKyT5Fwc851qYVOe9SFRERERMR7KiqIhFhTUxOvLVnCL154AeccT333u9x/222YWUg/BM2YNIkD5eXsPnQoZPuUyBMNBTTwFQzOXbzIPzz9NMdPn6b61CnOXbjAr37/e+qvXqWmrg7nHEn9+vH60qWkDRnCsKFDGZOfz8kzZxg6eDAxMTFR83xFRERExEdFhR6oqxflXdlO/aQ/acuePfzzr3/NwYoKpk2cyI+efJLMYcPCcqzs4cMZlpLClt27Q/4NsEiwTp87x+5Dh9h96BDnL14EICYmhrQhQ8gcPpwx+fkkJiRQ8eGHAFypreXSlSvsLS1l8+7dvLJoEQDxcXGkp6aSn51NYU6OzmkRERGRKKGigrRLTYuDd/7iRX7wr//Ku6tWkZ6ayk//6q+4e9assH4oMjOmTpjAohUr2L5vHzeNGxe2Y0l4hOpb+XAW99r6PdDY2MiOAwfYtncvx0+fxszIy8xk1uTJZKSlkTZkyEfTpLbkaWs/D91zD0erq3nu9dc5dvIkVcePs2LjRlZs3Mhb77/PrVOncs/NNzO9qIi4IKZdFREREZHup6KCRJVIK3ScvXCBNVu3suvgQeLj4vjm5z/PVx56iL6Jid1y/AmFhby3bh2vLF6sooKEXf3Vq2zdu5cNO3ZwuaaG4UOHcs/NNzNu5EiS+/e/7v29vnQpABNHjWLiqFEAXK6p4XBlJaVHjrBoxQreWLqUlEGDmDt7NvffdhvjRoxQCwYRERGRCBJUUcHM5gI/B2KB3zjnftJq+UDgd0COf58/c879d4izikSM8qoqnnvtNRYuX05MTAxTx4/n/3z3uwxLSenWHAnx8UwaM4Yla9YwOj+f5P791Q0lCgXT0sDLrkZ19fVs3LmTjbt2UVdfT35WFp+5+25yMzI6/YB/vYXApH79mDRmDJPGjKGxsZHUIUNYtGIFry5ezIt//COFubk8fN99fOr222/gGd0YjfsgIiIi8rFOiwpmFgs8DdwDVAGbzOxt59zegNW+Dex1zn3azFKBA2b2onPualhSyw2L5IviSM3mnGPTrl288PbbrNq8mT4JCUyfOJGZkyeT3L8/qzZvBro/d/H48WzcuZNt+/Yxp7i4W48t3uiu90hNbS2rt2xh/Y4d1NXXMyovj1umTAnbOCGtxcXFcdesWdw1axYXL19myZo1vFZSwo+ffZaf//a3jCkooHjCBNK6uZgnIiIiIh8LpqXCdKDUOVcGYGYvA/OBwKKCA5LN95VVEnAWaAxxVokgkdYNIZzq6uspWb2alxYu5EB5OUMGDuQvHnuMR+67j/c3bPA6HkMGDaIgO5tte/dyy003eR1HulG43ocXL1/mtSVL+O2bb3Lu4kUKc3O5bdo0hqemhuV4wRiQlMTD993H5+69l92HDvGHd99l0YoVbN27l7zMTKYXFVGYm+tZPhEREZHeKpiiQiZwNOB+FTCj1Tq/BN4GjgHJwOedc82td2RmjwOPA+Tk5HQlr4RYex9KmpqaOHr8OKVHjnDs5En6JibSv18/+vftS3L//hRkZ3epD3Uka/3tb2V1NQvefZdX332Xuvp6UgcP5kdPPsm8OXNITEjwMuo1iidM4NXFizlYUeF1FAmRtt6bzjlqamu5cPky9VevMjA5mQFJSSEbxLDiww95aeFC3n7/ferq65k1eTJjCgq6rWVCMMzsozEYCnNz2bZvH5t27eLVxYsZPGAADY2NzL/rLvr37et1VBEREZFeIZiiQlsdZl2r+/cB24E7gRHAUjNb5Zy7+ImNnHsWeBaguLi49T4kApw+d44v/dVfUVpZSW1dHWbGsJQULly6xJXaWuqvftyjJWv4cMYWFDCmoICByckR223helxtaOCd5cv54wcfsH7HDuJiYynMy6N4wgRy0tP5zN13ex2xTSNzchiYnMzm3bu9jiIhVltXx5qtWymtrOT8pUs0Nl7bCCy5f38GDxjA8KFD6ZOQwLiRI8nNyPhoBob2OOc4dOQIG3buZPWWLWzYsYP4uDjmzZnDFx54gDEFBZ63Smrr90rLY3379OHmm25iRlERB8rL2bhrF//ym9/wny+9xPy77uKxT32KrOHDQ5pDRERERD4pmKJCFZAdcD8LX4uEQF8FfuKcc0CpmZUDY4CNIUkp3eJAeTlvvfceZkZhbi6FubkU5OR8YiaDhsZGzl24wIGKCvaXlbF07VqWrl1LbkYGN40dy5iCgpDnutGL+Y4GuFtQUkJjUxNHjh1jz6FD7Dt8mIbGRjLS0nji0Ud56N57WblpU5eO153FlZiYGIpGj2bV5s2cOX+elEGDuu3YEh4NjY1s2rWLtVu3Unf1KiNzchiRk8PApCQGJScTHx/PxcuXuXDpEucvXeLshQts27ePjbt2AdAnMZHMYcPISEsjIy2NYSkpNDQ2cqWmhss1NVy4dIlt+/Zx9sIFAPIyM3ni0Ud5ZO5cUgYNYkFJCbsPHfLyJQhabGws40aOZNzIkYzOz+elhQt5ZdEiXlq4kNumTeOLn/40xRMmaNYIERERkTAIpqiwCSg0s3zgQ+BR4Aut1qkE7gJWmdkwYDRQFsqgEhrtNaleuWkTq7ZsISMtjYfvu48BSUltbh8fF0daSgppKSnMnjqVsxcusLe0lO379vHme+/Rd/VqKqurmTdnDhMKC6/rIr67vgl0zvHhiROs37GDVxcvpryqiobGRhLi4xk/ciTf+dKXuGnsWGJiYrolT6iMyc9n1ebNfLBhQ1S3FuntFpSUcLC8nHdXr+bi5cuMzM3lzhkzghqMsLm5mTPnz3Ps5EkGJifz4YkTHDt5ku379nHpyhUA+iQk0L9fP5L69WPm5MnMKCpi5qRJDBs6NNxPrVtMHDWKH3/ve/zlV77Cq4sX81pJCcs3bmRUXh5f+PSnmTd7dsR1XxIRERGJZp0WFZxzjWb2JFCCb0rJ55xze8zsCf/yZ4B/BJ43s134ukt83zl3Ooy5JUTqr17lrffe42BFBUWjR3P/nDnExQU10ygAQwYO5NapU7llyhTKP/yQ7Xv38uq77/LSwoUMS0nhjhkzuHPmTCaNGdPlC3nnHA2NjTQ1NdHU3Eyz/4YZ1adOYYDFxGBmGL5v7Z1zXLx8mfMXL7K/rIwrtbWcOnuWRStWcLCigss1NQAMTE6maPRoRubkkJeZSXx8PFPHj283SyQ3gU5LSWFQcrKKClGu9MgR/lBSQlpKCg/eeSd5mZlBbxsTE0PqkCGkDhlyzTlQW1dHfHw8cbGxPaKrUmfShgzhyS9+kW88/DCLV67kxT/+kR/9x3/w8//5H+bfdRefu/destPTgc5bM4mIiIhI+4L69OicWwQsavXYMwH/PwbcG9poEm7OOV5fsoSyqiruu/XWG2oebGYUZGVRkJXFPTffzIpNm8doSoUAACAASURBVHh//XreWLaMlxctwszITEsjPzub/MxMBg0Y4CsC+G9Xr15l0+7d1NbVUVtfT11dHS+89ZavMHDpEr6eNdf65e9+F3TGhPh40lJSGJ2fz7w5c5gybhxb9uy55jlH6wcuM2N0QQEbd+7kck0NSf36eR1JrtOHJ07w2pIlDEtJ4Uvz54f0G/W+ffqEbF9eCPbDfev378LlywH4w89/zsadO3ll8WJeeOstnn/jDWZNnsyfzJuHc05dI8LIzOYCP8f3xcRvnHM/abXc/MvvB2qArzjntna0rZn9FPg0cBU4DHzVOXfev+yHwNeBJuD/cc6pMiQiIhJGwX8lLT3Opl27OHz0KHNnz6Z4woSQ7XdgcjIP3nknD955J7V1dazfsYP9ZWVUfPgh5VVVbNixg6sNDddslxAfT98+feibmEjfPn0ozM1lYHIyx06eJDEhgbjYWGJiYoiNicH8XROmjhuHw9fsG/+/LQWIAUlJDBowgC27d9O3b18G9O//0QeHlg8cW/fuvSZHNBudn8+GHTv41+ee4++efNLrOHIdTp87x8uLFpHUrx+PfupT3dpEv6Nv6qNV6yKEmTFj0iRmTJrEiTNneHPZMl5fsoS//PGPSRk0iFk33cTEwkKP0vZcZhYLPA3cg2+Mpk1m9rZzLvCX7zyg0H+bAfwKmNHJtkuBH/pbU/4z8EPg+2Y2Dl83zfFABrDMzEY555q64/mKiIj0Rioq9BKtL7BPnD7Ne+vWUZib22Fz/xvVt08f7pgxgztmfDwLaXNzM1cbGnCAa27G4Rur4e333//Eti0fbjr6hvKz99zTaYYPT5zoUvZQ6c7m01nDhtG/b18OlJd32zHlxl28fJmXFi4kxozHHnggJK1MorXFTXdYtXkzKYMG8c6zz/LeunX82/PPs/CDD1i5aRNNzc08Mncu8dfRDUw6NB0odc6VAZjZy8B8ILCoMB/4rX+w5/VmNsjM0oG89rZ1zi0J2H498HDAvl52ztUD5WZW6s+wLlxPUEREpLfTVVMv1NDYyBvLltG3Tx8euOOObm/2GxMT81GT5HB+4OmNfaFjYmIYlZfHntJS6q9e1YB0UaC5uZkFJSXU1dfzpfnzGTJwoKd5etP7Ji42lvtuvZWLly9TdvQoa7Zu5V9+8xteeOstfvm3f8uI7OzOd4IKOJ3IBI4G3K/C1xqhs3Uyg9wW4GvAKwH7Wt/Gvj7BzB4HHgfIycnp7DmIiIhIB1RU6MHa+3CwbO1aTp87x2MPPED/vn27OVVk6MkfnEbn57Nt3z427NzJnOJir+OIX3vn3I79+zl28iSfuftu0lNTIyJTb2NmjPBP2bnv8GEWr1zJn3z3u9wxfTrTi4qibiaYCNNW1br1IDntrdPptmb210Aj8OJ1HA/n3LPAswDFxcVtD9ojIiIiQVFRoZc5VFHBlj17mFFUFPS3cF6J9g88XuXPy8oiIT6eD9avV1EhwtXV1/PBhg1kDx/O+JEjvY4jwNgRI8hOT2fRihUsW7eOAxUVfO7eezXwaddVAYF/bLKAY0Guk9DRtmb2Z8ADwF3u49F8gzmeiIiIhJC+fulFmpubWbJmDamDB3PHzJlex5EwiYuNZWRuLss3baKpSWOTRbLVW7ZQU1fHvbfeGvZuSAtKSj66SceS+vXjkblzefDOO6k+dYrfL1xIXX2917Gi1Sag0MzyzSwB3yCKb7da523gy+YzE7jgnKvuaFv/rBDfBx50ztW02tejZpZoZvn4Bn/cGM4nKCIi0tuppUIvsvvQIc5dvMgjc+cSFxsb9uMF08/4Rj/ghHPU+mj+8DUmP5+9paVs378/rANxSufaO4/OnD/Pxl27mDxmTLd3exCfjt7jZkbR6NH079uXVxYv5uVFi/jiAw8QHx/fjQmjn392hieBEnzTQj7nnNtjZk/4lz+Db8rq+4FSfFNKfrWjbf27/iWQCCz1F+TWO+ee8O/7VXwDQTYC39bMDyIiIuGlokIv0dzczOotW0hLSWFUXl63HjuaP5xHqxE5OcTHxfH++vUqKkSoZWvXEhcby+0z2hp3LnT0/rsxI3Jy+Mzdd/PG0qX8oaSEz8+bR2w3FGV7EufcInyFg8DHngn4vwO+Hey2/sfb7S/knHsKeKqreUVEROT6qPtDL7H38GHOXrjA7KlTu322B+l+iQkJzJg0iQ82bODjrsao+XuEOFxZyaEjR5g9dar66keBcSNGcP9tt1F29Chvvvcezc3NXkcSERERiRhqqdALtLRSSB08mDEFBV7HCRt9WP6k26ZNY/WWLRw5doy8zGtmVBOPOOdYtnYtgwcOZFpRkddxJEg3jR1L/dWrLFu7lmEpKdw6darXkUREREQigooKvcD+sjJOnzvHZ++5R60UepGbb7oJgLXbtqmoEEEOV1Zy6tw5PnPXXd0yton4hKLoOHPSJKqOH2f1li2MLywMQSoRERGR6KeiQg/nnGPVli0MHTyYsT24lYJca9327QweOJB127fzhQce8DqO+G3atYukfv0YO2KE11GkC+695RbKjh6lZNUqBiUnq1ArIiIivZ7GVOjhDpSXc+rsWW6dMoWYGP24e5uCrCw27drF1YYGr6MIcObcOQ4fPcrU8eM12F+UGpCUxG3TplFaWcmB8nKv44iIiIh4Tp8ye7i127YxZOBAxo1sd6Bs6cEKsrOpq69nx/79XkcRYNPu3cTGxDBl3Divo8gNmDZxIsNSUihZvZr6q1e9jiMiIiLiKRUVerCTZ85w7ORJiidMUCuFXiovM5O42FjWbd/udZRer6W4M76wkP6a8SGqxcTEMG/OHC5ducLKTZu8jiMiIiLiKX3S7MF27N9PTEwMEzSgWK+VmJBA0ejRrFdRwXM79u+nobGRaRMneh1FQiBr+HCmjBvHxl27OHH6tNdxRERERDyjokIP1dTUxK6DBxmdl0e/vn29jiMemjV5MvvKyjh74YLXUXqt5uZmNu3eTdbw4aSnpnodR0LkjpkzSUxIYNWWLYBvhomWm4iIiEhvoaJCD3WwooKaujomjx3b7cfWRXVkmTl5Ms45NuzY4XWUXqu0spLzFy8yvajI6ygSQn0TE5kybhwHyss5f/Gi13FEREREPKGiQg+1Y/9+kvv3Jz8ry+so4rFxI0YwMDlZ4yp4aNOuXST378/ovDyvo0iITZ0wAYDNu3d7nERERETEGyoq9EAXL1/m8NGjFI0erQEahTeWLSMzLY0PNmzAOed1nF7n7IULlFdVaRrJHmpgUhJjCwrYtm+fpm4VERGRXkmfOHuQlm4Huw4exDnHpDFjvI4kESI/O5tLV65w+tw5r6P0OrsPHgSgaPRoj5NIuEwvKqL+6lV2HjjgdRQRERGRbqeiQg/jnGP7/v3kZGQwZOBAr+NIhCjwd4M5fPSox0l6F+ccuw4eJC8zkwFJSV7HkTDJHDaMjLQ0Nu7cqdZAIiIi0uuoqNDDVFZXc+7CBSarlYIEGJicTMqgQZSpqNCtdh44wLmLF5k4apTXUSSMzIzpEydy9sIFDldWeh1HREREpFupqNDD7Ni/n4T4eMYWFHgdRSJMQXY2lceO0djY6HWUXmFBSQm/eOEF4uLiGKP3Y483dsQIkvv3Z8POnV5HEREREelWKir0IA2Njew7fJhxI0cSHx/vdRyJMAVZWTQ2NXH0+HGvo/QKTU1N7C0tZXReHokJCV7HkTCLjY1l6vjxlFdVcersWa/jiIiIiHQbFRV6kLKjR2lobGTciBFeR5EIlJORgZlR8eGHXkfpFUorK6mtr1fXh15kyrhxxMXGsmXPno8GzhURERHp6YIqKpjZXDM7YGalZvaDdta53cy2m9keM1sR2pgSjH2HD9M3MZHcjAyvo0gESkxIIDMtjYqqKq+j9Aq7Dhygf9++FGRnex1Fukm/vn0pzM1l3+HDNDc3ex1HREREpFt0WlQws1jgaWAeMA54zMzGtVpnEPCfwIPOufHAI2HIKh242tDAwYoKRufnExsb63UciVB5WVkcO3WKS1eueB2lR7t4+TKHjhxhfGEhMTFqENabjB0xgiu1tVRWV3sdRURERKRbBHO1Ox0odc6VOeeuAi8D81ut8wXgdedcJYBz7mRoY0pn1m/fztWGBsaq64N0IC8zE+ccW/bs8TpKj7ZkzRqampvV9aEXGpmbS1xcHPsOH/Y6ioiIiEi3CKaokAkEzkNX5X8s0ChgsJktN7MtZvbltnZkZo+b2WYz23zq1KmuJZY2LV27lj4JCeRltv7RiHwsa/hw4uLiNEJ9mL2zfDlDBw9m+NChXkeRbpYQH09hTg77y8pobm7W2AoiIiLS4wVTVLA2HnOt7scBU4FPAfcB/9vMrvmKzjn3rHOu2DlXnJqaet1hpW0NDQ0s37iRUer6IJ2Ii40le/hwNqqoEDYfnjjBtn37mDhqFGZt/fqUnk5dIERERKQ3iQtinSogcKSxLOBYG+ucds5dAa6Y2UpgEnAwJCmlXQtKSjhcWcmlK1cYW1DgdRyJAvlZWby/fj2nzp4ldcgQr+P0OEvWrAFg/MiRHicRrwR2gVDrMREREenpgmmpsAkoNLN8M0sAHgXebrXOW8BsM4szs37ADGBfaKNKe/YdPkxiQgL5GmVegpCflQXAxl27PE7SMy1ds4YJhYUMGjDA6yjikdZdIHq7zmaQMp9f+JfvNLMpnW1rZo/4Z5tqNrPigMfzzKzWPxvVdjN7JvzPUEREpHfrtKjgnGsEngRK8BUKXnXO7TGzJ8zsCf86+4B3gZ3ARuA3zrnd4YstLZqamjhQXk5hXh5x6vogQRiWksKApCR1gQiDo9XV7D18mHtvucXrKOIxdYHwCWYGKf+yQv/tceBXQWy7G3gIWNnGYQ875yb7b0+E+CmJiIhIK8F0f8A5twhY1OqxZ1rd/ynw09BFk2Acqa6mtr5eXR8kaDExMUybMIGNO3finFO//xBaunYtAHfffDNrt23zOI14SV0gPvLRDFIAZtYyg9TegHXmA791zjlgvZkNMrN0IK+9bf1fZuj3l4iISATQBOpRbt/hwyTEx1Ogrg9yHaYXFVF96hRHjx/3OkqPsmTNGopGjyYjLc3rKOIxdYH4SDAzSLW3TjDbtiXfzLaZ2Qozm339kUVEROR6qKgQxZqamjhQVsbI3Fzi44JqdCICwIxJkwDUBSKEjhw7xv6yMu5R1wfxUxcIILgZpNpbJ5htW6sGcpxzNwHfA14ys2sGONEU1yIiIqGjokIU23ngADV1dYzJz/c6ikSZ3IwM0lJS2LBjh9dReoyl/lkf7pk1y+MkEikCu0D0YsHOINXWOsFs+wnOuXrn3Bn//7cAhwFNcS0iIhJGKipEsQ82bCA2JoYROTleR5EoY2ZMLypi065dvb1pdsgsWbOGSWPGMFwfUMQvIT6egqwsSisr8Q0X0CsFM4PU28CX/bNAzAQuOOeqg9z2E8ws1T/AI2ZWgG/wx7LQPiUREREJpKJClHLO8cHGjeRlZpKYkOB1HIlCM4uKOH/pEgcrKryOEvUqPvyQgxUV3HvLLSwoKWFBSYnXkSRCFGRnc+HSpU90gehN50gwM0jhGwi6DCgFfg18q6NtAczss2ZWBcwC3jGzlhd0DrDTzHYAC4AnnHNnu+GpioiI9FrqiB+lyqqqOFpdzbw5c7yOIlFqun9chXXbtzNGs4fckJauD3fffDOrNm/2OI1EkpZBdNdt20ZuRobHabzR2QxS/lkfvh3stv7H3wDeaOPx14DXbjCyiIiIXAe1VIhSyzdsAGBUXp63QSQqLSgpYeWmTYzMyWG9xlW4YUvWrOGmsWMZlpLidRSJMEMGDmTQgAGs277d6ygiIiIiYaGiQpT6YMMGJhQWkty/v9dRJIrNnDyZbXv3Uldf73WUqFV29CiHjhzhXs36IO0YkZ3Npl27aGho8DqKiIiISMipqBCFTp49y+5Dh7h9+nSvo0iUmzlpElcbGti6d6/XUaLW0rVrMTPuuvlmr6NIhCrIzqamro5f/O53vWYsBREREek9VFSIQis3bQLg9hkzPE4i0W7q+PHEx8WpC8QNWLJmDVPGjSNtyBCvo0iEysvMxMw4XFnpdRQRERGRkFNRIQp9sGED2cOHMyI7u/OVRTrQt08fJo8dy3r19+6S0spKDldWco+6PkgHEhMSyBo2jPKqKq+jiIiIiIScigpR5kptLRt37uSOGTMwM6/jSA8wc9IkDlZUcOb8ea+jRJ2la9YQExPD3bNmeR1FIlxBTg7Vp05xpbbW6ygiIiIiIaWiQpRZs3UrDY2NGk9BQmbW5MkA6gJxnZxzLFmzhqnjx7N840YWlJSov7y0qyArC0CtFURERKTHUVEhyizfuJHBAwYwacwYr6NIDzGmoIBByckfdYHQh+PglB45QnlVlWZ9kKCkp6bSNzGRsqNHP3pM7zURERHpCVRUiCINjY2s2ryZ2cXFxMbGeh1HeoiYmBimFxWxfvt2nHNex4kaS/xdH+6aOdPrKBIFYmJiyM/KouzoUb3PREREpEdRUSGKbN2zh0tXrnCHZn2QEJs1eTKnzp3jcMC3qNK+lq4P0yZMYMigQV7HkShRkJ3N5ZoaTp0963UUERERkZBRUSGKLN+4kT4JCcz094EXCZWWc0qzQATnYEUFR44d06wPcl0K/DP2lLUq3qkbhIiIiEQzFRWihHOO5Rs3MnPyZPomJnodR3qY9NRUcjMyWKeiQlCWrFlDbEwMd6rrg1yHAUlJDB08WC2CREREpEdRUSFKHCgvp/rUKc36IGEza/JktuzZQ2NTk9dRItpHXR8mTmTIwIFex5Eok5+VRdXx4zTpfSYiIiI9hIoKUeKDDRuIiYlhzrRpgJrLSujNnDyZuvp6qo4f9zpKRDtQXs7R6mruvfVWr6NIFMpJT6ehsZHqU6e8jiIiIiISEp4VFc5dvKgPxdfhzffeIzMtTd+MSlgsKCnh2MmTxMbEcOjIEa/jRLR3V60iLjaWOzVgqnRBTno6AJXV1R4nEREREQkNtVSIAsdOnuTE6dOMys/3Oor0MIEtXhLi48nNzORQRYW3oSJYc3Mz765axaybbmLQgAFex5Eo1L9fP1IGDaLy2DGvo4iIiIiEhOdFhZYPNWq10L7lGzcCMDovz9sg0uONysvj7IULnDl/3usoEWn7vn0cP32aebNnex1FolhuRgZHjx+nubnZ6ygiIiIiNyzO6wDSueUbNjB08GCGDBqk4ouE1cicHAB1gWjH4lWr6JOY+NGAqXo/SlfkpKezde9eTpw5Q3pqqtdxRERERG6I5y0VpGMXL19my549jFIrBekGgwYMIHXIEHWBaENDYyNL16zh9unT6de3r9dxJIrlZGQAqAuEiIiI9AgqKkS4VVu20NTczGiNpyDdpDA3l8rqai5evux1FE+17pa1fvt2zl+6xP1z5qjLltyQAUlJDB4wgCMqKoiIiEgPoKJChPtgwwZSBw8mIy3N6yjSSxTm5eGcY9327V5HiSiLV61iQFISsyZP9jqK9AA5GRkcra7GOed1FBEREZEbElRRwczmmtkBMys1sx90sN40M2sys4dDF7H3utrQwNqtW7lt+nTMzOs40ktkpqXRr08fVmza5HWUiLCgpISXFi5k6Zo13HPzzcTHx3sdSXqAnPR0auvrOXX2rNdRRERERG5Ip0UFM4sFngbmAeOAx8xsXDvr/TOgNsEhsm7bNmrq6rhjxgyvo0gvEhMTw4icHNZs3UpjU5PXcSLCoYoKGhobmTdnjtdRpIf4aFyF6mqPk4iIiIjcmGBaKkwHSp1zZc65q8DLwPw21vsO8BpwMoT5erWSNWsYmJzM9KIir6NIL1OYl8eFS5fYuX+/11Eiwp7SUpL792fKuGvqqSJdMig5mQFJSRpXQURERKJeMEWFTOBowP0q/2MfMbNM4LPAMx3tyMweN7PNZrb54oUL15u