path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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stringclasses 1
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|---|---|---|---|
2040512/cell_10 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
import pandas as pd
daily_Data = pd.read_csv('../input/KaggleV2-May-2016.csv')
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(daily_Data['Gender'])
daily_Data['Gender'] = le.transform(daily_Data['Gender'])
le.fit(daily_Data['No-show'])
daily_Data['No-show'] = le.transform(daily_Data['No-show'])
le.fit(daily_Data['Neighbourhood'])
daily_Data['Neighbourhood'] = le.transform(daily_Data['Neighbourhood'])
daily_Data['AppointmentDay'].head() | code |
2040512/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
daily_Data = pd.read_csv('../input/KaggleV2-May-2016.csv')
print('Age:', sorted(daily_Data.Age.unique()))
print('Gender:', daily_Data.Gender.unique())
print('Neighbourhood', daily_Data.Neighbourhood.unique())
print('Scholarship:', daily_Data.Scholarship.unique())
print('Hipertension:', daily_Data.Hipertension.unique())
print('Diabetes:', daily_Data.Diabetes.unique())
print('Alcoholism:', daily_Data.Alcoholism.unique())
print('Handcap:', daily_Data.Handcap.unique())
print('SMS_received:', daily_Data.SMS_received.unique()) | code |
89132381/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/heart-disease-dataset/heart.csv')
df.dtypes
feature_list = [feature for feature in df.columns]
discrete_feature = [feature for feature in feature_list if len(df[feature].unique()) < 25]
print('Discrete Variables Count: {}'.format(len(discrete_feature)))
print('Discrete features are ', discrete_feature)
cont_feature = [feature for feature in feature_list if len(df[feature].unique()) > 25]
print('Continuous Variables Count: {}'.format(len(cont_feature)))
print('Continuous features are ', cont_feature) | code |
89132381/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/heart-disease-dataset/heart.csv')
df.describe() | code |
89132381/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input/'):
for filename in filenames:
print(os.path.join(dirname, filename))
print('Setup complete, packages loaded') | code |
89132381/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/heart-disease-dataset/heart.csv')
df.dtypes | code |
89132381/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/heart-disease-dataset/heart.csv')
print(df.isnull().any()) | code |
89132381/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/heart-disease-dataset/heart.csv')
df.dtypes
feature_list = [feature for feature in df.columns]
fig, ax = plt.subplots(figsize=(10,8))
sns.heatmap(df.corr(),annot=True,ax=ax,cmap="Greys")
fig = plt.figure(figsize=(18, 15))
gs = fig.add_gridspec(3, 3)
gs.update(wspace=0.5, hspace=0.25)
ax0 = fig.add_subplot(gs[0, 0])
ax1 = fig.add_subplot(gs[0, 1])
ax2 = fig.add_subplot(gs[0, 2])
ax3 = fig.add_subplot(gs[1, 0])
ax4 = fig.add_subplot(gs[1, 1])
ax5 = fig.add_subplot(gs[1, 2])
ax6 = fig.add_subplot(gs[2, 0])
ax7 = fig.add_subplot(gs[2, 1])
ax8 = fig.add_subplot(gs[2, 2])
background_color = '#ffffff'
color_palette = ['#397367', '#63CCCA', '#5DA399', '#42858C', '#35393C']
fig.patch.set_facecolor(background_color)
ax0.set_facecolor(background_color)
ax1.set_facecolor(background_color)
ax2.set_facecolor(background_color)
ax3.set_facecolor(background_color)
ax4.set_facecolor(background_color)
ax5.set_facecolor(background_color)
ax6.set_facecolor(background_color)
ax7.set_facecolor(background_color)
ax8.set_facecolor(background_color)
ax0.spines['bottom'].set_visible(False)