1V6mrr2f5hg3cOXMm8XGa+VO614jsbOJiY1m5ebPXUTxXW1dHaWUl40eOJCZGw9BIaJgZORkZVB47pnEVREREJKoFc4XcVmf+1ldA/w583znXYVtp59yzzrli51zxgIEDg83YK63ZupWaujruu/VWr6NIL5SYkMDU8eNZqXEV2FdWRnNzMxMKC72OIj1MTno6V2prOXv+vNdRwqqzcZnM5xf+5TvNbEpn25rZI2a2x8yazay41f5+6F//gJndF95nJyIiIsF8BV4FZAfczwJat9csBl72DyY4FLjfzBqdc2+GJGUv9O6qVfTr04fKY8eoOn7c6zjSC80uLuZnzz3H0epqstPTvY7jmZ379zN08GCGDR3qdRTpYXL94yocqa4mZfDgHjlNacC4TPfgu57YZGZvO+f2Bqw2Dyj032YAvwJmdLLtbuAh4L9aHW8c8CgwHsgAlpnZqM6+9BAREZGuC6alwiag0MzyzSwB3x/rtwNXcM7lO+fynHN5wALgW6EoKLTMBd8TL7Q6UlNby6rNmxk7YoSaW4tnbps2DYD3N2zwOIl3Tp87R9WJE0waM0YzsEjIDRk4kP59+1LZs8dVCGZcpvnAb53PemCQmaV3tK1zbp9z7kAbx5sPvOycq3fOlQOl/v2IiIhImHT6idU51wg8iW9Wh33Aq865PWb2hJk9Ee6AvdHKzZupu3qVcSNHeh1FerHs9HTGjRzJuytXeh3FMzsPHMDMmDhqlNdRpAcyM3IzMjjSs8dV6HRcpg7WCWbbrhzvE2M8nTp1qpNdioiISEeCGgHQObcIWNTqsTYHZXTOfeXGY/VuJatXkzp4MNnDh3sdRXq5eXPm8K/PPUd5VRX5WVlex+lWzc3N7DxwgJE5OST16wfQ61pNSfjlZGSw9/Bhzl+6xOABA7yOEw7BjMvU3jrBbNuV4+GcexZ4FqC4uLjHVnRERES6g9rWR5jLNTWs2bqVu2+5RV0fxHP33XorZsbiVatCut9o6NZUdvQol2tqmDRmjNdRpAfL8Y9X0oO7QAQzLlN76wSzbVeOJyIiIiGkT60RZvmGDVxtaGCuZn2QCJA2ZAjTJ05k8cqVPbl5dpt27N9Pvz59KMzN9TqK9GCpQ4bQt08fjvTcokKn4zL573/ZPwvETOCCc646yG1bext41MwSzSwf3+CPG0P5hEREROSTVFSIMO+uXk16aipFo0d7HUUE8HWBOFpdze5Dh7yO0m3OX7zIwYoKJhQWEhsb63Uc6cHMjJz0dCqrq72OEhZBjsu0CCjDN6jir4FvdbQtgJl91syqgFnAO2ZW4t9mD/AqsBd4F/i2Zn4QEREJr6DGVOhukd4sOlwuXLrE+u3b+cKnP62R5iVi3DlzJk898wyLV67sNQMWLl65kqbmZnV9kG6Rk57OgfJyLl6+zICkJK/jhFxn4zI5XzOobwe7rf/xN4A32tnmKeCpG4gsIiIi16HXtFSIhj7cJatX09jURHxcXMRnld5jQFISs4uLKVm9mqam3vGF31vvv8/woUMZNnSo11GkF8jNyAB69LgKIiIi0oNFVFEhGj74h4tzjteWLGFUXh7D9UFGIsz9c+Zw5vx5Nu3e7XWUsDtQXs7+sjK1UpBuk5aSQmJCAkd6aBcIERER6dkiqqjQm+09fJgD5eV87t571fVBIs6tU6eS1K8fi1eu9DpK2L353nvExcUxvrDQ6yjSS8TExJCdnq6WCiIiIhKVVFSIEK+VlNAnIYH7b7vN6ygi1+iTmMidM2fy3rp11F+96nWcsKmpreWP77/PPTffTL8+fbyOI71ITno6Z86f53JNjddRRERERK6LigoR4EptLYtXreLeW28luX9/r+OItGnenDlcrqlh5ebNXkcJm3dWrOByTQ2P3n+/11Gkl/loXAV1gRAREZEoo6JCBHh31Spq6+r43L33eh1FpF3TJ05k+NChvLp4sddRwsI5xyuLFjGmoEBTukq3Gz50KPFxceoCISIiIlFHRYUI8NqSJYzMydEHGYlosbGxPPbAA2zatYv9ZWVexwm5LXv2UFpZyaP3369xTaTbxcbGkjV8uIoKIiIiEnVUVPDY/rIy9paW8pAGaJQoEBsTQ0J8PC+89ZbXUULu5UWLGJiczNzZs72OIr1UbkYGJ8+epbauzusoIiIiIkFTUcFjry1ZQmJCAp/SAI0SBfokJjJ57FhKVq/mxOnTXscJmRNnzvDB+vXMv+su+iQmeh1Heqmc9HRA4yqIiIhIdFFRwUO1dXUsWrGCe26+mYHJyV7HEQnKtIkTaXaO37/zjtdRQua1khKaneNP5s71Oor0YhnDhhEbG6uigoiIiEQVFRU89M6KFVypreWhe+4BYEFJCQtKSjxOJdKxwQMGcNfMmSwoKaGmttbrODesoaGB15YsYfbUqWQNH+51HOnF4mJjyRw2TOMqiIiISFRRUcEjDY2NPPfaa4wvLOSmceO8jiNyXb40fz6Xa2p48733vI5yw5atW8eZ8+f5vKaRlAiQm5HB8R7UtUhERER6PhUVPPLOihUcO3mSb/7Jn2iARolIHbWcKRo9mkljxvDiH/9IU1NTNycLnT+8+y6/eOEFstPTmTV5stdxRMjLzMQ553UMERERkaCpqOCBxqYm/u+CBYwpKGB2cbHXcUS65EsPPsiHJ07wwYYNXkfpssOVlVSfOkXR6NG8vnSpuh+J57KGDSM+Ls7rGCIiIiJBU1HBAyWrVnG0uprH1UpBotgdM2aQk5HBL198kasNDV7HuW7OOVZv2cKApCSKRo3yOo4IALGx/3979x4fVXXvffzzyxUQSMJVFBCkqGhBCoJSblVAbgr0PN6PRS0KrfoclWoBtT0cPfoAWl/qeVQEpEW05S5EXlZQij5F5K5cFYgIEozcBEKRa7KeP2YHJ2GSTJKZ7Jnk+3699mtm1t579u+35rb3mrXXTjx7FQgRERGReKC/QypZXl4ek2fPpvVFF/GLzp39DkekVMX9e5+Y+wLh4QAAGL1JREFUmMjvhw3jwaefZvqCBQy76aZKjqxiVm/cSPbevfTr3p3ExMSz5eqtIH5r2bSp3yGIiIiIhE2NCpXsg+XL2blnDxMee4yEhAQdwEhc69axI72uuYbJs2bRr3t3Lmzc2O+QwjZp1ixq16pF+8su8zsUkULUqCAiIiLxRKc/RFBpl4TMz89n0qxZXNysGb27dKnEyESi57Fhw8CM5954w+9QwvbZli2s2bSJLu3bk6Tz1yXGNKpf3+8QRERERMKmRoVK9MHy5ezYvZv7br6ZhARVvVQN5zdsyIhbb+WjVav4f6tX+x1OWCbNmkVGWhoddDlXiUEaa0dERETiiY5sK8nRY8d4fupULmnRguu7dvU7HJGIuvPGG7m4aVPGTZ7M8ZMn/Q6nRBu3bePTzz9n6ODBJCcn+x2OiIiIiEhcU6NCJXl5+nQOHj7MHx94oNCgcCJVQXJyMmNGjODbffuY+Le/+R1OiSbOmEFanTrc0r+/36GIiIiIiMS9mDyZ+PSZMyxbu5ac/fs5deoUJ0+dYsrs2VzQqBG/7NOHPj//OTVSU/0OM2zrNm9m9vvv86tBg/hp69Z+hyMSFZ3atuV/XX890+bPp/VFF3HDtdf6HdI5Plq1ik/WrWPk3XdzXs2afocjIiIiIhL3wuqpYGb9zGyrmWWZ2egQ8//dzDZ403Izu7K8Ae09cICpc+bwybp1HD9xguTkZOqnp9OpbVsOHj7MH156ieuHDWPClCns2rOnvJupNCdPneKpV1/lgkaNuP+OO/wORySqRt93H53atmXsK6+weuNGv8Mp5PjJk4yfPJlWzZtz+w03+B2OiIiIiEiVUGpPBTNLBF4B+gDZwGozy3TObQla7Gugp3PukJn1ByYBV5clEOccKzdsYOmKFdSsUYPbBw6kVfPmZ+ff1LcvzjnWbNrEnEWLmPX++8x87z1uv+EGRtx6K3XOO68sm6s0U2bPZueePbz6n/9JzRo1/A5HJKqSk5Pp0akTO3bvZuS4cbw5fnzMXB7vjTlzyNm/nyn//d8k64oPIiIiIiIREU5Phc5AlnNuh3PuFDADGBy8gHNuuXPukPdwBVCmo4i8vDxm/v3vfLh8Oa2aN+e+W24p1KBQwMzo1LYt4x99lEVTpjCkd2/efvddhjzwAO8uXUp+fn5ZNnuO0i4JWVbbd+7kz/PmceO11/Lzn/0sYs8rEstqpqZy28CBJCcl8eDTT/P9kSN+h8SuPXuY9s47DOzZk6t++lO/wxGpVsLo7Whm9rI3f4OZdShtXTOrZ2YfmNl27zbDK29hZsfN7HNvmlg5WYqIiFRf4TQqXAjsDnqc7ZUVZxjw91AzzGy4ma0xszW5QQcaH69eTdauXVzftSs39+sX1rnO9dPT+cP99/PWc89xQaNG/OGll7jn8cf5cseOMFKKvkO5ufxuwgTq1q7N7+65x+9wRCpVRt26vPTEExw4dIjfjh3L3oMHfYvFOcf/mTSJ1JQUHrn7bt/iEKmOgno79gcuB243s6LXcu0PtPam4cBrYaw7GljinGsNLPEeF/jKOdfem34TncxERESkQDiNCqEumO1CLmh2LYFGhVGh5jvnJjnnrnLOXVU3LQ2AHbt3s/yzz2jfpg2d27Ur8/W5r/jJT5g2bhxjH3yQb3JyuOPRR3n29dc5cvRomZ4nko6fPMlDzzzD3gMHeGHMGNLr1vUtFhG/tL3kEv40ahS7c3K489FH2ZyV5UscHyxfzor163ngjjtokJHhSwwi1VipvR29x2+6gBVAupk1KWXdwcA07/40YEi0ExEREZHQwmlUyAaaBT1uCnxbdCEzawdMAQY758L6W/JfP/zAgiVLaJiRQd+uXUtctqRTExISEhjSuzcLXnmFW/v3Z86iRQy+/37mLl5MXl5eOKFETH5+Po+/8AIbt23j2Uceof1ll1Xq9kViSbeOHZk2bhxJSUn8+vHHWbRsWdjrRuJ0pJz9+xk/eTKXtmzJzUGXkIz0qU4iUqxwejsWt0xJ6zZ2zuUAeLeNgpZraWafmdnHZtY9VFDBPSf3799f1pxEREQkSDijla0GWptZS2APcBtQ6DIGZtYcmAf8yjm3LdyNL1iyhJOnT3PnoEEkJyeXIezQ6tauzaj77mNI796MnzyZp199lbmLFzNm+PCzyxQcSNzUt2+Ft1eUc47Fn3zCmk2bGHXvvRzKzT3nwCUa2xWpbGX5HLVu0YK3n3uOkePHM+r558natYsGGRkkJiZGNcZjx4/z0DPPcPLUKZ4dOZKkKG9PREIKp7djccuE3VMySA7Q3Dl30Mw6AvPN7ArnXG6hJ3FuEoFBpbnqqqtKe04REREpQak9FZxzZ4AHgUXAF8As59xmM/uNmRWcq/hHoD7wqjcw0prSnvdfx47xdXY2fbt2pWG9ehVI4VyXtmzJG888w7OPPML+gwf51e9/z8KlSzl2/HhEtxMsPz+fj1evZs2mTdw1ZIguWSfVQrj/+NdLT2fSU08x6LrrmDx7NlPnzeO7AweiFldeXh6Pv/ACX33zDRMee4xWzZqVvpKIREM4vR2LW6akdfd6p0jg3e4DcM6dLOgt6ZxbC3wFXBKRTERERCSksK6r5px7D3ivSNnEoPv3AveWZcNHf/iBXj/5Ce3btCnLamH/Q2pmDOjZkx6dOjFp1izeyszkix076NyuHZ3bti3TNktzKDeXJ198kU/WraPdpZfy0NChpa6jrtdS3aQkJ/PUf/wHPTt14o8vv8zUuXMx4L5bbiElAj2Vgr305pt8vHo1Y4YP15VXRPxVam9HIBN40MxmELgc9RHnXI6Z7S9h3UzgLmCcd7sAwMwaAt875/LM7GICgz/GxgjOIiIiVZRvF2s3M/p161bmgRmLCj44D9XQULtWLUbefTe1atTgHytW8M81a1i5fj2HcnO588YbqZeeXqHtf7ZlC6P+9CcOHTlC/+7d6XDFFSQkhDNUhUj1UPQz2qtLF77dt4/Fn3zC5Nmz+fDTT3l46FB6dOpU4e8DgLmLF/PmggXcPnAgtw4YUOHnE5Hyc86dMbOC3o6JwNSC3o7e/IkE/rQYAGQBPwD3lLSu99TjgFlmNgz4BrjZK+8BPGVmZ4A84DfOue8rIVUREZFqy7dGhTq1alErjEtHlkeo3gwNMjK4pX9/9h48yCfr1vHnefP467vv0rtrVwZde22Zr12/9+BBZr73HtPeeYcLGjVi+oQJbNq+PezYRKqjgvd/zRo1GNyrFyNuvZXn3niDh559lg6XX85DQ4dyZTkHNz1x8iQvT5/OXxcupGuHDvzu17+OZOgiUk5h9HZ0wAPhruuVHwR6hSifC8ytYMgiIiJSBr41KkSrQaE0jevX59/69KHjww/zVmYmi5YtY+HSpTRp2JBWzZtz0QUXcDg3N+RlIPPy8vj088+Zs2gR/1yzhrz8fPp1786Tv/0ttWvVCqtRQUR+1K1jR66+8koWLFnCxBkzuGv0aH7RuTO3DRyIcy7sngtbsrJ48sUX2ZGdze0DB/LQ0KEamFFEREREpBL4evqDn1o2bcof7r+fx4YNY+nKlby7dCnL1q5l2dq1vP3uu5zfoAGtW7TACIyZcDg3l4OHD/PDiRNkpKUxdMgQ/q1PH1Zu2MD7//ynr7mI+K0iPXCSk5K4qW9fBvbsyVuZmUzPzOSjVatIq1OH9m3a0L1jRxo3aHDOenl5eWzftYsPP/2Uv8ybR0ZaGq+NHUuX9u2jEqeIiIiIiJzLt0aFWFEjNZX+PXrQv0cP3pw/n+8OHKBR/fps/fprsnbtIjExkfQ6dbiwcWMy6talfZs2XHf11WcvgblywwafMxCJX8GnKtWsUYP7brmFoUOG8I8VK3h95kw+XrWKj1etIiMtjWbnn0+z88+nQUYG23ftYv2XX569oku/7t0ZM3w4aXXq+JmOiIiIiEi1E7eNCtH4x7FWzZpc3KxZqVeWKI3+DRUpv9SUFPr36MGx48f5/sgRUlNS+Obbb9mdk8O6LVvYd/AgLZs2ZUDPnrS/7DLat2nDhY0b+x22iIiIiEi1FLeNCqEUPZjXwb1IfKuXlnZOI19ZxloAfQ+IiIiIiERTlWpUEJGqL1SDQmmXlhURERERkehQo0I56d9Pkdikz6aIiIiISOWpto0KwQPEiUjklffgXo0CIiIiIiLxo9o2KhTQAYyIiIiIiIhI+ST4HYCIiIiIiIiIxKdq31NBRPxXUo8hDcIoIiIiIhK71FMhhDmLFum0CBEREREREZFSqFFBRERERERERMpFpz+UkXowiPhHV20REREREYktalQQkbilRj4REREREX+pUaEEOmARiU36bIqIiIiIxAaNqSAiIiIiIiIi5aJGBREREREREREpFzUqiIiIiIiIiEi5aEwFERERkQhbP2nSOWVXDh/uQyQiIiLRpZ4KIiIiIiIiIlIualQQERERERERkXLR6Q8iIiIiUbB37VoObdvGqaNHOXX0KFnz59Prf/6H9Fat/A5NREQkYtRTQURERCTC9q1fz4ZJk8hZuZKj2dm4/HxyVqzgrU6d2PnBB36HJyIiEjHqqSAiIiISQccPHGDzX/5CnWbN6DRqFInJyQBc1Ls384cMYW6/fvR8/nk6PvwwZuZztCIiIhUTVk8FM+tnZlvNLMvMRoeYb2b2sjd/g5l1iHyoIiIiEm8qsg9R3LpmVs/MPjCz7d5tRtC8Md7yW82sb/QzLCz/9Gk2TJ4M+fm0GzHibIMCwK4PP6TtvffS8Mor+WjkSF6pX5+/tGvHsiefZOvs2Xy/bRsuP7+yQxYREamQUnsqmFki8ArQB8gGVptZpnNuS9Bi/YHW3nQ18Jp3KyIiItVURfYhSll3NLDEOTfOa2wYDYwys8uB24ArgAuAD83sEudcXrRy/HLmTL5bs4aaDRpQs0EDdixcSO7OnbQbMYJaDRues3xSjRq0Gz6cPcuXc2jrVo5mZ7Ny3DhcXiDEpFq1aNi2Lec1aUJqWlpgSk8/ez8lLY2k1FQSkpN/nJKSSEhOJjG4rOCxNy8hOblwr4iC+0FlVpllIiJSJs654maELLaEhCKLOfJPnybv9GkSU1JISErCzHDOcebECU7l5nLmxAlSatcmpU4dElNSwo4tnNMfOgNZzrkdAGY2AxgMBO8QDAbedIFMV5hZupk1cc7lhB2JiIiIVDXl3ocAWpSw7mDgF97604CPgFFe+Qzn3EngazPL8mL4NFoJLnvySQ5nZRUqa96rF407FN9p0xISaNqtG027dQMg7/RpjuXkcHT3bo5mZ/OvPXs4uns3Z44fD0wnTkQr/NjgNTSEbPSgYo0YIZ9bROJWyAPrcMvKsH5ZDuCj8ZwRYRZoWE5KwuXnk3fy5Dnzk1JTyTt9+mzDdrDE1NSwNxVOo8KFwO6gx9mc2wsh1DIXAoUaFcxsODDce3jy5n79NoUdaXxoABzwO4gIU07xoarlVNXyAeUUL2Ilp4v8DiBCKrIPUdK6jQv+uHDO5ZhZo6DnWhHiuQopsj/yLzPbGm5CxSj8vlmyJDBJeAp2qM/dsY6Vz2NVojqNDtVr5KlOK8o5OH06MP3ox3p1DkpqtA40QoS1PxJOo0Kopt2i3/rhLINzbhIwCcDM1jjnrgpj+3FDOcUH5RT7qlo+oJziRVXMyWcV2YcIa9+iHNsrtD8SCXrfRIfqNfJUp9Gheo081Wl0RKtewxmoMRtoFvS4KfBtOZYRERGR6qUi+xAlrbvXO0UC73ZfGbYnIiIiERROo8JqoLWZtTSzFAIDIGUWWSYTGOqN4HwNcETjKYiIiFR7FdmHKGndTOAu7/5dwIKg8tvMLNXMWhIY/HFVtJITERGRME5/cM6dMbMHgUVAIjDVObfZzH7jzZ8IvAcMALKAH4B7wth2xLodxhDlFB+UU+yravmAcooXVTEn31RkH6K4db2nHgfMMrNhwDfAzd46m81sFoHBHM8AD0Tzyg9B9L6JDtVr5KlOo0P1Gnmq0+iISr1asaNQioiIiIiIiIiUIJzTH0REREREREREzqFGBREREREREREpF18aFcysn5ltNbMsMxvtRwzFMbNmZrbUzL4ws81m9pBXXs/MPjCz7d5tRtA6Y7xctppZ36Dyjma20Zv3spmZV55qZjO98pVm1qIS8ko0s8/MbGFVyMfbbrqZzTGzL73Xq0s852Vmj3jvuU1m9jczqxFv+ZjZVDPbZ2abgsoqJQczu8vbxnYzKxjALVo5Pee97zaY2Ttmlh7vOQXNe9TMnJk1qAo5mdn/9uLebGYT4ikniQ8Ww/s0scjMdnqfsc/NbI1XFrHfierAz9/aqqyYeh1rZnu89+vnZjYgaJ7qtRRWRY+r/FZCvfr3fnXOVepEYLClr4CLgRRgPXB5ZcdRQnxNgA7e/TrANuByYAIw2isfDYz37l/u5ZAKtPRyS/TmrQK6ELhu9t+B/l75/cBE7/5twMxKyGsk8Fdgofc4rvPxtjUNuNe7nwKkx2tewIXA10BN7/Es4O54ywfoAXQANgWVRT0HoB6ww7vN8O5nRDGn64Ek7/74qpCTV96MwKB4u4AG8Z4TcC3wIZDqPW4UTzlpiv2JGN+nicUJ2Fnw/RJUFrHfieowFfN9F1f7C7E4FVOvY4FHQyyreg2vTqvkcZXfUwn16tv71Y9K6AIsCno8Bhjj94tTQrwLgD7AVqBJ0Au5NVT8BHbIu3jLfBlUfjvwevAy3v0k4ADeoJlRyqEpsAS4jh8bFeI2H287dQkchFuR8rjMi0Cjwm4CByZJwEICB65xlw/QgsI/yFHPIXgZb97rwO3RyqnIvF8Cb1eFnIA5wJUE7fTHc04EGud6h1gubnLSFNsTcbZPEwsToRsVIvY7UV2mEN93cbe/EItTiHodS+iDNNVr+eo37o+rYnEKqlff3q9+nP5QcPBUINsrizleN4+fASuBxi5w3Wy820beYsXlc6F3v2h5oXWcc2eAI0D9aOTgeRH4PZAfVBbP+UDgX6H9wJ8tcFrHFDM7jzjNyzm3B3iewKXRcghcp31xvOZTRGXk4Of3yq8JtOwWiq9IHDGfk5kNAvY459YXmRW3OQGXAN29bnsfm1mnovEViSMecpLYote/7Byw2MzWmtlwryySvxPVVVXYX4hVD1rgdMepQd30Va9lVIWOq2JKkXoFn96vfjQqhDrnzVV6FKUws9rAXOBh51xuSYuGKHMllJe0TsSZ2Q3APufc2nBXCVEWM/kESSLQRe0159zPgGMEuk8VJ6bz8j70gwl0SboAOM/M7ixplWJii4l8whTJHHzJzcyeAM4AbxcUFRNHTOdkZrWAJ4A/hppdTBwxnZMnicApCdcAjwGzvHMF4zkniS16/cuuq3OuA9AfeMDMepSwrD53FVcV9hf89BrQCmhP4E+fP3nlqtcyqCrHVbEmRL369n71o1Ehm8B5uwWaAt/6EEexzCyZwAv0tnNunle818yaePObAPu88uLyyfbuFy0vtI6ZJQFpwPeRzwSArsAgM9sJzACuM7O3iN98CmQD2c65gla5OQQaGeI1r97A1865/c6508A84OfEbz7BKiOHSv9escCAfDcA/+68vmElxBHrObUi0KC13vuuaAqsM7PzS4gj1nMqiGOeC1hFoLdWgxLiiIecJLbo9S8j59y33u0+4B2gM5H9naiuqsL+Qsxxzu11zuU55/KByQTer6B6DVsVO66KGaHq1c/3qx+NCquB1mbW0sxSCAz8kOlDHCF5/2K9AXzhnHshaFYmcJd3/y4C564UlN/mjZDZEmgNrPK68hw1s2u85xxaZJ2C57oJ+EfQQUlEOefGOOeaOudaEKjrfzjn7ozXfILy+g7YbWaXekW9gC1xnNc3wDVmVsuLoxfwRRznE6wyclgEXG9mGV6vj+u9sqgws37AKGCQc+6HoFlxmZNzbqNzrpFzroX3XZFNYACg7+I1J898AmPJYGaXEBhI70Cc5ySxJab3aWKNmZ1nZnUK7hP4vGwisr8T1VVV2F+IOQUHvp5fEni/guo1LFXtuCpWFFevvr5fixtsIZoTMIDAKJVfAU/4EUMJsXUj0LVjA/C5Nw0gcA7JEmC7d1svaJ0nvFy2EjT6MHCV92J+BfxfvMEtgBrAbCCLwIibF1dSbr/gx4Eaq0I+7YE13ms1n0A357jNC/gv4EsvlukERmiNq3yAvxHobnWawIHpsMrKgcDYBlnedE+Uc8oicJ5ZwXfExHjPqcj8nQQNpBavORFoRHjLi3EdcF085aQpPiZieJ8m1iYC4yGt96bNBfVFBH8nqsNUzPddXO0vxOJUTL1OBzYS2NfMxBtcUPUadp1W2eOqGK1X396vBSuJiIiIiIiIiJSJH6c/iIiIiIiIiEgVoEYFERERERERESkXNSqIiIiIiIiISLmoUUFEREREREREykWNCiIiIiIiIiJSLmpUEBEREREREZFyUaOCiIiIiIiIiJTL/wcruManpG/wLAAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"### Distribution