ax0.spines['left'].set_visible(False)
ax0.spines['top'].set_visible(False)
ax0.spines['right'].set_visible(False)
ax0.tick_params(left=False, bottom=False)
ax0.set_xticklabels([])
ax0.set_yticklabels([])
ax0.text(0.5, 0.5, 'Count plot for various\n categorical features\n_________________', horizontalalignment='center', verticalalignment='center', fontsize=18, fontweight='bold', fontfamily='serif', color='#000000')
ax1.text(0.3, 750, 'Sex', fontsize=14, fontweight='bold', fontfamily='serif', color='#000000')
ax1.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1, 5))
sns.countplot(ax=ax1, data=df, x='sex', palette=color_palette)
ax1.set_xlabel('')
ax1.set_ylabel('')
ax2.text(0.3, 730, 'Exang', fontsize=14, fontweight='bold', fontfamily='serif', color='#000000')
ax2.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1, 5))
sns.countplot(ax=ax2, data=df, x='exang', palette=color_palette)
ax2.set_xlabel('')
ax2.set_ylabel('')
ax3.text(1.5, 630, 'Ca', fontsize=14, fontweight='bold', fontfamily='serif', color='#000000')
ax3.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1, 5))
sns.countplot(ax=ax3, data=df, x='ca', palette=color_palette)
ax3.set_xlabel('')
ax3.set_ylabel('')
ax4.text(1.5, 530, 'Cp', fontsize=14, fontweight='bold', fontfamily='serif', color='#000000')
ax4.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1, 5))
sns.countplot(ax=ax4, data=df, x='cp', palette=color_palette)
ax4.set_xlabel('')
ax4.set_ylabel('')
ax5.text(0.5, 900, 'Fbs', fontsize=14, fontweight='bold', fontfamily='serif', color='#000000')
ax5.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1, 5))
sns.countplot(ax=ax5, data=df, x='fbs', palette=color_palette)
ax5.set_xlabel('')
ax5.set_ylabel('')
ax6.text(0.75, 550, 'Restecg', fontsize=14, fontweight='bold', fontfamily='serif', color='#000000')
ax6.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1, 5))
sns.countplot(ax=ax6, data=df, x='restecg', palette=color_palette)
ax6.set_xlabel('')
ax6.set_ylabel('')
ax7.text(0.85, 520, 'Slope', fontsize=14, fontweight='bold', fontfamily='serif', color='#000000')
ax7.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1, 5))
sns.countplot(ax=ax7, data=df, x='slope', palette=color_palette)
ax7.set_xlabel('')
ax7.set_ylabel('')
ax8.text(1.2, 570, 'Thal', fontsize=14, fontweight='bold', fontfamily='serif', color='#000000')
ax8.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1, 5))
sns.countplot(ax=ax8, data=df, x='thal', palette=color_palette)
ax8.set_xlabel('')
ax8.set_ylabel('')
for s in ['top', 'right', 'left']:
ax1.spines[s].set_visible(False)
ax2.spines[s].set_visible(False)
ax3.spines[s].set_visible(False)
ax4.spines[s].set_visible(False)
ax5.spines[s].set_visible(False)
ax6.spines[s].set_visible(False)
ax7.spines[s].set_visible(False)
ax8.spines[s].set_visible(False) | code |
89132381/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/heart-disease-dataset/heart.csv')
df.dtypes
feature_list = [feature for feature in df.columns]
fig, ax = plt.subplots(figsize=(10,8))
sns.heatmap(df.corr(),annot=True,ax=ax,cmap="Greys")
fig = plt.figure(figsize=(18,15))
gs = fig.add_gridspec(3,3)
gs.update(wspace=0.5, hspace=0.25)
ax0 = fig.add_subplot(gs[0,0])
ax1 = fig.add_subplot(gs[0,1])
ax2 = fig.add_subplot(gs[0,2])
ax3 = fig.add_subplot(gs[1,0])
ax4 = fig.add_subplot(gs[1,1])
ax5 = fig.add_subplot(gs[1,2])
ax6 = fig.add_subplot(gs[2,0])
ax7 = fig.add_subplot(gs[2,1])
ax8 = fig.add_subplot(gs[2,2])
background_color = "#ffffff"
color_palette = ["#397367","#63CCCA","#5DA399","#42858C","#35393C"]
fig.patch.set_facecolor(background_color)
ax0.set_facecolor(background_color)
ax1.set_facecolor(background_color)
ax2.set_facecolor(background_color)
ax3.set_facecolor(background_color)
ax4.set_facecolor(background_color)
ax5.set_facecolor(background_color)
ax6.set_facecolor(background_color)
ax7.set_facecolor(background_color)
ax8.set_facecolor(background_color)
# Title of the plot
ax0.spines["bottom"].set_visible(False)
ax0.spines["left"].set_visible(False)
ax0.spines["top"].set_visible(False)
ax0.spines["right"].set_visible(False)
ax0.tick_params(left=False, bottom=False)
ax0.set_xticklabels([])
ax0.set_yticklabels([])
ax0.text(0.5,0.5,
'Count plot for various\n categorical features\n_________________',
horizontalalignment='center',
verticalalignment='center',
fontsize=18, fontweight='bold',
fontfamily='serif',
color="#000000")
# Sex count
ax1.text(0.3, 750, 'Sex', fontsize=14, fontweight='bold', fontfamily='serif', color="#000000")
ax1.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.countplot(ax=ax1,data=df,x='sex',palette=color_palette)
ax1.set_xlabel("")
ax1.set_ylabel("")
# Exng count
ax2.text(0.3, 730, 'Exang', fontsize=14, fontweight='bold', fontfamily='serif', color="#000000")
ax2.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.countplot(ax=ax2,data=df,x='exang',palette=color_palette)
ax2.set_xlabel("")
ax2.set_ylabel("")
# Caa count
ax3.text(1.5, 630, 'Ca', fontsize=14, fontweight='bold', fontfamily='serif', color="#000000")
ax3.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.countplot(ax=ax3,data=df,x='ca',palette=color_palette)
ax3.set_xlabel("")
ax3.set_ylabel("")
# Cp count
ax4.text(1.5, 530, 'Cp', fontsize=14, fontweight='bold', fontfamily='serif', color="#000000")
ax4.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.countplot(ax=ax4,data=df,x='cp',palette=color_palette)
ax4.set_xlabel("")
ax4.set_ylabel("")
# Fbs count
ax5.text(0.5, 900, 'Fbs', fontsize=14, fontweight='bold', fontfamily='serif', color="#000000")
ax5.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.countplot(ax=ax5,data=df,x='fbs',palette=color_palette)
ax5.set_xlabel("")
ax5.set_ylabel("")
# Restecg count
ax6.text(0.75, 550, 'Restecg', fontsize=14, fontweight='bold', fontfamily='serif', color="#000000")
ax6.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.countplot(ax=ax6,data=df,x='restecg',palette=color_palette)
ax6.set_xlabel("")
ax6.set_ylabel("")
# Slp count
ax7.text(0.85, 520, 'Slope', fontsize=14, fontweight='bold', fontfamily='serif', color="#000000")
ax7.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.countplot(ax=ax7,data=df,x='slope',palette=color_palette)
ax7.set_xlabel("")
ax7.set_ylabel("")
# Thall count
ax8.text(1.2, 570, 'Thal', fontsize=14, fontweight='bold', fontfamily='serif', color="#000000")
ax8.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1,5))
sns.countplot(ax=ax8,data=df,x='thal',palette=color_palette)
ax8.set_xlabel("")
ax8.set_ylabel("")
for s in ["top","right","left"]:
ax1.spines[s].set_visible(False)
ax2.spines[s].set_visible(False)
ax3.spines[s].set_visible(False)
ax4.spines[s].set_visible(False)
ax5.spines[s].set_visible(False)
ax6.spines[s].set_visible(False)
ax7.spines[s].set_visible(False)
ax8.spines[s].set_visible(False)
fig = plt.figure(figsize=(18, 16))
gs = fig.add_gridspec(2, 3)
gs.update(wspace=0.3, hspace=0.15)
ax0 = fig.add_subplot(gs[0, 0])
ax1 = fig.add_subplot(gs[0, 1])
ax2 = fig.add_subplot(gs[0, 2])
ax3 = fig.add_subplot(gs[1, 0])
ax4 = fig.add_subplot(gs[1, 1])
ax5 = fig.add_subplot(gs[1, 2])
background_color = '#ffffff'
color_palette = ['#397367', '#63CCCA', '#5DA399', '#42858C', '#35393C']