of Fraud and Genuine Transactions"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig, axs = plt.subplots(ncols=2, figsize=(18,4))\n\nsns.distplot(df[(df['Class'] == 1)]['Time'], bins=100, color='saddlebrown', ax=axs[0])\naxs[0].set_title(\"Distribution of Fraud Transactions\")\n\nsns.distplot(df[(df['Class'] == 0)]['Time'], bins=100, color='purple', ax=axs[1])\naxs[1].set_title(\"Distribution of Genuine Transactions\")\n\nplt.show()","execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1296x288 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"### Amount of transactions according to time"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig, axs = plt.subplots(nrows=2,sharex=True,figsize=(16,6))\n\nsns.scatterplot(x='Time',y='Amount', data=df[df['Class']==1], ax=axs[0])\naxs[0].set_title(\"Distribution of Fraud Transactions\")\n\nsns.scatterplot(x='Time',y='Amount', data=df[df['Class']==0], ax=axs[1])\naxs[1].set_title(\"Distribution of Genue Transactions\")\n\nplt.show()","execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x432 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"### Both Transactions Relative to Time"},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.set_style('whitegrid')\nax = sns.lmplot(y=\"Amount\", x=\"Time_min\", fit_reg=False,aspect=1.8,\n data=df, hue='Class')\nplt.title(\"Amounts by Minutes of Frauds and Normal Transactions\",fontsize=16)\nplt.show()","execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 690.375x360 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"ax = sns.lmplot(y=\"Amount\", x=\"Time_hour\", fit_reg=False,aspect=1.8,\n data=df, hue='Class')\nplt.title(\"Amounts by Hour of Frauds and Normal Transactions\", fontsize=16)\n\nplt.show()\ndf.drop(['Time_hour', 'Time_min'], axis=1, inplace=True)","execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 690.375x360 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Distribuiton of Amount vs Class"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16,7))\ns = sns.boxplot(ax = ax1, x=\"Class\", y=\"Amount\", hue=\"Class\",data=df, palette=\"PRGn\",showfliers=True)\ns = sns.boxplot(ax = ax2, x=\"Class\", y=\"Amount\", hue=\"Class\",data=df, palette=\"PRGn\",showfliers=False)\nplt.show();","execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x504 with 2 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAA8AAAAGpCAYAAABCsl8gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO39bZRV5Z0n/H8PVVpGeVLsohQZ007TK9wGFcEHOgamKUtakZGodCdOTOSfRIckppUZE9G0QTpt0mulY5tkzQSWuY2TBzOJncCEmo4PlY64csehQWhCQjJxVmr9QaEqjSBFEJDi3C+8rQSREqWqdtXZn8+r2hfn7PPbxV7nqu++rn3tSrVarQYAAABq3LCiCwAAAICBIAADAABQCgIwAAAApSAAAwAAUAoCMAAAAKVQX3QBA239+vVpaGgougwAasS+ffty3nnnFV3GkKZvBqAv9dY3ly4ANzQ0ZOLEiUWXAUCN2LRpU9ElDHn6ZgD6Um99synQAAAAlIIADAAAQCkIwAAAAJRC6e4BBmDweemll7Jly5bs3bu36FKO6IQTTsgZZ5yR4447ruhSSsE5AUB/EIABKNyWLVsyYsSIvPWtb02lUim6nMNUq9Vs3749W7ZsyR/+4R8WXU4pOCcA6A+mQANQuL1792bMmDGDMugkSaVSyZgxYwb1aGStcU4A0B8EYAAGhcEadF4x2OurRYP9dz7Y6wPgcAIwAAAApSAAAzAk/OY3v8mtt96aSy+9NFdccUU+9KEP5de//nWuvPLKokujQM4LAN4Ii2ABMOhVq9V89KMfzdy5c3PvvfcmSTZt2pTt27cXXBlFcl4A8EYJwAAMek899VTq6+vznve8p6dt4sSJ2bJlS8/2li1b8vGPfzwvvvhikuSv/uqvcv7556ezszO33nprdu/ene7u7ixevDiTJ0/OnXfemY0bN6ZSqeSaa67JDTfcMNCHxTFyXgDwRgnAAAx6v/rVr3L22Wf3+poxY8bkgQceSENDQ9rb27Nw4cJ897vfzcqVK3PJJZdkwYIF6e7uzosvvphNmzalo6MjK1euTJLs2rVrIA6DPua8AOCNEoBr3K5du/LQQw/luuuuy4gRI4ouB6DfHDhwIEuWLMkvfvGLDBs2LO3t7UmSSZMm5Y477siBAwdy6aWXZuLEiRk/fnw2b96cv/7rv86MGTNyySWXFFs8/cZ5AcDvswhWjWtra0t7e3va2tqKLgXgTZswYUJ+9rOf9fqar371qzn11FOzYsWK/MM//ENeeumlJMkFF1yQr3/96xk7dmw+/vGPZ/ny5Rk1alRWrFiRCy+8MN/85jdz5513DsRh0MecFwC8UQJwDdu1a1fWrl2barWaNWvWpKurq+iSAN6Uiy++OPv378+3v/3tnrYNGzbkueee69nu6urKH/zBH2TYsGFZsWJFuru7kyTPPvtsxowZkz//8z/PNddck5/97Gd5/vnnU61WM2vWrPzlX/5lfv7znw/4MXHsnBcAvFGmQNewtra2VKvVJC+vlNnW1pa5c+cWXBXAG1epVPKlL30p99xzT5YtW5aGhoaMGzcud9xxR89rrrvuutx88835wQ9+kIsuuignnnhikmT16tX5yle+kvr6+px44on527/923R2dmbRokU5ePBgkmThwoWFHBfHxnkB1Jq1a9dmzZo1fba/3bt3J0mGDx/eZ/ucOnVqpkyZ0mf7G2iV6isJqSQ2bdqUiRMnFl3GgPjUpz6Vffv29Ww3NDTk7rvvLrAigNc2VL6bX6vOoVL7YDaUf69DpU5gaOjrALx169YkyWmnndZn+xwKAbi372YjwDXsvPPOy5o1a9Ld3Z26urpMnjy56JIAAIAjmDJlSp+Gy6VLlyZJbrrppj7b51DnHuAa1tzcnEqlkuTlaWLNzc0FVwQAAFAcAbiGjRw5MlOmTEmlUsnUqVM9BgkAACg1U6BrXHNzczo7O43+AgAApScA17iRI0ea8w8AAJB+nAK9devWXH/99bn88ssze/bsPPjgg0mSL37xi3nnO9+Zq666KldddVWeeOKJnvcsXbo0LS0tmTVrVp588sme9o0bN2bOnDlpaWnJpz/96Z5H++zfvz+33HJLWlpaMm/evGzZsqW/DgcAAIAhrt9GgOvq6nL77bfn7LPPzu7du3PNNdfkHe94R5LkhhtuyAc+8IFDXv/MM8+ktbU1ra2t6ejoyPz58/PII4+krq4uixcvzpIlS3LeeeflQx/6UFatWpUZM2bkO9/5TkaOHJnHHnssra2t+dznPpe///u/769DAmCAPPCVB7K767d9tr/hI07K/A/M7/U1q1atyt/8zd/k4MGDmTdvXm688cY++3yOXRHnROK8AKg1/RaAGxsb09jYmOTlBy+fddZZ6ejoOOLr29raMnv27Bx//PEZP358zjzzzGzYsCHjxo3L7t27ex7hM3fu3LS1tWXGjBn54Q9/mI9+9KNJklmzZmXJkiWpVqs9Kx8DMDTt7vpt/uT/+nd9tr//5+c/6vXfu7u7s2TJkjzwwAMZO3Zsrr322sycOTN/9Ed/1Gc1cGwG+pxInBcAtWhA7gHesmVLNm3alHPPPTdPP/10vvGNb2T58uV5+9vfnttvvz2jRo1KR0dHzj333J73jB07Nh0dHamvr09TU1NPe1NTU0+Q7ujo6Hmoc319fUaMGJEdO3bklFNOOWIt+/bty6ZNm/rpSAF4M1566aW8+OKLPduv3OrSV6rV6iH7f7V/+Zd/yRlnnJFTTz013d3dueyyy/KDH/zgsNlKL730kj6kRDZs2JAzzzwz48ePT5LMnj07bW1tAjDAENbvAfi3v/1tPvaxj+WOO+7I8OHD8573vCcf/vCHU6lUct999+Wzn/1sPvOZz7zmHzuVSuWI7clr/4H0eqO/DQ0NmThx4ps8GgD6w6ZNm/KWt7ylZ7uvZ/JUKpVD9v9qO3fuzLhx43pec8YZZ2TDhg2Hvee44447rA8RiGtXR0fHIRfhx44dmw0bNhRYEQDHql+fA/zSSy/lYx/7WObMmZPLLrssSXLqqaemrq4uw4YNy7x58/LTn/40ycsju9u2bet5b0dHRxobGw9r37ZtW8/U6qampmzdujVJcuDAgXR1dWX06NH9eUgA1KA3c0GV2ue8AKg9/RaAq9Vq7rzzzpx11lmZP/93i0x0dnb2/Pz4449nwoQJSZKZM2emtbU1+/fvz+bNm9Pe3p5zzjknjY2NOemkk7J+/fpUq9UsX76855m2M2fOzPe+970kySOPPJKLL75YxwTAG3aki7D07VMdhhrnBUDt6bcp0GvXrs2KFSvyx3/8x7nqqquSJAsXLszKlSvzi1/8Ikkybty4LFmyJEkyYcKEXH755bniiitSV1eXu+66K3V1dUmSxYsXZ9GiRdm7d2+mT5+e6dOnJ0muvfba3HbbbWlpacmoUaNy77339tfhAFDDJk2alPb29mzevDljx45Na2tr/u7v/q7osgaFvnyqw1DjvACoPf0WgKdOnZpf/vKXh7XPmDHjiO9ZsGBBFixYcFj7pEmTsnLlysPaGxoa8oUvfOHYCgVg0Bk+4qSjWqX3jeyvN/X19bnrrrvywQ9+MN3d3bnmmmt6ZiiVXV891eGVpzm8WQN9TiTOC4BaNCCrQAPAG3E0z2ftazNmzOj1Ii3H9lSH3rzWExpevTL4u697d98eTNLryuCvuPDCC7N8+fIjvsfK4MBgtmfPniQWbPx9AjAA8LqO9akOvXmtJzS8emXwweq1VgYHGCxWrVqVJKX7nuot8PfrKtAAwNDXF091AIDBQAAGAI6or57qAACDgSnQAMAR9eVTHQCgaAIwAHBEfflUBwAominQAAAAlIIRYAAGnf/7q19J12+7+mx/I04akf/fDR/o9TWLFi3Kj370o4wZM+Y1nz1PsZwTAPQFARiAQafrt115+5+9rc/2t/EHv3jd11x99dV573vfm0984hN99rn0HecEAH3BFGgASHLBBRdk1KhRRZfBIOKcAKg9AjAAAAClIAADAABQCgIwAAAApSAAAwAAUApWgQZg0Blx0oijWqX3jezv9SxcuDCrV6/Ojh07Mn369Nx8882ZN29en9XAsXFOANAXBGAABp3Xez5rf/j85z8/4J/J0XNOANAXTIEGAACgFARgAAAASkEABmBQqFarRZfQq8FeXy0a7L/zwV4fAIcTgAEo3AknnJDt27cP2kBRrVazffv2nHDCCUWXUhrOCQD6g0WwACjcGWeckS1btuQ3v/lN0aUc0QknnJAzzjij6DJKwzkBQH8QgAEo3HHHHZc//MM/LLoMBhHnBAD9wRRoAAAASkEABgAAoBQEYAAAAEpBAAYAAKAUBGAAAABKQQAGAACgFARgAAAASkEABgAAoBQEYAAAAEpBAAYAAKAUBGAAAABKQQAGAACgFARgAAAASkEABgAAoBQEYAAAAEpBAAYAAKAUBGAAAABKQQAGAACgFARgAAAASkEABgAAoBQEYAAAAEpBAAYAAKAUBGAAAABKQQAGAACgFARgAAAASkEABgAAoBQEYAAAAEpBAAYAAKAUBGAAAABKQQAGAACgFARgAAAASkEABgAAoBQEYAAAAEpBAAYAAKAUBGAAAABKQQAGAACgFPotAG/dujXXX399Lr/88syePTsPPvhgkmTnzp2ZP39+LrvsssyfPz8vvPBCz3uWLl2alpaWzJo1K08++WRP+8aNGzNnzpy0tLTk05/+dKrVapJk//79ueWWW9LS0pJ58+Zly5Yt/XU4AAAADHH9FoDr6upy++235x//8R/z3//7f883v/nNPPPMM1m2bFmmTZuWRx99NNOmTcuyZcuSJM8880xaW1vT2tqa+++/P3fffXe6u7uTJIsXL86SJUvy6KOPpr29PatWrUqSfOc738nIkSPz2GOP5YYbbsjnPve5/jocAAAAhrh+C8CNjY05++yzkyTDhw/PWWedlY6OjrS1tWXu3LlJkrlz5+bxxx9PkrS1tWX27Nk5/vjjM378+Jx55pnZsGFDOjs7s3v37kyePDmVSiVz585NW1tbkuSHP/xh3vWudyVJZs2alZ/85Cc9o8MAAADw++oH4kO2bNmSTZs25dxzz8327dvT2NiY5OWQ/PzzzydJOjo6cu655/a8Z+zYseno6Eh9fX2ampp62puamtLR0dHzntNOO+3lA6mvz4gRI7Jjx46ccsopR6xl37592bRpU58fIwAAAINbvwfg3/72t/nYxz6WO+64I8OHDz/i615r5LZSqRyxvbf39KahoSETJ058vbJrxq5du/LQQw/luuuuy4gRI4ouB6DmuKgKAENHv64C/dJLL+VjH/tY5syZk8suuyxJMmbMmHR2diZJOjs7e0Zrm5qasm3btp73dnR0pLGx8bD2bdu29YwgNzU1ZevWrUmSAwcOpKurK6NHj+7PQxpy2tra0t7e3jNtHADeiL5c1BIAitZvAbharebOO+/MWWedlfnz5/e0z5w5M8uXL0+SLF++PM3NzT3tra2t2b9/fzZv3pz29vacc845aWxszEknnZT169enWq0e9p7vfe97SZJHHnkkF1988euOAJfJrl27snbt2lSr1axZsyZdXV1FlwTAENOXi1oCQNH6LQCvXbs2K1asyFNPPZWrrroqV111VZ544onceOON+fGPf5zLLrssP/7xj3PjjTcmSSZMmJDLL788V1xxRT74wQ/mrrvuSl1dXZKXV4H+5Cc/mZaWlvybf/NvMn369CTJtddem507d6alpSUPPPBA/vN//s/9dThDUltbW8808Wq1ahQYgDesrxa1BIDBoN/uAZ46dWp++ctfvua/vTJ96tUWLFiQBQsWHNY+adKkrFy58rD2hoaGfOELXzi2QmvY+vXre666d3d3Z926dT1/rADAG3Usi1r2xgKVAP1jz549SaxX8fsGZBVoinHeeedlzZo16e7uTl1dXSZPnlx0SQAMUce6qGVvyrZAJcBAWbVqVZKU7ju2t8Dfr4tgUazm5uaePzoqlUrPvdMA8Eb0xaKWADAYCMA1bOTIkZkyZUoqlUqmTp3qMUgAvGF9taglAAwGpkDXuObm5nR2dhr9BeBNeWVRyz/+4z/OVVddlSRZuHBhbrzxxtxyyy15+OGHc9ppp+W+++5LcuiilnV1dYcsagkARROAa9zIkSNz0003FV0GAENUXy5qCQBFMwW6xu3atStLly71DGAAAKD0BOAa19bWlvb2ds8ABgAASk8ArmG7du3K2rVrU61Ws2bNGqPAAABAqQnANaytra3neYzVatUoMAAAUGoCcA1bv359uru7kyTd3d1Zt25dwRUBAAAURwCuYeedd17Poyfq6uoyefLkgisCAAAojgBcw5qbm1OpVJIklUrFs4ABAIBSE4Br2MiRIzNlypRUKpVMnTo1I0aMKLokAACAwtQXXQD9q7m5OZ2dnUZ/AQCA0hOAa9zIkSNz0003FV0GAABA4UyBBgAAoBQEYAAAAEpBAAYAAKAUBGAAAABKwSJYAAC9WLt2bdasWdNn+9u9e3eSZPjw4X22z6lTp2bKlCl9tj+AWiUAAwAMoK6uriR9G4ABODoCMABAL6ZMmdKno6tLly5NEo8pBCiAe4ABAAAoBQEYAACAUhCAAQAAKAUBGAAAgFIQgAEAACgFARgAAIBSEIABAAAoBQEYAACAUhCAAQAAKAUBGAAAgFIQgAEAACgFARgAAIBSEIABAAAoBQEYAACAUhCAAQAAKAUBGAAAgFIQgAEAACgFARgAAIBSEIABAAAoBQEYAACAUhCAAQAAKAUBGAAAgFIQgAEAACgFARgAAIBSEIABAAAoBQEYAACAUhCAAQAAKAUBGAAAgFIQgAEAACgFARgAAIBSEIABAAAoBQEYAACAUhCAAQAAKAUBGAAAgFIQgAEAACgFARgAAIBS6LcAvGjRokybNi1XXnllT9sXv/jFvPOd78xVV12Vq666Kk888UTPvy1dujQtLS2ZNWtWnnzyyZ72jRs3Zs6cOWlpacmnP/3pVKvVJMn+/ftzyy23pKWlJfPmzcuWLVv661AAAACoAf0WgK+++urcf//9h7XfcMMNWbFiRVasWJEZM2YkSZ555pm0tramtbU1999/f+6+++50d3cnSRYvXpwlS5bk0UcfTXt7e1atWpUk+c53vpORI0fmscceyw033JDPfe5z/XUoAAAA1IB+C8AXXHBBRo0adVSvbWtry+zZs3P88cdn/PjxOfPMM7Nhw4Z0dnZm9+7dmTx5ciqVSubOnZu2trYkyQ9/+MO8613vSpLMmjUrP/nJT3pGhwEAAODVBvwe4G984xuZM2dOFi1alBdeeCFJ0tHRkaampp7XjB07Nh0dHYe1NzU1paOjo+c9p512WpKkvr4+I0aMyI4dOwbwSAAAABhK6gfyw97znvfkwx/+cCqVSu6777589rOfzWc+85nXHLmtVCpHbE/S67/1Zt++fdm0adObqB4AymfRokX50Y9+lDFjxmTlypVJXl7T49vf/nZOOeWUJMnChQt7bmtaunRpHn744QwbNiyf/OQn8853vrOw2gHg1QY0AJ966qk9P8+bNy//8T/+xyQvj+xu27at5986OjrS2Nh4WPu2bdvS2NjY856tW7emqakpBw4cSFdXV0aPHv26NTQ0NGTixIl9dUgAlFytX1S9+uqr8973vjef+MQnDmm/4YYb8oEPfOCQtt9f06OjoyPz58/PI488krq6uoEsGQCOaECnQHd2dvb8/Pjjj2fChAlJkpkzZ6a1tTX79+/P5s2b097ennPOOSeNjY056aSTsn79+lSr1SxfvjzNzc097/ne976XJHnkkUdy8cUXH9UIMABw9PpiTQ8AGCz6bQR44cKFWb16dXbs2JHp06fn5ptvzurVq/OLX/wiSTJu3LgsWbIkSTJhwoRcfvnlueKKK1JXV5e77rqr52rx4sWLs2jRouzduzfTp0/P9OnTkyTXXnttbrvttrS0tGTUqFG59957++tQAIBX+cY3vpHly5fn7W9/e26//faMGjUqHR0dOffcc3te88qaHq+nbLcn7dmzJ0ntzx4Aiuf75nD9FoA///nPH9Y2b968I75+wYIFWbBgwWHtkyZN6rnn6Pc1NDTkC1/4wrEVCQC8YW90TY/XU7bbk155pGOZjhkoRlm/b3oL/AO+CjQAMLSdeuqpqaury7BhwzJv3rz89Kc/TXLkNT0AYLAQgAGAN+SNrukBAIPFgK4CDQAMLX21pgcADAYCMABwRH21pgcADAamQAMAAFAKAjAAAAClIAADAABQCq8bgN///vcfVRsAMHjpzwGgl0Ww9u3blxdffDE7duzICy+80PNw+927dx/y+AMAYPDSnwPA7xwxAH/rW9/Kgw8+mM7Ozlx99dU9Hebw4cPzH/7DfxiwAgGAN09/DgC/c8QA/P73vz/vf//787WvfS3XX3/9QNYEAPQR/TkA/M7rPgf4+uuvz9NPP51nn3023d3dPe1z587t18IAgL6jPweAowjAt912WzZv3py3ve1tqaurS5JUKhUdJgAMIfpzADiKALxx48b8z//5P1OpVAaiHgCgH+jPAeAoHoM0YcKE/OY3vxmIWgCAfqI/B4CjGAHesWNHZs+enXPOOSfHHXdcT/uXv/zlfi0MAOg7+nMAOIoAfPPNNw9EHQBAP9KfA8BRBOALL7xwIOoAAPqR/hwAjiIAT548uWfBjJdeeikHDhzIW97yljz99NP9XhwA0Df05wBwFAF43bp1h2w//vjj2bBhQ78VRN/atWtXHnrooVx33XUZMWJE0eUAUBD9OQAcxSrQr3bppZfmqaee6o9a6AdtbW1pb29PW1tb0aUAMIjozwEoo9cdAX700