fig.patch.set_facecolor(background_color)
ax0.set_facecolor(background_color)
ax1.set_facecolor(background_color)
ax2.set_facecolor(background_color)
ax3.set_facecolor(background_color)
ax4.set_facecolor(background_color)
ax5.set_facecolor(background_color)
ax0.spines['bottom'].set_visible(False)
ax0.spines['left'].set_visible(False)
ax0.spines['top'].set_visible(False)
ax0.spines['right'].set_visible(False)
ax0.tick_params(left=False, bottom=False)
ax0.set_xticklabels([])
ax0.set_yticklabels([])
ax0.text(0.5, 0.5, 'Box plot for various\n continuous features\n_________________', horizontalalignment='center', verticalalignment='center', fontsize=18, fontweight='bold', fontfamily='serif', color='#000000')
ax1.text(-0.05, 81, 'Age', fontsize=14, fontweight='bold', fontfamily='serif', color='#000000')
ax1.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1, 5))
sns.boxenplot(ax=ax1, y=df['age'], palette=['#397367'], width=0.6)
ax1.set_xlabel('')
ax1.set_ylabel('')
ax2.text(-0.05, 208, 'Trestbps', fontsize=14, fontweight='bold', fontfamily='serif', color='#000000')
ax2.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1, 5))
sns.boxenplot(ax=ax2, y=df['trestbps'], palette=['#63CCCA'], width=0.6)
ax2.set_xlabel('')
ax2.set_ylabel('')
ax3.text(-0.05, 600, 'Chol', fontsize=14, fontweight='bold', fontfamily='serif', color='#000000')
ax3.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1, 5))
sns.boxenplot(ax=ax3, y=df['chol'], palette=['#5DA399'], width=0.6)
ax3.set_xlabel('')
ax3.set_ylabel('')
ax4.text(-0.09, 210, 'Thalach', fontsize=14, fontweight='bold', fontfamily='serif', color='#000000')
ax4.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1, 5))
sns.boxenplot(ax=ax4, y=df['thalach'], palette=['#42858C'], width=0.6)
ax4.set_xlabel('')
ax4.set_ylabel('')
ax5.text(-0.1, 6.6, 'Oldpeak', fontsize=14, fontweight='bold', fontfamily='serif', color='#000000')
ax5.grid(color='#000000', linestyle=':', axis='y', zorder=0, dashes=(1, 5))
sns.boxenplot(ax=ax5, y=df['oldpeak'], palette=['#35393C'], width=0.6)
ax5.set_xlabel('')
ax5.set_ylabel('')
for s in ['top', 'right', 'left']:
ax1.spines[s].set_visible(False)
ax2.spines[s].set_visible(False)
ax3.spines[s].set_visible(False)
ax4.spines[s].set_visible(False)
ax5.spines[s].set_visible(False) | code |
89132381/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/heart-disease-dataset/heart.csv')
df.dtypes
feature_list = [feature for feature in df.columns]
fig, ax = plt.subplots(figsize=(10, 8))
sns.heatmap(df.corr(), annot=True, ax=ax, cmap='Greys') | code |
89132381/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/heart-disease-dataset/heart.csv')
df.dtypes
feature_list = [feature for feature in df.columns]
print('There are', len(feature_list), 'features found in the data') | code |
89132381/cell_5 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/heart-disease-dataset/heart.csv')
print('Dataset has', df.shape[0], 'entries and', df.shape[1], 'variables') | code |
326886/cell_4 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegression
pd.options.mode.chained_assignment = None
def get_title(name):
name = name.split(',')[1]
name = name.split('.')[0]
return name.strip()
def get_title_grouped(name):
title = get_title(name)
if title in ['Rev', 'Dr', 'Col', 'Major', 'the Countess', 'Sir', 'Lady', 'Jonkheer', 'Capt', 'Dona', 'Don']:
title = 'Rare'
elif title in ['Ms', 'Mlle']:
title = 'Miss'
elif title == 'Mme':
title = 'Mrs'
return title
def get_deck(cabin):
if isinstance(cabin, str):
if cabin[0] == 'T':
return np.nan
return cabin[0]
return cabin
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([train, test])