Ud7fj548GA2btzoGYJDxK5du7J27dpUq9WsWbMmzc3NRoEBSkp/DgBHEYD/6Z/+qefnurq6jBs3Lv/lv/yXfi2KvtHW1pZqtZokqVaraWtry9y5cwuuCoAi6M8B4CgC8Gc+85mBqIN+sH79+nR3dydJuru7s27dOgEYoKT05wBwFPcAb9u2LR/5yEcybdq0/Mmf/EluvvnmbNu2bSBq4xidd955qaurS/Ly1f7JkycXXBEARdGfA8BRBOBFixZl5syZefLJJ7Nq1ar86Z/+aRYtWjQQtXGMmpube+7vqlQqaW5uLrgiAIqiPweAowjAzz//fK655prU19envr4+V199dZ5//vmBqI1jNHLkyEyZMiWVSiVTp061ABZAienPAeAoAvDJJ5+cFStWpLu7O93d3VmxYkVGjx49ELXRB5qbm/PWt77V6C9AyenPAeAoAvA999yTf/zHf8w73vGOXHLJJXnkkUdyzz33DERt9IGRI0fmpptuMvoLUHL6cwA4ilWgTz/99Hz5y18eiFoAgH6iPweAowjAmzdvzte//vU8++yzOXDgQE+7ThQAhg79OQAcRQD+yEc+kmuvvTZ/+qd/mmHDXnfGNAAwCOnPAeAoAnBDQ0Pe9773DUQt9INdu3bloYceynXXXec+YIAS058DwFEE4Pe973350pe+lHe84x05/vjje9rPPvvsfi2MvtHW1pb29va0tbVl7ty5RZcDQEH05wBwFAH4f//v/50VK1bkqaeeSqVSSZJUKpX8t//23/q9OI7Nrl27smbNmlSr1fzzP/9zmpubjQIDlJT+HACOIgA/9thjefzxxw+5WszQ0NbWlu7u7iRJd3e3UWCAEtOfA8BRPAf4bW97W7q6ugaiFvrYunXrDtl++umnC6oEgKLpzwHgKEaAt2/fnssvvzyTJk3Kcccdl+TlKVP/9b/+134vjmMzevTodHZ29myffPLJBVYDQJH05wBwFAH45ptv7vm5Wq1m7dq1aW1t7dei6Bs7d+48ZHvHjh0FVQJA0fTnAHAUAfjCCy/Mpk2b8v3vfz8/+MEPMm7cuLz73e8eiNo4RpMnT87q1atTrVZTqVRy/vnnF10SAAXRnwNALwH417/+dVpbW9Pa2prRo0fniiuuSLVazde+9rWBrI9j0NzcnLVr1+bAgQOpq6tLc3Nz0SUBMMD05wDwO0cMwJdffnmmTp2aL3/5yznzzDOTJF/96lcHqi76wMiRIzNlypSsXr06U6dO9QgkgBLSnwPA7xxxFegvfvGLOfXUU/O+970vn/zkJ/OTn/wk1Wp1IGujDzQ3N+etb32r0V+AktKfA8DvHHEEuKWlJS0tLdmzZ08ef/zxfPWrX8327dvzqU99Ki0tLbnkkksGsk7epJEjR+amm24qugwACqI/B4Dfed3nAJ944on59//+32fp0qV54oknMnHixCxbtmwgagMA+oj+HACOYhXo3zd69Oi8+93vtmokAAxh+nMAyup1R4ABAACgFgjAAAAAlIIADAAAQCkIwAAAAJSCAAwAAEApCMAAAACUggAMAABAKQjAAAAAlIIADAAAQCkIwDVu165dWbp0abq6uoouBQAAoFD1RRdA/2pra0t7e3va2toyd+7cossBAICa8f3vfz/PPfdc0WUc0datW5MkS5cuLbiSIzv99NMzZ86cAfs8AbiG7dq1K2vXrk21Ws2aNWvS3NycESNGFF0WAADUhOeeey6bn/3/Z8SY4UWX8pqGNVSSJDv3Pl9wJa+ta/vuAf9MAbiGtbW1pVqtJkmq1apRYAAA6GMjxgzP1CsnF13GkLRm5boB/8x+uwd40aJFmTZtWq688sqetp07d2b+/Pm57LLLMn/+/Lzwwgs9/7Z06dK0tLRk1qxZefLJJ3vaN27cmDlz5qSlpSWf/vSnewLd/v37c8stt6SlpSXz5s3Lli1b+utQhqz169enu7s7SdLd3Z116wb+BAMAABgs+i0AX3311bn//vsPaVu2bFmmTZuWRx99NNOmTcuyZcuSJM8880xaW1vT2tqa+++/P3fffXdPcFu8eHGWLFmSRx99NO3t7Vm1alWS5Dvf+U5GjhyZxx57LDfccEM+97nP9dehDFlnn332Idtvf/vbC6oEAACgeP0WgC+44IKMGjXqkLbfn4I7d+7cPP744z3ts2fPzvHHH5/x48fnzDPPzIYNG9LZ2Zndu3dn8uTJqVQqmQhEV/QAABTTSURBVDt3btra2pIkP/zhD/Oud70rSTJr1qz85Cc/6Rkd5mWv/n34/QDwRvXVjC4AGAwG9B7g7du3p7GxMUnS2NiY559/+Wbsjo6OnHvuuT2vGzt2bDo6OlJfX5+mpqae9qampnR0dPS857TTTkuS1NfXZ8SIEdmxY0dOOeWUXmvYt29fNm3a1KfHNVht3LjxkO2f/vSnmTRpUkHVADAUXX311Xnve9+bT3ziEz1tr8zouvHGG7Ns2bIsW7Yst9122yEzujo6OjJ//vw88sgjqaurK/AIAOB3BsUiWK81MlmpVI7Y3tt7Xk9DQ0MmTpz4Jqoces4///z88z//cw4ePJhhw4ZlypQppTl2gIFS6xdVL7jggsPW2Whra8vXvva1JC/P6Lr++utz2223HXFG1+TJFocBYHAY0AA8ZsyYdHZ2prGxMZ2dnT2jtU1NTdm2bVvP6zo6OtLY2HhY+7Zt23pGkJuamrJ169Y0NTXlwIED6erqyujRowfycAa95ubmrF27ticANzc3F10SADXgjc7oej1lmp2VJHv27ElS+xdPoAz27NnTjzeVlsOePXsG9PtwQAPwzJkzs3z58tx4441Zvnx5TyCbOXNm/tN/+k+ZP39+Ojo60t7ennPOOSd1dXU56aSTsn79+px77rlZvnx5rr/++p73fO9738vkyZPzyCOP5OKLLz6qEeAyGTlyZKZMmZLVq1dn6tSpngEMQL8yO+vovLKgZ5mOGWrVqlWrsn/v3qLLGNJOPPHEPv8+7C1Q91sAXrhwYVavXp0dO3Zk+vTpufnmm3PjjTfmlltuycMPP5zTTjst9913X5JkwoQJufzyy3PFFVekrq4ud911V8/9QosXL86iRYuyd+/eTJ8+PdOnT0+SXHvttbntttvS0tKSUaNG5d577+2vQxnSmpub09nZafQXgD7zRmd0AcBg0W8B+POf//xrtj/44IOv2b5gwYIsWLDgsPZJkyZl5cqVh7U3NDTkC1/4wrEVWQIjR47MTTfdVHQZANSQNzqjCwAGi0GxCBYAMDj11YwuABgMBGAA4Ij6akYXAAwG1iwDAACgFARgAAAASkEABgAAoBQEYAAAAEpBAK5xzz33XD71qU9l69atRZcCAABQKAG4xn3rW9/Kvn378q1vfavoUgAAAAolANew5557Lp2dnUmSjo4Oo8AAAECpCcA17NWjvkaBAQCAMhOAa9gro7+v6OjoKKgSAACA4gnANayxsfGQ7bFjxxZUCQAAQPEE4Br27ne/u9dtAACAMhGAa9jpp5+eE044IUnylre8JaeddlrBFQEAABRHAK5hu3btyv79+5Mk+/btS1dXV8EVAQAAFEcArmFtbW05ePBgkuTgwYNpa2sruCIAAIDiCMA1bN26dYdsP/300wVVAgAAUDwBuIaNHj36kO2TTz65oEoAAACKJwDXsJ07dx6yvWPHjoIqAQAAKJ4AXMMmT558yPb5559fUCUAAADFE4Br2EUXXdTrNgAAQJkIwDXsySef7HUbAACgTATgGvYv//Ivh2yvX7++oEoAAACKJwDXsGq12us2AABAmQjANUwABgAA+B0BGAAAgFKoL7oAAIC+9P3vfz/PPfdc0WUc0datW5MkS5cuLbiSIzv99NMzZ86cossA6HMCMABQU5577rls2bwlo4efXHQpr+m4YcclSXbv+G3Blby2nbt3FF0CQL8RgAGAmjN6+Mn5d+dfVnQZQ9KPnn606BIA+o17gGvYsGHDet0GAAAoE4mohp133nmHbE+ePLmgSgAAAIonANewP/uzP+t1GwAAoEwE4Bq2e/fuXrcBAADKRACuYd/85jcP2f7GN75RUCUAAADFE4Br2L/+67/2ug0AAFAmAjAAAAClIAADAABQCgJwDatUKr1uAwAAlIkAXMOOP/74Q7YbGhoKqgQAAKB4AnAN27dv3yHbe/fuLagSAACA4gnANezVI74nnHBCQZUAAAAUTwCuYfv37z9k+9UjwgAAAGUiANcwi2ABAAD8jgBcw0455ZRetwEAAMpEAK5hL7zwQq/bAAAAZSIA17CTTz75kG0jwAAAQJkJwDXs+eefP2R7+/btBVUCAABQPAG4hg0bduh/b11dXUGVAAAAFE8ArmEegwQAAPA7AjAAAAClIADXsBNOOKHXbQAAgDIRgGvY3r17e90GAAAoEwEYAACAUhCAAQAAKAUBuIa9+p7ft7zlLQVVAgAAUDwBuIa9+p7fF198saBKAAAAiicAAwAAUAoCMAAAAKVQX8SHzpw5MyeddFKGDRuWurq6fPe7383OnTtz66235tlnn824cePy93//9xk1alSSZOnSpXn44YczbNiwfPKTn8w73/nOJMnGjRuzaNGi7N27NzNmzMidd96ZSqVSxCEBAAAwyBU2Avzggw9mxYoV+e53v5skWbZsWaZNm5ZHH30006ZNy7Jly5IkzzzzTFpbW9Pa2pr7778/d999d7q7u5MkixcvzpIlS/Loo4+mvb09q1atKupwAKB0Zs6cmTlz5uSqq67K1VdfnSTZuXNn5s+fn8suuyzz58/PCy+8UHCVAPA7g2YKdFtbW+bOnZskmTt3bh5//PGe9tmzZ+f444/P+PHjc+aZZ2bDhg3p7OzM7t27M3ny5FQqlcydOzdtbW1FHgIAlM7RXtAGgMGgkCnQSfKBD3wglUolf/EXf5G/+Iu/yPbt29PY2JgkaWxszPPPP58k6ejoyLnnntvzvrFjx6ajoyP19fVpamrqaW9qakpHR8frfu6+ffuyadOmPj6aoaPMxw5A/2tra8vXvva1JC9f0L7++utz2223FVwVALyskAD80EMPZezYsdm+fXvmz5+fs84664ivrVarh7VVKpUjtr+ehoaGTJw48Y0VXEPKfOwA/aHsFxaP9oJ2b/r64vSePXv6bF9ltWfPntKf23A09uzZM4jm1A5NA/19U0gAHjt2bJJkzJgxaWlpyYYNGzJmzJh0dnamsbExnZ2dOeWUU5K8PLK7bdu2nvd2dHSksbHxsPZt27b1dLgAQP97Ixe0e9PXF6dXrVqV3ft+22f7K6MTTzzRRXM4CqtWrcr+vXuLLmNI64/vm94C9YBfr9izZ092797d8/OPf/zjTJgwITNnzszy5cuTJMuXL09zc3OSlxfYaG1tzf79+7N58+a0t7fnnHPOSWNjY0466aSsX78+1Wr1kPcAAP2vtwvaSQ65oA0Ag8GAjwBv3749H/nIR5Ik3d3dufLKKzN9+vRMmjQpt9xySx5++OGcdtppue+++5IkEyZMyOWXX54rrrgidXV1ueuuu1JXV5fk5VWgX3kM0vTp0zN9+vSBPhwAKKU9e/bk4MGDGT58eM8F7Q9/+MM9F7RvvPFGF6cBGHQGPACPHz8+/+N//I/D2k8++eQ8+OCDr/meBQsWZMGCBYe1T5o0KStXruzzGgGA3r3RC9oAMBgUtgo0ADB0vZkL2gBQNGuWAQAAUAoCMAAAAKUgAAMAAFAKAjAAAAClIAADAABQCgIwAAAApSAAAwAAUAoCMAAAAKUgAAMAAFAKAjAAAAClIAADAABQCgIwAAAApSAAAwAAUAoCMAAAAKUgAAMAAFAKAjAAAAClIAADAABQCgIwAAAApSAAAwAAUAoCMAAAAKUgAAMAAFAKAjAAAAClIAADAABQCgIwAAAApSAAAwAAUAoCMAAAAKUgAAMAAFAKAjAAAAClIAAD0Cd27dqVpUuXpqurq+hSAABekwAMQJ9oa2tLe3t72traii4FAOA11RddAABD365du7J27dpUq9WsWbMmzc3NGTFiRNFlUVJdXV15oeuF/OjpR4suZUja2bUj1fqDRZcB0C+MAANwzNra2lKtVpMk1WrVKDAAMCgZAQbgmK1fvz7d3d1Jku7u7qxbty5z584tuCrKasSIEakcGJZ/d/5lRZcyJP3o6UczfMRJRZcB0C+MAANwzM4777xUKpUkSaVSyeTJkwuuCADgcAIwAMfsoosuOmQK9EUXXVRwRQAAhxOAAThm/+t//a9etwEABgMBGIBjtn79+kO2161bV1AlAABHJgADcMzOO++8Q7bdAwwADEYCMADH7NX3/LoHGAAYjARgAI6Ze4ABgKFAAAbgmD399NOHbK9du7agSgAAjkwABuCYdXd3H7J98ODBgioBADgyARiAY/bqwPvqQAwAMBgIwAAAAJSCAAwAAEApCMAAHLNTTjml120AgMFAAAbgmO3cubPXbQCAwaC+6AIAGPpevQiWVaABKIOurq507dqdNSvXFV3KkNS1fXfqRh43oJ9pBBgAAIBSMAIMAADwJowYMSLdx72UqVdOLrqUIWnNynUZccKIAf1MI8AAAACUggAMAABAKZgCDQDUnJ27d+RHTz9adBmvae/+F5MkJxz/loIreW07d+/I8JNPKroMgH4hAAMANeX0008vuoRedW3dlSQ59eRTC67ktQ0/+aRB/zsEeLMEYACgpsyZM6foEnq1dOnSJMlNN91UcCUA5eMeYAAAAErBCDBASa1duzZr1qzpt/2/Msp1LKZOnZopU6b0QTUAADUQgFetWpW/+Zu/ycGDBzNv3rzceOONRZd0TPxBCsBQV2t9MwC1Y0gH4O7u7ixZsiQPPPBAxo4dm2uvvTYzZ87MH/3RHw1YDd///vfz3HPP9dn+urq6snv37j7b36tt3br1mPfxT//0T30a0k8//fRBf78WDAZ9/X0zFKxZs8b3zRAzGPpmgIHUtX131qxcV3QZr2nfnv1JkoYTjy+4ktfWtX13Ro87ZUA/c0gH4A0bNuTMM8/M+PHjkySzZ89OW1vbgHayv/zlL/Ov//qvA/Z5x2rv3r19so++POauri5/kMJRGGrfN7/+9a+LLuEwvm/632DomwEGymBfMX3rjpcHv0afMrAh82iNHnfKgP8Oh3QA7ujoSFNTU8/22LFjs2HDhgGt4eSTT+7TEdsDBw6ku7u7T/ZVrVYPa6tUKse837q6utTX992pc/LJJ/fZvqCWDebvm+TQ75y++K5JfN8MRYOhb+5rfX170iuzsfritqRXuD0JitHXF1X7+3bIvjDUv2+GdAB+MwFv37592bRpU5/V8Cd/8id9tq8k+T//5//kV7/6VZ/tr6Ojo+fnsWPH9sk+J0yYkH/7b/9tn+zrFX35fwK1arB/3+zYsSP79+/PiSeemBEjRvTJPn3fDD2DoW/ua1u3bs2ePXv6bH/HH//yVMS+3OfWrVsH9e8QODq+b/rfkA7ATU1N2bZtW892R0dHGhsbe31PQ0NDJk6c2N+lvWl9Xdvtt9/e8/Ott97ap/sGhrbB/F04lAzlPwL6g74Z4M3zfdM3euubh3QAnjRpUtrb27N58+aMHTs2ra2t+bu/+7uiyxpUPvvZzxZdAgAlom8GYDAb0gG4vr4+d911Vz74wQ+mu7s711xzTSZMmFB0WQBQWvpmAAazIR2Ak2TGjBmZMWNG0WUAAP8ffTMAg9WwogsAAACAgSAAAwAAUAoCMAAAAKUgAAMAAFAKAjAAAAClIAADAABQCgIwAAAApSAAAwAAUAoCMAAAAKUgAAMAAFAKAjAAAAClIAADAABQCvVFFzDQ9u3bl02bNhVdBgA1Yt++fUWXMOTpmwHoS731zZVqtVodwFoAAACgEKZAAwAAUAoCMAAAAKUgAAMAAFAKAjAAAAClIAADAABQCgIwAAAApSAAU6hVq1Zl1qxZaWlpybJly4ouBwalRYsWZdq0abnyyiuLLgUoAX0zvD5989AlAFOY7u7uLFmyJPfff39aW1uzcuXKPPPMM0WXBYPO1Vdfnfvvv7/oMoAS0DfD0dE3D10CMIXZsGFDzjzzzIwfPz7HH398Zs+enba2tqLLgkHnggsuyKhRo4ouAygBfTMcHX3z0CUAU5iOjo40NTX1bI8dOzYdHR0FVgQA5aZvBmqdAExhqtXqYW2VSqWASgCARN8M1D4BmMI0NTVl27ZtPdsdHR1pbGwssCIAKDd9M1DrBGAKM2nSpLS3t2fz5s3Zv39/WltbM3PmzKLLAoDS0jcDta5Sfa25LjBAnnjiidxzzz3p7u7ONddckwULFhRdEgw6CxcuzOrVq7Njx46MGTMmN998c+bNm1d0WUCN0jfD69M3D10CMAAAAKVgCjQAAAClIAADAABQCgIwAAAApSAAAwAAUAoCMAAAAKUgAENJ/OY3v8mtt96aSy+9NFdccUU+9KEP5de//nWuvPLKoksDgFLSN8PAqy+6AKD/VavVfPSjH83cuXNz7733Jkk2bdqU7du3F1wZAJSTvhmKIQBDCTz11FOpr6/Pe97znp62iRMnZsuWLT3bW7Zsycc//vG8+OKLSZK/+qu/yvnnn5/Ozs7ceuut2b17d7q7u7N48eJMnjw5d955ZzZu3JhKpZJrrrkmN9xww0AfFgAMWfpmKIYADCXwq1/9KmeffXavrxkzZkweeOCBNDQ0pL29PQsXLsx3v/vdrFy5MpdcckkWLFiQ7u7uvPjii9m0aVM6OjqycuXKJMmuXbsG4jAAoGbom6EYAjCQJDlw4ECWLFmSX/ziFxk2bFja29uTJJMmTcodd9yRAwcO5NJLL83EiRMzfvz4bN68OX/913+dGTNm5JJLLim2eACoQfpm6HsWwYISmDBhQn72s5/1+pqvfvWrOfXUU7NixYr8wz/8Q1566aUkyQUXXJCvf/3rGTt2bD7+8Y9n+fLlGTVqVFasWJELL7ww3/zmN3PnnXcOxGEAQM3QN0MxBGAogYsvvjj79+/Pt7/97Z62DRs25LnnnuvZ7urqyh/8wR9k2LBhWbFiRbq7u5Mkzz77bMaMGZM///M/zzXXXJOf/exnef7551OtVjNr1qz85V/+ZX7+858P+DEBwFCmb4ZimAINJVCpVPKlL30p99xzT5YtW5aGhoaMGzcud9xxR89rrrvuutx88835wQ9+kIsuuignnnhikmT16tX5yle+kvr6+px44on527/923R2dmbRokU5ePBgkmThwoWFHBcADFX6ZihGpVqtVosuAgAAAPqbKdAAAACUggAMAABAKQjAAAAAlIIADAAAQCkIwAAAAJSCAAwAAEApCMAAAACUwv8LcrPq+P3hfEkAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Density Plots of all Features"},{"metadata":{"trusted":true},"cell_type":"code","source":"# cols plot \ncols_names = df.drop(['Class', 'Amount', 'Time'], axis=1)\nidx = 0\n\n# Spliting classes\nfraud = df[df['Class']==1]\nnormal = df[df['Class']==0]\n\n# figure plot \nfig, ax = plt.subplots(nrows=7, ncols=4, figsize=(18,18))\nfig.subplots_adjust(hspace=1, wspace=1)\n\nfor col in cols_names:\n idx += 1\n plt.subplot(7, 4, idx)\n sns.kdeplot(fraud[col], label=\"Normal\", color='blue', shade=True)\n sns.kdeplot(normal[col], label=\"Fraud\", color='orange', shade=True)\n plt.title(col, fontsize=11)\n plt.tight_layout()","execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1296x1296 with 28 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Correlation Matrix"},{"metadata":{"trusted":true},"cell_type":"code","source":"f, (ax1, ax2) = plt.subplots(1,2,figsize =( 18, 8))\ncorr = df.corr()\nmask = np.zeros_like(corr, dtype=np.bool)\nmask[np.triu_indices_from(mask)] = True\nsns.heatmap((df.loc[df['Class'] ==1]).corr(), vmax = .8, square=True, ax = ax1, cmap = 'afmhot', mask=mask);\nax1.set_title('Fraud')\nsns.heatmap((df.loc[df['Class'] ==0]).corr(), vmax = .8, square=True, ax = ax2, cmap = 'YlGnBu', mask=mask);\nax2.set_title('Normal')\nplt.show()","execution_count":17,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1296x576 with 4 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Scaling and Splitting the data"},{"metadata":{"trusted":true},"cell_type":"code","source":"rob_scaler = RobustScaler()\n\ndf['scaled_amount'] = rob_scaler.fit_transform(df['Amount'].values.reshape(-1,1))\ndf['scaled_time'] = rob_scaler.fit_transform(df['Time'].values.reshape(-1,1))\n\ndf.drop(['Time', 'Amount'], axis=1, inplace=True)\n\nX = df.drop('Class', axis=1)\ny = df['Class']\n\ntarget = ['Class']\nfeat_cols = ['scaled_time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19',\n 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'scaled_amount']","execution_count":3,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sss = StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=42)\n\nfor train_index, test_index in sss.split(X, y):\n X_train_full, X_test = X.iloc[train_index], X.iloc[test_index]\n y_train_full, y_test = y.iloc[train_index], y.iloc[test_index]\n\nsss2 = StratifiedShuffleSplit(n_splits=5, test_size=0.3, random_state=42)\nfor train_idx, test_idx in sss2.split(X_train_full, y_train_full):\n X_train, X_valid = X_train_full.iloc[train_idx], X_train_full.iloc[test_idx]\n y_train, y_valid = y_train_full.iloc[train_idx], y_train_full.iloc[test_idx]","execution_count":4,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('X_train shape: ', X_train.shape)\nprint('y_train shape: ', y_train.shape)\nprint('X_test shape: ', X_test.shape)\nprint('y_test shape: ', y_test.shape)","execution_count":20,"outputs":[{"output_type":"stream","text":"X_train shape: (159491, 30)\ny_train shape: (159491,)\nX_test shape: (56962, 30)\ny_test shape: (56962,)\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"Lets try some models and see how they can predict the frauds."},{"metadata":{},"cell_type":"markdown","source":"We should not be happy for high accuracy of test set. If the precision/recall score is low then there is grave problem in the model.\n\n1. Low precision and high recall means the model detects most of the features as fraud including the genuine transactions.\n2. High precision and low recall means the model precisely detects the non-fruads but labels the frauds as genuine too.\n\nBy considering these issues, We have to build a model that can have highly balanced precision-recall/ f1 score, for that we have to check beyond accuracy score."},{"metadata":{},"cell_type":"markdown","source":"## Naive Bayes"},{"metadata":{"trusted":true},"cell_type":"code","source":"## Naive Bayes\n\nModel = GaussianNB()\nModel.fit(X_train[feat_cols], y_train)\ny_pred = Model.predict(X_test[feat_cols])\n\n# Summary of the predictions made by the classifier\nprint(classification_report(y_test, y_pred))\nprint(confusion_matrix(y_test, y_pred))\n# Accuracy score\n\nprint('accuracy is',accuracy_score(y_pred,y_test))","execution_count":21,"outputs":[{"output_type":"stream","text":" precision recall f1-score support\n\n 0 1.00 0.98 0.99 56864\n 1 0.06 0.81 0.11 98\n\n accuracy 0.98 56962\n macro avg 0.53 0.89 0.55 56962\nweighted avg 1.00 0.98 0.99 56962\n\n[[55578 1286]\n [ 19 79]]\naccuracy is 0.9770899898177733\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## LightGBM"},{"metadata":{"trusted":true},"cell_type":"code","source":"from scipy.stats import randint as sp_randint\nfrom scipy.stats import uniform as sp_uniform\n\nfit_params = {\"early_stopping_rounds\" : 50, \n \"eval_metric\" : 'binary', \n \"eval_set\" : [(X_test[feat_cols].values, y_test)],\n 'eval_names': ['valid'],\n 'verbose': 0,\n 'categorical_feature': 'auto'}\n\nparam_test = {'learning_rate' : [0.001, 0.005, 0.03, 0.05, 0.1],\n 'n_estimators' : [100, 300, 500, 800, 1000],\n 'num_leaves': sp_randint(6, 50), \n 'min_child_samples': sp_randint(100, 500), \n 'min_child_weight': [1e-5, 1e-3, 1e-2, 1e-1, 1, 1e2],\n 'subsample': sp_uniform(loc=0.2, scale=0.8), \n 'max_depth': [-1, 1, 2, 3, 4, 5, 6, 7],\n 'colsample_bytree': sp_uniform(loc=0.4, scale=0.6),\n 'reg_alpha': [0, 1e-1, 1, 5, 7, 10],\n 'reg_lambda': [0, 1e-1, 1, 5, 10]}\n\n#number of combinations\nn_iter = 5 #(replace 2 by 200, 90 minutes)\n\n#intialize lgbm and lunch the search\nlgbm_clf = lgb.LGBMClassifier(random_state=42, silent=True, metric='None', n_jobs=4)\ngrid_search = RandomizedSearchCV(\n estimator=lgbm_clf, param_distributions=param_test, \n n_iter=n_iter,\n scoring='accuracy',\n cv=4, n_jobs=-1,\n refit=True,\n random_state=42,\n verbose=True)\n\ngrid_search.fit(X_train[feat_cols].values, y_train, **fit_params)\nprint('Best score reached: {} with params: {} '.format(grid_search.best_score_, grid_search.best_params_))\n\nopt_parameters = grid_search.best_params_\n\nclf_sw = lgb.LGBMClassifier(**lgbm_clf.get_params())\n#Optimal parameter\nclf_sw.set_params(**opt_parameters)","execution_count":22,"outputs":[{"output_type":"stream","text":"Fitting 4 folds for each of 5 candidates, totalling 20 fits\n","name":"stdout"},{"output_type":"stream","text":"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n[Parallel(n_jobs=-1)]: Done 20 out of 20 | elapsed: 1.1min finished\n","name":"stderr"},{"output_type":"stream","text":"Best score reached: 0.9995799138752799 with params: {'colsample_bytree': 0.6247240713084175, 'learning_rate': 0.1, 'max_depth': 6, 'min_child_samples': 206, 'min_child_weight': 1, 'n_estimators': 1000, 'num_leaves': 44, 'reg_alpha': 0.1, 'reg_lambda': 1, 'subsample': 0.27997993265440235} \n","name":"stdout"},{"output_type":"execute_result","execution_count":22,"data":{"text/plain":"LGBMClassifier(colsample_bytree=0.6247240713084175, max_depth=6, metric='None',\n min_child_samples=206, min_child_weight=1, n_estimators=1000,\n n_jobs=4, num_leaves=44, random_state=42, reg_alpha=0.1,\n reg_lambda=1, subsample=0.27997993265440235)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"lgbm_clf = lgb.LGBMClassifier(colsample_bytree=0.6247240713084175, max_depth=6, metric='None',\n min_child_samples=206, min_child_weight=1, n_estimators=1000,\n n_jobs=4, num_leaves=44, random_state=42, reg_alpha=0.1,\n reg_lambda=1, subsample=0.27997993265440235)\n\nlgbm_clf.fit(X_train, y_train)\nlgbm_clf.fit(X_train, y_train)\ny_pred = lgbm_clf.predict(X_test)\ny_score = lgbm_clf.predict_proba(X_test)[:,1]\nprint(classification_report(y_test,y_pred.round()))","execution_count":23,"outputs":[{"output_type":"stream","text":" precision recall f1-score support\n\n 0 1.00 1.00 1.00 56864\n 1 0.94 0.78 0.85 98\n\n accuracy 1.00 56962\n macro avg 0.97 0.89 0.92 56962\nweighted avg 1.00 1.00 1.00 56962\n\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## Random Forest"},{"metadata":{"trusted":true},"cell_type":"code","source":"\nclf = RandomForestClassifier(max_depth=5, min_samples_split=5,min_samples_leaf=4, random_state=42)\n \nclf.fit(X_train, y_train)\n\ny_pred = clf.predict(X_test)\n# Summary of the predictions made by the classifier\nprint(classification_report(y_test, y_pred))\nprint(confusion_matrix(y_test, y_pred))\n# Accuracy score\n\nprint('accuracy is',accuracy_score(y_pred,y_test))","execution_count":27,"outputs":[{"output_type":"stream","text":" precision recall f1-score support\n\n 0 1.00 1.00 1.00 56864\n 1 0.89 0.65 0.75 98\n\n accuracy 1.00 56962\n macro avg 0.94 0.83 0.88 56962\nweighted avg 1.00 1.00 1.00 56962\n\n[[56856 8]\n [ 34 64]]\naccuracy is 0.9992626663389628\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## Catboost"},{"metadata":{"trusted":true,"collapsed":true},"cell_type":"code","source":"Model=CatBoostClassifier(eval_metric='Accuracy',use_best_model=True,random_seed=42,task_type='GPU')\nModel.fit(X_train[feat_cols],y_train,eval_set=(X_test[feat_cols],y_test))","execution_count":19,"outputs":[{"output_type":"stream","text":"Learning rate set to 0.046269\n0:\tlearn: 0.9992351\ttest: 0.9989818\tbest: 0.9989818 (0)\ttotal: 10.4ms\tremaining: 10.4s\n1:\tlearn: 0.9992790\ttest: 0.9991047\tbest: 0.9991047 (1)\ttotal: 19.8ms\tremaining: 9.87s\n2:\tlearn: 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(300)\ttotal: 9.61s\tremaining: 19.3ms\n998:\tlearn: 0.9999937\ttest: 0.9994909\tbest: 0.9995084 (300)\ttotal: 9.62s\tremaining: 9.63ms\n999:\tlearn: 0.9999937\ttest: 0.9994909\tbest: 0.9995084 (300)\ttotal: 9.63s\tremaining: 0us\nbestTest = 0.9995084442\nbestIteration = 300\nShrink model to first 301 iterations.\n","name":"stdout"},{"output_type":"execute_result","execution_count":19,"data":{"text/plain":"<catboost.core.CatBoostClassifier at 0x7f97e4078150>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"## CatBoost\ny_pred=Model.predict(X_test[feat_cols])\nprint(classification_report(y_test,y_pred))\nprint(confusion_matrix(y_pred,y_test))\n#Accuracy Score\nprint('accuracy is ',accuracy_score(y_pred,y_test))","execution_count":20,"outputs":[{"output_type":"stream","text":" precision recall f1-score support\n\n 0 1.00 1.00 1.00 56864\n 1 0.96 0.74 0.84 98\n\n accuracy 1.00 56962\n macro avg 0.98 0.87 0.92 56962\nweighted avg 1.00 1.00 1.00 56962\n\n[[56861 25]\n [ 3 73]]\naccuracy is 0.9995084442259752\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## Feature Importance"},{"metadata":{"trusted":true},"cell_type":"code","source":"tmp = pd.DataFrame({'Feature': feat_cols, 'Feature importance': Model.feature_importances_})\ntmp = tmp.sort_values(by='Feature importance',ascending=False)\nplt.figure(figsize = (10,6))\nplt.title('Features importance',fontsize=14)\ns = sns.barplot(x='Feature',y='Feature importance',data=tmp)\ns.set_xticklabels(s.get_xticklabels(),rotation=90)\nplt.show()","execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 720x432 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"## XGBoost"},{"metadata":{"trusted":true},"cell_type":"code","source":"\nparams = {\n 'learning_rate': [0.001, 0.01, 0.05, 0.1],\n 'max_depth': [3, 5, 7, 10],\n 'min_child_weight': [1, 3, 5],\n 'subsample': [0.5, 0.7],\n 'colsample_bytree': [0.5, 0.7],\n 'n_estimators' : [100, 200, 500],\n 'objective': ['binary:logistic'],\n }\n\nclf = xgb.XGBClassifier(random_state=42, tree_method='gpu_hist')\nmodel = GridSearchCV(estimator=clf, param_grid=params, cv=3, n_jobs=-1)\nmodel.fit(X_train[feat_cols].values, y_train)\n","execution_count":8,"outputs":[{"output_type":"execute_result","execution_count":8,"data":{"text/plain":"GridSearchCV(cv=3,\n estimator=XGBClassifier(base_score=None, booster=None,\n colsample_bylevel=None,\n colsample_bynode=None,\n colsample_bytree=None, gamma=None,\n gpu_id=None, importance_type='gain',\n interaction_constraints=None,\n learning_rate=None, max_delta_step=None,\n max_depth=None, min_child_weight=None,\n missing=nan, monotone_constraints=None,\n n_estimators=100, n_jobs...\n reg_alpha=None, reg_lambda=None,\n scale_pos_weight=None, subsample=None,\n tree_method='gpu_hist',\n validate_parameters=None, verbosity=None),\n n_jobs=-1,\n param_grid={'colsample_bytree': [0.5, 0.7],\n 'learning_rate': [0.001, 0.01, 0.05, 0.1],\n 'max_depth': [3, 5, 7, 10],\n 'min_child_weight': [1, 3, 5],\n 'n_estimators': [100, 200, 500],\n 'objective': ['binary:logistic'],\n 'subsample': [0.5, 0.7]})"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_pred=model.predict(X_test[feat_cols].values)\nprint(classification_report(y_test,y_pred))\nprint(confusion_matrix(y_pred,y_test))\n#Accuracy Score\nprint('accuracy is ',accuracy_score(y_pred,y_test))","execution_count":9,"outputs":[{"output_type":"stream","text":" precision recall f1-score support\n\n 0 1.00 1.00 1.00 56864\n 1 0.94 0.73 0.82 98\n\n accuracy 1.00 56962\n macro avg 0.97 0.87 0.91 56962\nweighted avg 1.00 1.00 1.00 56962\n\n[[56859 26]\n [ 5 72]]\naccuracy is 0.9994557775359011\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"Excluding Naive Bayes, all the models have showed promising accuracy, precision, recall, f1-score. Now, for that unbalanced problem in the dataset there is a solution, we can use undersampling or oversampling method and there is library for that. Next we will use imblearn's ADASYN over-sampler to solve the unbalance problem."