# feature engineering described in previous notebooks
full['Embarked'].fillna('C', inplace=True)
full['Fare'].fillna(8.05, inplace=True)
full['Title'] = full['Name'].apply(get_title_grouped)
full['Deck'] = full['Cabin'].apply(get_deck)
full['Family size'] = full['Parch'] + full['SibSp']
ticket_nums = [int(n.split()[-1]) for n in full['Ticket'].values if n.split()[-1].isdigit()]
plt.hist(ticket_nums, 50)
plt.xlabel('Ticket number')
plt.ylabel('Count')
plt.show() | code |
326886/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegression
pd.options.mode.chained_assignment = None
def get_title(name):
name = name.split(',')[1]
name = name.split('.')[0]
return name.strip()
def get_title_grouped(name):
title = get_title(name)
if title in ['Rev', 'Dr', 'Col', 'Major', 'the Countess', 'Sir', 'Lady', 'Jonkheer', 'Capt', 'Dona', 'Don']:
title = 'Rare'
elif title in ['Ms', 'Mlle']:
title = 'Miss'
elif title == 'Mme':
title = 'Mrs'
return title
def get_deck(cabin):
if isinstance(cabin, str):
if cabin[0] == 'T':
return np.nan
return cabin[0]
return cabin
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([train, test])
# feature engineering described in previous notebooks
full['Embarked'].fillna('C', inplace=True)
full['Fare'].fillna(8.05, inplace=True)
full['Title'] = full['Name'].apply(get_title_grouped)
full['Deck'] = full['Cabin'].apply(get_deck)
full['Family size'] = full['Parch'] + full['SibSp']
ticket_nums = [int(n.split()[-1]) for n in full['Ticket'].values if n.split()[-1].isdigit()]
ticket_nums = [num for num in ticket_nums if num < 2000000]
def get_ticket_num(ticket):
ticket_num = ticket.split()
ticket_num = ''.join((char for char in ticket_num[-1].strip() if char not in string.punctuation))
if not ticket_num.isdigit():
return np.nan
return int(ticket_num)
full['Ticket number'] = full['Ticket'].apply(get_ticket_num)
full['Ticket number'].fillna(np.nanmedian(full['Ticket number'].values), inplace=True)
full.drop(['Name', 'Ticket', 'Cabin', 'Parch', 'SibSp'], axis=1, inplace=True)
encoders = {}
to_encode = ['Embarked', 'Sex', 'Title']
for col in to_encode:
encoders[col] = LabelEncoder()
encoders[col].fit(full[col])
full[col] = encoders[col].transform(full[col])
age_train = full[full['Age'].notnull()]
age_predict = full[~full['Age'].notnull()]
lr = LinearRegression()
lr.fit(age_train.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1), age_train['Age'])
predicted_ages = lr.predict(age_predict.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1))
age_predict['Age'] = [max(0.0, age) for age in predicted_ages]
full = pd.concat([age_train, age_predict]).sort_values('PassengerId')
ages = age_train.Age
ages.plot.kde(label='Original')
ages = full.Age
ages.plot.kde(label='With predicted missing values')
train = full[full.PassengerId < 892]
test = full[full.PassengerId >= 892]
rf = RandomForestClassifier(n_estimators=100, oob_score=True)
rf.fit(train.drop(['Survived', 'PassengerId'], axis=1), train['Survived'])
rf.score(train.drop(['Survived', 'PassengerId'], axis=1), train['Survived']) | code |
326886/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegression
pd.options.mode.chained_assignment = None
def get_title(name):
name = name.split(',')[1]
name = name.split('.')[0]
return name.strip()
def get_title_grouped(name):
title = get_title(name)
if title in ['Rev', 'Dr', 'Col', 'Major', 'the Countess', 'Sir', 'Lady', 'Jonkheer', 'Capt', 'Dona', 'Don']:
title = 'Rare'
elif title in ['Ms', 'Mlle']:
title = 'Miss'
elif title == 'Mme':
title = 'Mrs'
return title
def get_deck(cabin):
if isinstance(cabin, str):