},{"metadata":{},"cell_type":"markdown","source":"## Using Over-Sampling (ADASYN) Method"},{"metadata":{"trusted":true},"cell_type":"code","source":"from imblearn.over_sampling import ADASYN\n\nads = ADASYN( random_state=42)\n\nX_ads, y_ads = ads.fit_resample(X_train_full[feat_cols].values, y_train_full)","execution_count":5,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sss = StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=42)\n\nfor train_index, test_index in sss.split(X_ads, y_ads):\n X_train_ads, X_valid_ads = X_ads[train_index], X_ads[test_index]\n y_train_ads, y_valid_ads = y_ads[train_index], y_ads[test_index]","execution_count":6,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Naive Bayes"},{"metadata":{"trusted":true},"cell_type":"code","source":"## Naive Bayes\n\nModel = GaussianNB()\nModel.fit(X_train_ads, y_train_ads)\ny_pred = Model.predict(X_test[feat_cols])\n\n# Summary of the predictions made by the classifier\nprint(classification_report(y_test, y_pred))\nprint(confusion_matrix(y_test, y_pred))\n# Accuracy score\n\nprint('accuracy is',accuracy_score(y_pred,y_test))","execution_count":30,"outputs":[{"output_type":"stream","text":" precision recall f1-score support\n\n 0 1.00 0.96 0.98 56864\n 1 0.04 0.90 0.07 98\n\n accuracy 0.96 56962\n macro avg 0.52 0.93 0.52 56962\nweighted avg 1.00 0.96 0.98 56962\n\n[[54491 2373]\n [ 10 88]]\naccuracy is 0.9581650925178189\n","name":"stdout"}]},{"metadata":{},"cell_type":"raw","source":"recall is high but precision is very low."},{"metadata":{},"cell_type":"markdown","source":"## XGBoost"},{"metadata":{"trusted":true},"cell_type":"code","source":"params = {\n 'learning_rate': [0.001, 0.01, 0.05, 0.1],\n 'max_depth': [3, 5, 7],\n 'min_child_weight': [1, 3, 5],\n 'subsample': [0.5, 0.7],\n 'colsample_bytree': [0.5, 0.7],\n 'n_estimators' : [100, 200, 500],\n 'objective': ['binary:logistic']\n }\n\nclf = xgb.XGBClassifier(random_state=42, tree_method='gpu_hist')\nmodel = GridSearchCV(estimator=clf, param_grid=params, cv=3, n_jobs=-1)\nmodel.fit(X_train_ads, y_train_ads)","execution_count":12,"outputs":[{"output_type":"execute_result","execution_count":12,"data":{"text/plain":"GridSearchCV(cv=3,\n estimator=XGBClassifier(base_score=None, booster=None,\n colsample_bylevel=None,\n colsample_bynode=None,\n colsample_bytree=None, gamma=None,\n gpu_id=None, importance_type='gain',\n interaction_constraints=None,\n learning_rate=None, max_delta_step=None,\n max_depth=None, min_child_weight=None,\n missing=nan, monotone_constraints=None,\n n_estimators=100, n_jobs...\n reg_alpha=None, reg_lambda=None,\n scale_pos_weight=None, subsample=None,\n tree_method='gpu_hist',\n validate_parameters=None, verbosity=None),\n n_jobs=-1,\n param_grid={'colsample_bytree': [0.5, 0.7],\n 'learning_rate': [0.001, 0.01, 0.05, 0.1],\n 'max_depth': [3, 5, 7], 'min_child_weight': [1, 3, 5],\n 'n_estimators': [100, 200, 500],\n 'objective': ['binary:logistic'],\n 'subsample': [0.5, 0.7]})"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_pred=model.predict(X_test[feat_cols].values)\nprint(classification_report(y_test,y_pred))\nprint(confusion_matrix(y_pred,y_test))\n#Accuracy Score\nprint('accuracy is ',accuracy_score(y_pred,y_test))","execution_count":13,"outputs":[{"output_type":"stream","text":" precision recall f1-score support\n\n 0 1.00 1.00 1.00 56864\n 1 0.75 0.84 0.79 98\n\n accuracy 1.00 56962\n macro avg 0.87 0.92 0.89 56962\nweighted avg 1.00 1.00 1.00 56962\n\n[[56836 16]\n [ 28 82]]\naccuracy is 0.9992275552122467\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## Random Forest"},{"metadata":{"trusted":true},"cell_type":"code","source":"\nclf = RandomForestClassifier(max_depth=3, min_samples_split=4,min_samples_leaf=4, random_state=42)\nclf.fit(X_train_ads, y_train_ads)\n\ny_pred = clf.predict(X_test)\n# Summary of the predictions made by the classifier\nprint(classification_report(y_test, y_pred))\nprint(confusion_matrix(y_test, y_pred))\n# Accuracy score\n\nprint('accuracy is',accuracy_score(y_pred,y_test))","execution_count":33,"outputs":[{"output_type":"stream","text":" precision recall f1-score support\n\n 0 1.00 0.96 0.98 56864\n 1 0.00 0.03 0.00 98\n\n accuracy 0.96 56962\n macro avg 0.50 0.50 0.49 56962\nweighted avg 1.00 0.96 0.98 56962\n\n[[54729 2135]\n [ 95 3]]\naccuracy is 0.9608510937115972\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"0% precision and 3% recall is not acceptable."},{"metadata":{},"cell_type":"markdown","source":"## LightGBM"},{"metadata":{"trusted":true},"cell_type":"code","source":"from scipy.stats import randint as sp_randint\nfrom scipy.stats import uniform as sp_uniform\nfit_params = {\"early_stopping_rounds\" : 50,\n \"eval_metric\" : 'binary', \n \"eval_set\" : [(X_valid[feat_cols].values, y_valid)],\n 'eval_names': ['valid'],\n 'verbose': 0,\n 'categorical_feature': 'auto'}\n\nparam_test = {'learning_rate' : [0.001, 0.005, 0.03, 0.05, 0.1],\n 'n_estimators' : [100, 300, 500, 800],\n 'num_leaves': sp_randint(6, 40),\n 'min_child_weight': [1e-5, 1e-3, 1e-2, 1e-1, 1e2],\n 'subsample': sp_uniform(loc=0.2, scale=0.8), \n 'max_depth': [ 3, 4, 5, 6, 7],\n 'colsample_bytree': sp_uniform(loc=0.4, scale=0.6),\n 'reg_alpha': [0, 1, 5, 7, 10],\n 'reg_lambda': [0, 1, 5, 10]}\n\n#number of combinations\nn_iter = 4 #(replace 2 by 200, 90 minutes)\n\n#intialize lgbm and lunch the search\nlgbm_clf = lgb.LGBMClassifier(random_state=42, silent=True, metric='None')\ngrid_search = RandomizedSearchCV(\n estimator=lgbm_clf, param_distributions=param_test, \n scoring='accuracy',\n cv=4, n_jobs=-1)\n\ngrid_search.fit(X_train_ads, y_train_ads, **fit_params)\nprint('Best score reached: {} with params: {} '.format(grid_search.best_score_, grid_search.best_params_))\n\nopt_parameters = grid_search.best_params_\n\nclf_sw = lgb.LGBMClassifier(**lgbm_clf.get_params())\n#Optimal parameter\nclf_sw.set_params(**opt_parameters)","execution_count":14,"outputs":[{"output_type":"stream","text":"Best score reached: 0.9997554515079574 with params: {'colsample_bytree': 0.5339868613881635, 'learning_rate': 0.05, 'max_depth': 7, 'min_child_weight': 0.1, 'n_estimators': 800, 'num_leaves': 36, 'reg_alpha': 0, 'reg_lambda': 1, 'subsample': 0.9901662910177844} \n","name":"stdout"},{"output_type":"execute_result","execution_count":14,"data":{"text/plain":"LGBMClassifier(colsample_bytree=0.5339868613881635, learning_rate=0.05,\n max_depth=7, metric='None', min_child_weight=0.1,\n n_estimators=800, num_leaves=36, random_state=42, reg_alpha=0,\n reg_lambda=1, subsample=0.9901662910177844)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"lgbm_clf = lgb.LGBMClassifier(colsample_bytree=0.5339868613881635, learning_rate=0.05,\n max_depth=7, metric='None', min_child_weight=0.1,\n n_estimators=800, num_leaves=36, random_state=42, reg_alpha=0,\n reg_lambda=1, subsample=0.9901662910177844)\n\nlgbm_clf.fit(X_train, y_train)\ny_pred = lgbm_clf.predict(X_test)\ny_score = lgbm_clf.predict_proba(X_test)[:,1]\nprint(classification_report(y_test,y_pred.round()))","execution_count":18,"outputs":[{"output_type":"stream","text":" precision recall f1-score support\n\n 0 1.00 1.00 1.00 56864\n 1 0.95 0.76 0.84 98\n\n accuracy 1.00 56962\n macro avg 0.97 0.88 0.92 56962\nweighted avg 1.00 1.00 1.00 56962\n\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## Catboost"},{"metadata":{"trusted":true,"collapsed":true},"cell_type":"code","source":"Model=CatBoostClassifier(eval_metric='Accuracy',use_best_model=True,random_seed=42, task_type='GPU')\nModel.fit(X_train_ads,y_train_ads,eval_set=(X_valid_ads,y_valid_ads))","execution_count":16,"outputs":[{"output_type":"stream","text":"Learning rate set to 0.043136\n0:\tlearn: 0.8842351\ttest: 0.8851459\tbest: 0.8851459 (0)\ttotal: 12.4ms\tremaining: 12.4s\n1:\tlearn: 0.9029774\ttest: 0.9033687\tbest: 0.9033687 (1)\ttotal: 23.2ms\tremaining: 11.6s\n2:\tlearn: 0.9106711\ttest: 0.9111612\tbest: 0.9111612 (2)\ttotal: 33.7ms\tremaining: 11.2s\n3:\tlearn: 0.9151417\ttest: 0.9157334\tbest: 0.9157334 (3)\ttotal: 44.3ms\tremaining: 11s\n4:\tlearn: 0.9188786\ttest: 0.9184371\tbest: 0.9184371 (4)\ttotal: 55ms\tremaining: 10.9s\n5:\tlearn: 0.9212801\ttest: 0.9209540\tbest: 0.9209540 (5)\ttotal: 65.6ms\tremaining: 10.9s\n6:\tlearn: 0.9210356\ttest: 0.9208990\tbest: 0.9209540 (5)\ttotal: 76.3ms\tremaining: 10.8s\n7:\tlearn: 0.9193155\ttest: 0.9194263\tbest: 0.9209540 (5)\ttotal: 87ms\tremaining: 10.8s\n8:\tlearn: 0.9205822\ttest: 0.9204814\tbest: 0.9209540 (5)\ttotal: 101ms\tremaining: 11.1s\n9:\tlearn: 0.9240856\ttest: 0.9238446\tbest: 0.9238446 (9)\ttotal: 111ms\tremaining: 11s\n10:\tlearn: 0.9255858\ttest: 0.9257240\tbest: 0.9257240 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182ms\n985:\tlearn: 0.9998516\ttest: 0.9996373\tbest: 0.9996483 (949)\ttotal: 12s\tremaining: 170ms\n986:\tlearn: 0.9998516\ttest: 0.9996373\tbest: 0.9996483 (949)\ttotal: 12s\tremaining: 158ms\n987:\tlearn: 0.9998544\ttest: 0.9996373\tbest: 0.9996483 (949)\ttotal: 12s\tremaining: 146ms\n988:\tlearn: 0.9998544\ttest: 0.9996373\tbest: 0.9996483 (949)\ttotal: 12s\tremaining: 134ms\n989:\tlearn: 0.9998544\ttest: 0.9996373\tbest: 0.9996483 (949)\ttotal: 12s\tremaining: 121ms\n990:\tlearn: 0.9998544\ttest: 0.9996373\tbest: 0.9996483 (949)\ttotal: 12s\tremaining: 109ms\n991:\tlearn: 0.9998544\ttest: 0.9996373\tbest: 0.9996483 (949)\ttotal: 12s\tremaining: 97.1ms\n992:\tlearn: 0.9998544\ttest: 0.9996373\tbest: 0.9996483 (949)\ttotal: 12.1s\tremaining: 85ms\n993:\tlearn: 0.9998544\ttest: 0.9996373\tbest: 0.9996483 (949)\ttotal: 12.1s\tremaining: 72.8ms\n994:\tlearn: 0.9998571\ttest: 0.9996373\tbest: 0.9996483 (949)\ttotal: 12.1s\tremaining: 60.7ms\n995:\tlearn: 0.9998571\ttest: 0.9996373\tbest: 0.9996483 (949)\ttotal: 12.1s\tremaining: 48.6ms\n996:\tlearn: 0.9998571\ttest: 0.9996373\tbest: 0.9996483 (949)\ttotal: 12.1s\tremaining: 36.4ms\n997:\tlearn: 0.9998571\ttest: 0.9996373\tbest: 0.9996483 (949)\ttotal: 12.1s\tremaining: 24.3ms\n998:\tlearn: 0.9998571\ttest: 0.9996483\tbest: 0.9996483 (949)\ttotal: 12.1s\tremaining: 12.1ms\n999:\tlearn: 0.9998571\ttest: 0.9996483\tbest: 0.9996483 (949)\ttotal: 12.1s\tremaining: 0us\nbestTest = 0.9996482937\nbestIteration = 949\nShrink model to first 950 iterations.\n","name":"stdout"},{"output_type":"execute_result","execution_count":16,"data":{"text/plain":"<catboost.core.CatBoostClassifier at 0x7f97790bac10>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"## CatBoost\n\ny_pred=Model.predict(X_test[feat_cols].values)\n\nprint(classification_report(y_test,y_pred))\nprint(confusion_matrix(y_pred,y_test))\n#Accuracy Score\nprint('accuracy is ',accuracy_score(y_pred,y_test))","execution_count":17,"outputs":[{"output_type":"stream","text":" precision recall f1-score support\n\n 0 1.00 1.00 1.00 56864\n 1 0.56 0.85 0.68 98\n\n accuracy 1.00 56962\n macro avg 0.78 0.92 0.84 56962\nweighted avg 1.00 1.00 1.00 56962\n\n[[56800 15]\n [ 64 83]]\naccuracy is 0.9986131104947158\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"Both LightGBM and XGBoost showed great results with oversampler. Next we will try Convolutional Neural Network to detect the frauds."},{"metadata":{},"cell_type":"markdown","source":"## Fraud Detection with CNN"},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train_d = np.expand_dims(X_train, -1)\nX_test_d = np.expand_dims(X_test, -1)\nX_valid_d = np.expand_dims(X_valid, -1)\ny_valid_d = np.expand_dims(y_valid, -1)\ny_train_d = np.expand_dims(y_train, -1)","execution_count":7,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Sequential()\nmodel.add(Conv1D(32, 2, activation='selu', kernel_initializer=\"lecun_normal\", input_shape=X_train_d[0].shape))\nmodel.add(BatchNormalization())\nmodel.add(Dropout(0.1))\n\nmodel.add(Conv1D(64, 2, activation='selu', kernel_initializer=\"lecun_normal\"))\nmodel.add(BatchNormalization())\nmodel.add(Dropout(0.2))\n\nmodel.add(Conv1D(128, 2, activation='selu', kernel_initializer=\"lecun_normal\"))\nmodel.add(BatchNormalization())\nmodel.add(Dropout(0.3))\n\nmodel.add(Flatten())\nmodel.add(Dropout(0.4))\nmodel.add(Dense(512, activation='selu', kernel_initializer=\"lecun_normal\"))\nmodel.add(Dropout(0.5))\n\nmodel.add(Dense(1, activation='sigmoid'))","execution_count":12,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"collapsed":true},"cell_type":"code","source":"optimizer = keras.optimizers.Nadam(lr=0.0001, beta_1=0.9, beta_2=0.999)\nmodel.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=[keras.metrics.AUC()])\nm = model.fit(X_train_d, y_train, \n validation_data=(X_valid_d, y_valid_d),\n batch_size=500, \n epochs=20,\n )","execution_count":13,"outputs":[{"output_type":"stream","text":"Epoch 1/20\n319/319 [==============================] - 4s 11ms/step - loss: 0.0731 - auc_1: 0.8042 - val_loss: 0.0077 - val_auc_1: 0.8767\nEpoch 2/20\n319/319 [==============================] - 3s 10ms/step - loss: 0.0149 - auc_1: 0.8874 - val_loss: 0.0065 - val_auc_1: 0.9066\nEpoch 3/20\n319/319 [==============================] - 3s 10ms/step - loss: 0.0115 - auc_1: 0.8781 - val_loss: 0.0054 - val_auc_1: 0.9067\nEpoch 4/20\n319/319 [==============================] - 3s 10ms/step - loss: 0.0092 - auc_1: 0.8937 - val_loss: 0.0054 - val_auc_1: 0.9152\nEpoch 5/20\n319/319 [==============================] - 3s 10ms/step - loss: 0.0073 - auc_1: 0.8984 - val_loss: 0.0054 - val_auc_1: 0.9109\nEpoch 6/20\n319/319 [==============================] - 3s 10ms/step - loss: 0.0069 - auc_1: 0.9114 - val_loss: 0.0047 - val_auc_1: 0.9152\nEpoch 7/20\n319/319 [==============================] - 3s 10ms/step - loss: 0.0063 - auc_1: 0.9188 - val_loss: 0.0052 - val_auc_1: 0.9067\nEpoch 8/20\n319/319 [==============================] - 4s 11ms/step - loss: 0.0064 - auc_1: 0.9062 - val_loss: 0.0052 - val_auc_1: 0.9109\nEpoch 9/20\n319/319 [==============================] - 3s 10ms/step - loss: 0.0056 - auc_1: 0.9046 - val_loss: 0.0049 - val_auc_1: 0.9152\nEpoch 10/20\n319/319 [==============================] - 4s 12ms/step - loss: 0.0053 - auc_1: 0.9208 - val_loss: 0.0046 - val_auc_1: 0.9152\nEpoch 11/20\n319/319 [==============================] - 3s 11ms/step - loss: 0.0047 - auc_1: 0.9211 - val_loss: 0.0042 - val_auc_1: 0.9237\nEpoch 12/20\n319/319 [==============================] - 3s 10ms/step - loss: 0.0049 - auc_1: 0.9194 - val_loss: 0.0044 - val_auc_1: 0.9152\nEpoch 13/20\n319/319 [==============================] - 3s 10ms/step - loss: 0.0043 - auc_1: 0.9321 - val_loss: 0.0044 - val_auc_1: 0.9236\nEpoch 14/20\n319/319 [==============================] - 3s 10ms/step - loss: 0.0043 - auc_1: 0.9230 - val_loss: 0.0040 - val_auc_1: 0.9236\nEpoch 15/20\n319/319 [==============================] - 3s 11ms/step - loss: 0.0044 - auc_1: 0.9267 - val_loss: 0.0041 - val_auc_1: 0.9236\nEpoch 16/20\n319/319 [==============================] - 3s 10ms/step - loss: 0.0043 - auc_1: 0.9304 - val_loss: 0.0040 - val_auc_1: 0.9236\nEpoch 17/20\n319/319 [==============================] - 3s 10ms/step - loss: 0.0037 - auc_1: 0.9286 - val_loss: 0.0038 - val_auc_1: 0.9236\nEpoch 18/20\n319/319 [==============================] - 3s 10ms/step - loss: 0.0040 - auc_1: 0.9304 - val_loss: 0.0041 - val_auc_1: 0.9236\nEpoch 19/20\n319/319 [==============================] - 3s 10ms/step - loss: 0.0034 - auc_1: 0.9377 - val_loss: 0.0039 - val_auc_1: 0.9236\nEpoch 20/20\n319/319 [==============================] - 3s 10ms/step - loss: 0.0039 - auc_1: 0.9341 - val_loss: 0.0039 - val_auc_1: 0.9237\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"score = model.evaluate(X_test_d, y_test)\nprint(score)","execution_count":14,"outputs":[{"output_type":"stream","text":"1781/1781 [==============================] - 5s 3ms/step - loss: 0.0044 - auc_1: 0.9183\n[0.004424278624355793, 0.9182588458061218]\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# predict probabilities for test set\nyhat_probs = model.predict(X_test_d, verbose=0)\n# predict crisp classes for test set\nyhat_classes = model.predict_classes(X_test_d, verbose=0)\n\nprecision = precision_score(y_test, yhat_classes)\nprint('\\nPrecision: %f' % precision)\n# recall: tp / (tp + fn)\nrecall = recall_score(y_test, yhat_classes)\nprint('\\nRecall: %f' % recall)\n\nf1 = f1_score(y_test, yhat_classes)\nprint('\\nF1 score: %f' % f1)\n\nprint(\"_______________________________________________\")\nauc = roc_auc_score(y_test, yhat_probs)\nprint('\\nROC AUC: %f' % auc)\n# confusion matrix\nmatrix = confusion_matrix(y_test, yhat_classes)\nprint(\"_______________________________________________\\n\")\nprint(\"Confusion Matrix\\n\")\nprint(matrix)","execution_count":15,"outputs":[{"output_type":"stream","text":"\nPrecision: 0.837209\n\nRecall: 0.734694\n\nF1 score: 0.782609\n_______________________________________________\n\nROC AUC: 0.976063\n_______________________________________________\n\nConfusion Matrix\n\n[[56850 14]\n [ 26 72]]\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(12, 8))\n\nplt.subplot(2, 2, 1)\nplt.plot(m.history['loss'], label='Loss')\nplt.plot(m.history['val_loss'], label='val_Loss')\nplt.legend()\n\nplt.subplot(2, 2, 2)\nplt.plot(m.history['auc_1'], label='auc_1')\nplt.plot(m.history['val_auc_1'], label='val_auc_1')\nplt.legend()","execution_count":16,"outputs":[{"output_type":"execute_result","execution_count":16,"data":{"text/plain":"<matplotlib.legend.Legend at 0x7fbf7d666c10>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 864x576 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4} | 0047/235/47235228.ipynb | s3://data-agents/kaggle-outputs/sharded/010_00047.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"execution": {
"iopub.execute_input": "2020-11-19T11:43:51.595234Z",
"iopub.status.busy": "2020-11-19T11:43:51.593751Z",
"iopub.status.idle": "2020-11-19T11:43:51.601383Z",
"shell.execute_reply": "2020-11-19T11:43:51.600628Z"
},
"papermill": {
"duration": 0.029709,
"end_time": "2020-11-19T11:43:51.601520",
"exception": false,
"start_time": "2020-11-19T11:43:51.571811",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/kaggle/input/depression-in-married-couples/Dataset 1.csv\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load\n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the read-only \"../input/\" directory\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
"\n",
"import os\n",
"for dirname, _, filenames in os.walk('/kaggle/input'):\n",
" for filename in filenames:\n",
" print(os.path.join(dirname, filename))\n",
"\n",
"# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
"# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
"_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
"execution": {
"iopub.execute_input": "2020-11-19T11:43:51.636535Z",
"iopub.status.busy": "2020-11-19T11:43:51.635730Z",
"iopub.status.idle": "2020-11-19T11:43:53.066428Z",
"shell.execute_reply": "2020-11-19T11:43:53.065565Z"
},
"papermill": {
"duration": 1.451077,
"end_time": "2020-11-19T11:43:53.066572",
"exception": false,
"start_time": "2020-11-19T11:43:51.615495",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/kaggle/input/depression-in-married-couples/Dataset 1.csv\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"from sklearn.preprocessing import StandardScaler\n",
"from scipy.stats import kruskal\n",
"from sklearn.cluster import SpectralClustering, AffinityPropagation\n",
"from sklearn import metrics\n",
"from ipywidgets import interact\n",
"\n",
"import os\n",
"for dirname, _, filenames in os.walk('/kaggle/input'):\n",
" for filename in filenames:\n",
" print(os.path.join(dirname, filename))"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.013076,
"end_time": "2020-11-19T11:43:53.093782",
"exception": false,
"start_time": "2020-11-19T11:43:53.080706",
"status": "completed"
},
"tags": []
},
"source": [
"## Import, clean and explore the data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:53.126518Z",
"iopub.status.busy": "2020-11-19T11:43:53.125823Z",
"iopub.status.idle": "2020-11-19T11:43:53.160988Z",
"shell.execute_reply": "2020-11-19T11:43:53.161571Z"
},
"papermill": {
"duration": 0.054495,
"end_time": "2020-11-19T11:43:53.161722",
"exception": false,
"start_time": "2020-11-19T11:43:53.107227",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>No</th>\n",
" <th>Gender</th>\n",
" <th>Education</th>\n",
" <th>Working Status</th>\n",
" <th>Marriage Style</th>\n",
" <th>Status of Having a Child</th>\n",
" <th>B1</th>\n",
" <th>B2</th>\n",
" <th>B3</th>\n",
" <th>B4</th>\n",
" <th>...</th>\n",
" <th>B12</th>\n",
" <th>B13</th>\n",
" <th>B14</th>\n",
" <th>B15</th>\n",
" <th>B16</th>\n",
" <th>B17</th>\n",
" <th>B18</th>\n",
" <th>B19</th>\n",
" <th>B20</th>\n",
" <th>B21</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>345</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>73</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>107</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>131</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
" No Gender Education Working Status Marriage Style \\\n",
"0 345 2 4 2 2 \n",
"1 73 2 3 2 2 \n",
"2 107 2 4 2 2 \n",
"3 131 2 4 2 2 \n",
"4 4 2 4 2 1 \n",
"\n",
" Status of Having a Child B1 B2 B3 B4 ... B12 B13 B14 B15 B16 \\\n",
"0 2 3 3 3 3 ... 3 3 3 3 3 \n",
"1 1 1 1 1 1 ... 1 0 1 1 0 \n",
"2 1 0 0 0 1 ... 1 0 1 0 0 \n",
"3 1 0 1 0 1 ... 0 0 0 0 3 \n",
"4 1 0 0 0 0 ... 0 0 0 0 0 \n",
"\n",
" B17 B18 B19 B20 B21 \n",
"0 3 3 3 3 3 \n",
"1 1 0 0 0 0 \n",
"2 1 0 0 0 1 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
"[5 rows x 27 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv('/kaggle/input/depression-in-married-couples/Dataset 1.csv')\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:53.199378Z",
"iopub.status.busy": "2020-11-19T11:43:53.198529Z",
"iopub.status.idle": "2020-11-19T11:43:53.203147Z",
"shell.execute_reply": "2020-11-19T11:43:53.202551Z"
},
"papermill": {
"duration": 0.0273,
"end_time": "2020-11-19T11:43:53.203269",
"exception": false,
"start_time": "2020-11-19T11:43:53.175969",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"No 0\n",
"Gender 0\n",
"Education 0\n",
"Working Status 0\n",
"Marriage Style 0\n",
"Status of Having a Child 0\n",
"B1 0\n",
"B2 0\n",
"B3 0\n",
"B4 0\n",
"B5 0\n",
"B6 0\n",
"B7 0\n",
"B8 0\n",
"B9 0\n",
"B10 0\n",
"B11 0\n",
"B12 0\n",
"B13 0\n",
"B14 0\n",
"B15 0\n",
"B16 0\n",
"B17 0\n",
"B18 0\n",
"B19 0\n",
"B20 0\n",
"B21 0\n",
"dtype: int64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.014032,
"end_time": "2020-11-19T11:43:53.232608",
"exception": false,
"start_time": "2020-11-19T11:43:53.218576",
"status": "completed"
},
"tags": []
},
"source": [
"The data set is complete, no missing values."