if cabin[0] == 'T':
return np.nan
return cabin[0]
return cabin
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([train, test])
# feature engineering described in previous notebooks
full['Embarked'].fillna('C', inplace=True)
full['Fare'].fillna(8.05, inplace=True)
full['Title'] = full['Name'].apply(get_title_grouped)
full['Deck'] = full['Cabin'].apply(get_deck)
full['Family size'] = full['Parch'] + full['SibSp']
ticket_nums = [int(n.split()[-1]) for n in full['Ticket'].values if n.split()[-1].isdigit()]
ticket_nums = [num for num in ticket_nums if num < 2000000]
plt.hist(ticket_nums, 50)
plt.xlabel('Ticket number')
plt.ylabel('Count')
plt.show() | code |
326886/cell_16 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegression
pd.options.mode.chained_assignment = None
def get_title(name):
name = name.split(',')[1]
name = name.split('.')[0]
return name.strip()
def get_title_grouped(name):
title = get_title(name)
if title in ['Rev', 'Dr', 'Col', 'Major', 'the Countess', 'Sir', 'Lady', 'Jonkheer', 'Capt', 'Dona', 'Don']:
title = 'Rare'
elif title in ['Ms', 'Mlle']:
title = 'Miss'
elif title == 'Mme':
title = 'Mrs'
return title
def get_deck(cabin):
if isinstance(cabin, str):
if cabin[0] == 'T':
return np.nan
return cabin[0]
return cabin
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([train, test])
# feature engineering described in previous notebooks
full['Embarked'].fillna('C', inplace=True)
full['Fare'].fillna(8.05, inplace=True)
full['Title'] = full['Name'].apply(get_title_grouped)
full['Deck'] = full['Cabin'].apply(get_deck)
full['Family size'] = full['Parch'] + full['SibSp']
ticket_nums = [int(n.split()[-1]) for n in full['Ticket'].values if n.split()[-1].isdigit()]
ticket_nums = [num for num in ticket_nums if num < 2000000]
def get_ticket_num(ticket):
ticket_num = ticket.split()
ticket_num = ''.join((char for char in ticket_num[-1].strip() if char not in string.punctuation))
if not ticket_num.isdigit():
return np.nan
return int(ticket_num)
full['Ticket number'] = full['Ticket'].apply(get_ticket_num)
full['Ticket number'].fillna(np.nanmedian(full['Ticket number'].values), inplace=True)
full.drop(['Name', 'Ticket', 'Cabin', 'Parch', 'SibSp'], axis=1, inplace=True)
encoders = {}
to_encode = ['Embarked', 'Sex', 'Title']
for col in to_encode:
encoders[col] = LabelEncoder()
encoders[col].fit(full[col])
full[col] = encoders[col].transform(full[col])
age_train = full[full['Age'].notnull()]
age_predict = full[~full['Age'].notnull()]
lr = LinearRegression()
lr.fit(age_train.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1), age_train['Age'])
predicted_ages = lr.predict(age_predict.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1))
age_predict['Age'] = [max(0.0, age) for age in predicted_ages]
full = pd.concat([age_train, age_predict]).sort_values('PassengerId')
ages = age_train.Age
ages.plot.kde(label='Original')
ages = full.Age
ages.plot.kde(label='With predicted missing values')
full_with_deck = full[full['Deck'].notnull()]
full_without_deck = full[~full['Deck'].notnull()]
full_with_deck_means, full_without_deck_means = ([], [])
for col in full_with_deck:
if col not in ['Deck', 'PassengerId']:
sum_means = np.nanmean(full_with_deck[col].values) + np.nanmean(full_without_deck[col].values)
full_with_deck_means.append(np.nanmean(full_with_deck[col].values) / sum_means)
full_without_deck_means.append(np.nanmean(full_without_deck[col].values) / sum_means)
bar_width = 0.35
opacity = 0.4
x_index = np.arange(len(full_with_deck_means))
plt.bar(x_index, full_with_deck_means, bar_width, alpha=opacity, color='b', label='With deck value')