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.014095,
"end_time": "2020-11-19T11:43:53.261831",
"exception": false,
"start_time": "2020-11-19T11:43:53.247736",
"status": "completed"
},
"tags": []
},
"source": [
"Let's eliminate $N_{0}$ because is just an identification numbers"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:53.297327Z",
"iopub.status.busy": "2020-11-19T11:43:53.296629Z",
"iopub.status.idle": "2020-11-19T11:43:53.300289Z",
"shell.execute_reply": "2020-11-19T11:43:53.299624Z"
},
"papermill": {
"duration": 0.023978,
"end_time": "2020-11-19T11:43:53.300407",
"exception": false,
"start_time": "2020-11-19T11:43:53.276429",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"data=data.drop(columns=\"No\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:53.339436Z",
"iopub.status.busy": "2020-11-19T11:43:53.338765Z",
"iopub.status.idle": "2020-11-19T11:43:54.038550Z",
"shell.execute_reply": "2020-11-19T11:43:54.039075Z"
},
"papermill": {
"duration": 0.723984,
"end_time": "2020-11-19T11:43:54.039231",
"exception": false,
"start_time": "2020-11-19T11:43:53.315247",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1152x648 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data_std=pd.DataFrame(StandardScaler().fit_transform(data), index=data.index, columns=data.columns.values.tolist())\n",
"\n",
"sns.set_style(style='dark')\n",
"plt.figure(figsize=(16,9))\n",
"heatmap = sns.heatmap(data_std.corr(), vmin=-1,vmax=1, annot=False,cmap='viridis')\n",
"\n",
"heatmap.set_title('Correlation Heatmap', fontdict={'fontsize':12}, pad=12)\n",
"plt.show()\n",
"\n",
"#The correlation heatmap will give some important visualization of the data"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.015658,
"end_time": "2020-11-19T11:43:54.071334",
"exception": false,
"start_time": "2020-11-19T11:43:54.055676",
"status": "completed"
},
"tags": []
},
"source": [
"We can observe to regions of correlation uncorrelated to each other:\n",
"* The one with gender,working status, etc, which is poor correlationed\n",
"* The one of B1,B2,...,B21 which is strongly correlated\n",
"\n",
"Let's do a Kruskal test to see it. Then we can take a clustering to see if we are able to distinct some regions."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:54.109575Z",
"iopub.status.busy": "2020-11-19T11:43:54.108849Z",
"iopub.status.idle": "2020-11-19T11:43:54.112127Z",
"shell.execute_reply": "2020-11-19T11:43:54.111516Z"
},
"papermill": {
"duration": 0.024653,
"end_time": "2020-11-19T11:43:54.112249",
"exception": false,
"start_time": "2020-11-19T11:43:54.087596",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"Bi=[\"B\"+str(i) for i in range(1,22)]\n",
"col_names = data.columns.values.tolist()\n",
"col_names = col_names[0:4]\n",
"#We divide de data in two groups:\n",
"#one is for the attributes of the subject and the other for the demographic data"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:54.236616Z",
"iopub.status.busy": "2020-11-19T11:43:54.235877Z",
"iopub.status.idle": "2020-11-19T11:43:54.252662Z",
"shell.execute_reply": "2020-11-19T11:43:54.252000Z"
},
"papermill": {
"duration": 0.124428,
"end_time": "2020-11-19T11:43:54.252779",
"exception": false,
"start_time": "2020-11-19T11:43:54.128351",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"KruskalResult(statistic=97135.58728454646, pvalue=0.0) \n",
" KruskalResult(statistic=99490.3312012059, pvalue=0.0) \n",
" KruskalResult(statistic=94655.55897128813, pvalue=0.0) \n",
" KruskalResult(statistic=97378.21879438421, pvalue=0.0) \n",
" KruskalResult(statistic=101214.50536676496, pvalue=0.0) \n",
" The Kruskal test mean gives:\n",
" Kurskal result(statistic=97974.84032363794, pvalue=0.0)\n"
]
}
],
"source": [
"K1=kruskal(data[col_names],data[Bi[0:4]])\n",
"K2=kruskal(data[col_names],data[Bi[5:9]])\n",
"K3=kruskal(data[col_names],data[Bi[10:14]])\n",
"K4=kruskal(data[col_names],data[Bi[14:18]])\n",
"K5=kruskal(data[col_names],data[Bi[17:21]])\n",
"print(K1,\"\\n\",K2,\"\\n\",K3,\"\\n\",K4,'\\n',K5,\"\\n\",\n",
" \"The Kruskal test mean gives:\\n Kurskal result(statistic={}, pvalue={})\".format(np.mean([K1[0],K2[0],K3[0],K4[0],K5[0]]),np.mean([K1[1],K2[1],K3[1],K4[1],K5[1]])))\n",
"#The Kruskal tests aplied by subgroups and the mean of all of them gives an acceptable\n",
"#value of the test for the whole dataset"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.016345,
"end_time": "2020-11-19T11:43:54.285706",
"exception": false,
"start_time": "2020-11-19T11:43:54.269361",
"status": "completed"
},
"tags": []
},
"source": [
"We observe that there is obviusly a correlation between those two groups with a pvalue of 0.0"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.016312,
"end_time": "2020-11-19T11:43:54.318660",
"exception": false,
"start_time": "2020-11-19T11:43:54.302348",
"status": "completed"
},
"tags": []
},
"source": [
"## Clustering"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:54.359423Z",
"iopub.status.busy": "2020-11-19T11:43:54.358680Z",
"iopub.status.idle": "2020-11-19T11:43:54.689231Z",
"shell.execute_reply": "2020-11-19T11:43:54.690170Z"
},
"papermill": {
"duration": 0.355079,
"end_time": "2020-11-19T11:43:54.690439",
"exception": false,
"start_time": "2020-11-19T11:43:54.335360",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"af = AffinityPropagation(preference=-50,random_state=42).fit(data)\n",
"#This provides the number of necessary clusters"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:54.744971Z",
"iopub.status.busy": "2020-11-19T11:43:54.743884Z",
"iopub.status.idle": "2020-11-19T11:43:54.748082Z",
"shell.execute_reply": "2020-11-19T11:43:54.748875Z"
},
"papermill": {
"duration": 0.033762,
"end_time": "2020-11-19T11:43:54.749111",
"exception": false,
"start_time": "2020-11-19T11:43:54.715349",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Cluster Centers ID: [ 0 1 34 52 94 123 172 220 233 274 278 279 309 313 399] \n",
" Clust labels: [ 0 1 6 12 6 4 6 10 6 6 8 6 6 6 11 5 13 1 4 7 2 6 8 6\n",
" 1 6 9 4 8 7 7 2 14 1 2 11 1 13 7 1 4 4 7 7 7 6 7 4\n",
" 7 7 3 8 3 8 1 10 11 5 5 13 5 5 8 11 9 1 4 4 10 4 11 12\n",
" 4 4 4 4 5 4 13 4 4 8 4 4 12 4 4 4 4 4 4 4 4 6 4 4\n",
" 4 3 4 11 10 10 1 7 4 7 5 8 9 7 7 3 1 5 3 3 5 4 3 10\n",
" 2 14 1 5 5 5 10 8 1 6 4 4 4 4 4 4 4 5 3 11 11 7 13 9\n",
" 5 1 10 4 7 9 3 7 6 1 14 1 1 13 6 10 10 1 6 6 6 6 13 6\n",
" 6 7 6 6 6 6 6 11 14 11 11 10 14 8 1 6 4 6 12 6 13 11 11 13\n",
" 7 7 7 3 7 14 1 10 6 10 11 11 11 6 7 7 1 14 10 13 13 11 2 8\n",
" 6 7 7 7 7 7 10 11 7 10 14 13 1 1 10 10 13 8 11 8 11 12 6 4\n",
" 1 10 6 4 11 11 10 13 4 10 7 7 4 13 9 7 13 1 13 7 7 14 11 6\n",
" 1 12 6 7 7 5 7 7 7 10 9 12 11 13 10 11 11 8 11 11 10 13 13 6\n",
" 6 12 7 6 4 14 5 14 13 7 7 13 7 3 3 9 2 4 13 13 9 12 7 7\n",
" 14 13 13 11 13 7 7 8 13 2 13 7 10 2 11 11 6 13 6 11 6 13 11 11\n",
" 14 12 4 11 3 14 13 12 13 13 10 8 4 13 4 6 10 4 8 12 4 6 7 3\n",
" 13 7 7 7 6 14 8 14 13 10 10 12 14 10 10 13 9 13 12 8 10 11 9 12\n",
" 12 4 7 6 4 4 4 0 13 9 2 14 14 13 14 14 2 11 13 1 8 10 13 1\n",
" 1 7 7 7 10 3 1 2 13 14 13 13 11 8 1 10 6 2 13 13 4 12 12 7\n",
" 4] \n",
"Number of clusters: 15\n"
]
}
],
"source": [
"cluster_centers_ID = af.cluster_centers_indices_\n",
"clust_labels = af.labels_\n",
"n_clust = len(cluster_centers_ID)\n",
"print(\" Cluster Centers ID: {}\".format(cluster_centers_ID),\"\\n\",\n",
" \"Clust labels: \", clust_labels,\"\\n\"\n",
" \"Number of clusters: {}\".format(n_clust))\n",
"#Here we can see the centers of the clusters,\n",
"#where the affinity propagation method cluster the data and the number of clusters"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:54.810683Z",
"iopub.status.busy": "2020-11-19T11:43:54.809496Z",
"iopub.status.idle": "2020-11-19T11:43:54.993030Z",
"shell.execute_reply": "2020-11-19T11:43:54.993571Z"
},
"papermill": {
"duration": 0.21544,
"end_time": "2020-11-19T11:43:54.993713",
"exception": false,
"start_time": "2020-11-19T11:43:54.778273",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Clust label along its number of elements in it:\n",
" [(0, 372), (1, 2), (2, 2), (3, 4), (4, 14), (5, 2), (6, 4), (7, 3), (8, 6), (9, 9), (10, 1), (11, 5), (12, 5), (13, 2), (14, 2)]\n"
]
}
],
"source": [
"#Using Spectral Clustering, which is more robust, based on the number of clusters\n",
"#with a random state for reproducibility\n",
"clust_esp = SpectralClustering(15, random_state=42).fit_predict(data)\n",
"\n",
"(unique, counts)=np.unique(clust_esp, return_counts=True)\n",
"print(\" Clust label along its number of elements in it:\\n\",\n",
" list(zip(unique,counts)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.01774,
"end_time": "2020-11-19T11:43:55.029176",
"exception": false,
"start_time": "2020-11-19T11:43:55.011436",
"status": "completed"
},
"tags": []
},
"source": [
"Almost all values are in the first cluster"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:55.073619Z",
"iopub.status.busy": "2020-11-19T11:43:55.072708Z",
"iopub.status.idle": "2020-11-19T11:43:55.075990Z",
"shell.execute_reply": "2020-11-19T11:43:55.075394Z"
},
"papermill": {
"duration": 0.029441,
"end_time": "2020-11-19T11:43:55.076137",
"exception": false,
"start_time": "2020-11-19T11:43:55.046696",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"def clust_scatter(column1,column2):\n",
" with plt.style.context(\"ggplot\"):\n",
" plt.figure(figsize=(10,6))\n",
" plt.scatter(data[column1],data[column2], c=clust_esp, s=100, cmap=\"viridis\")\n",
" plt.xlabel(column1)\n",
" plt.ylabel(column2)\n",
" plt.title('Clustering visualization for '+column1+ \" and \" +column2)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:55.144499Z",
"iopub.status.busy": "2020-11-19T11:43:55.143633Z",
"iopub.status.idle": "2020-11-19T11:43:55.435834Z",
"shell.execute_reply": "2020-11-19T11:43:55.435355Z"
},
"papermill": {
"duration": 0.34179,
"end_time": "2020-11-19T11:43:55.435956",
"exception": false,
"start_time": "2020-11-19T11:43:55.094166",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<function __main__.clust_scatter(column1, column2)>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interact(clust_scatter, column1=data.columns.values.tolist(),column2=data.columns.values.tolist())\n",
"#Interactive plot for column against column to see the clusters that are given by those column values"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.01838,
"end_time": "2020-11-19T11:43:55.473455",
"exception": false,
"start_time": "2020-11-19T11:43:55.455075",
"status": "completed"
},
"tags": []
},
"source": [
"We found 15 clusters which represents 15 depression scores."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:55.526500Z",
"iopub.status.busy": "2020-11-19T11:43:55.518952Z",
"iopub.status.idle": "2020-11-19T11:43:55.679990Z",
"shell.execute_reply": "2020-11-19T11:43:55.679485Z"
},
"papermill": {
"duration": 0.187897,
"end_time": "2020-11-19T11:43:55.680125",
"exception": false,
"start_time": "2020-11-19T11:43:55.492228",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"with plt.style.context(\"ggplot\"):\n",
" plt.figure(figsize=(10,6))\n",
" ax = plt.subplot(projection=\"3d\")\n",
" ax.scatter3D(data[col_names].sum(axis=1),data[Bi].sum(axis=1),clust_esp, c=clust_esp, s=100, cmap=\"viridis\")\n",
" ax.view_init(elev=0,azim=45)\n",
" ax.set_xlabel(\"Attributes\")\n",
" ax.set_ylabel(\"Bi\")\n",
" ax.set_zlabel(\"Depression score\")\n",
" ax.set_title('Depression Score based on attributes and Bi')\n",
" \n",
"plt.show()\n",
"#3D plot for atributes against Bi (demographic effects) with the altitude and color given by de cluster"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"execution": {
"iopub.execute_input": "2020-11-19T11:43:55.737887Z",
"iopub.status.busy": "2020-11-19T11:43:55.737089Z",
"iopub.status.idle": "2020-11-19T11:43:55.741165Z",
"shell.execute_reply": "2020-11-19T11:43:55.740646Z"
},
"papermill": {
"duration": 0.040565,
"end_time": "2020-11-19T11:43:55.741276",
"exception": false,
"start_time": "2020-11-19T11:43:55.700711",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Gender</th>\n",
" <th>Education</th>\n",
" <th>Working Status</th>\n",
" <th>Marriage Style</th>\n",
" <th>Status of Having a Child</th>\n",
" <th>B1</th>\n",
" <th>B2</th>\n",
" <th>B3</th>\n",
" <th>B4</th>\n",
" <th>B5</th>\n",
" <th>...</th>\n",
" <th>B12</th>\n",
" <th>B13</th>\n",
" <th>B14</th>\n",
" <th>B15</th>\n",
" <th>B16</th>\n",
" <th>B17</th>\n",
" <th>B18</th>\n",
" <th>B19</th>\n",
" <th>B20</th>\n",
" <th>B21</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>226</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>399</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 26 columns</p>\n",
"</div>"
],
"text/plain": [
" Gender Education Working Status Marriage Style \\\n",
"226 1 3 2 2 \n",
"399 1 3 1 2 \n",
"\n",
" Status of Having a Child B1 B2 B3 B4 B5 ... B12 B13 B14 B15 \\\n",
"226 1 3 1 2 1 2 ... 1 1 1 1 \n",
"399 2 3 1 2 1 1 ... 2 1 1 1 \n",
"\n",
" B16 B17 B18 B19 B20 B21 \n",
"226 0 3 0 0 2 3 \n",
"399 1 2 0 0 1 2 \n",
"\n",
"[2 rows x 26 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#The most depressed individuals are\n",
"data[clust_esp==14]"
]
},
{
"cell_type": "markdown",
"metadata": {
"papermill": {
"duration": 0.022311,
"end_time": "2020-11-19T11:43:55.784591",
"exception": false,
"start_time": "2020-11-19T11:43:55.762280",
"status": "completed"
},
"tags": []
},
"source": [
"We can see that are females with Bachelor and a Flirt Marriage with some differences in demographic effects, Working Status and Status of having a child."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"papermill": {
"duration": 0.022462,
"end_time": "2020-11-19T11:43:55.827991",
"exception": false,
"start_time": "2020-11-19T11:43:55.805529",
"status": "completed"
},
"tags": []
},
"outputs": [],
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| 0047/235/47235248.ipynb | s3://data-agents/kaggle-outputs/sharded/010_00047.jsonl.gz |
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