plt.bar(x_index + bar_width, full_without_deck_means, bar_width, alpha=opacity, color='r', label='Missing deck value')
plt.legend(loc='upper center', prop={'size': 9})
plt.ylabel('Ratio of means')
plt.xticks(x_index + bar_width, [col for col in full_with_deck if col not in ['PassengerId', 'Deck']])
plt.show() | code |
326886/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegression
pd.options.mode.chained_assignment = None
def get_title(name):
name = name.split(',')[1]
name = name.split('.')[0]
return name.strip()
def get_title_grouped(name):
title = get_title(name)
if title in ['Rev', 'Dr', 'Col', 'Major', 'the Countess', 'Sir', 'Lady', 'Jonkheer', 'Capt', 'Dona', 'Don']:
title = 'Rare'
elif title in ['Ms', 'Mlle']:
title = 'Miss'
elif title == 'Mme':
title = 'Mrs'
return title
def get_deck(cabin):
if isinstance(cabin, str):
if cabin[0] == 'T':
return np.nan
return cabin[0]
return cabin
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([train, test])
# feature engineering described in previous notebooks
full['Embarked'].fillna('C', inplace=True)
full['Fare'].fillna(8.05, inplace=True)
full['Title'] = full['Name'].apply(get_title_grouped)
full['Deck'] = full['Cabin'].apply(get_deck)
full['Family size'] = full['Parch'] + full['SibSp']
ticket_nums = [int(n.split()[-1]) for n in full['Ticket'].values if n.split()[-1].isdigit()]
ticket_nums = [num for num in ticket_nums if num < 2000000]
def get_ticket_num(ticket):
ticket_num = ticket.split()
ticket_num = ''.join((char for char in ticket_num[-1].strip() if char not in string.punctuation))
if not ticket_num.isdigit():
return np.nan
return int(ticket_num)
full['Ticket number'] = full['Ticket'].apply(get_ticket_num)
full['Ticket number'].fillna(np.nanmedian(full['Ticket number'].values), inplace=True)
full.drop(['Name', 'Ticket', 'Cabin', 'Parch', 'SibSp'], axis=1, inplace=True)
encoders = {}
to_encode = ['Embarked', 'Sex', 'Title']
for col in to_encode:
encoders[col] = LabelEncoder()
encoders[col].fit(full[col])
full[col] = encoders[col].transform(full[col])
age_train = full[full['Age'].notnull()]
age_predict = full[~full['Age'].notnull()]
lr = LinearRegression()
lr.fit(age_train.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1), age_train['Age'])
predicted_ages = lr.predict(age_predict.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1))
age_predict['Age'] = [max(0.0, age) for age in predicted_ages]
full = pd.concat([age_train, age_predict]).sort_values('PassengerId')
ages = age_train.Age
ages.plot.kde(label='Original')
ages = full.Age
ages.plot.kde(label='With predicted missing values')
full_with_deck = full[full['Deck'].notnull()]
full_without_deck = full[~full['Deck'].notnull()]
full_with_deck_means, full_without_deck_means = ([], [])
for col in full_with_deck:
if col not in ['Deck', 'PassengerId']:
sum_means = np.nanmean(full_with_deck[col].values) + np.nanmean(full_without_deck[col].values)
full_with_deck_means.append(np.nanmean(full_with_deck[col].values) / sum_means)
full_without_deck_means.append(np.nanmean(full_without_deck[col].values) / sum_means)
bar_width = 0.35
opacity = 0.4
x_index = np.arange(len(full_with_deck_means))
plt.xticks(x_index + bar_width, [col for col in full_with_deck if col not in ['PassengerId', 'Deck']])
train = full[full.PassengerId < 892]
test = full[full.PassengerId >= 892]
rf = RandomForestClassifier(n_estimators=100, oob_score=True)
rf.fit(train.drop(['Survived', 'PassengerId'], axis=1), train['Survived'])
rf.score(train.drop(['Survived', 'PassengerId'], axis=1), train['Survived'])
rf.oob_score_
features = list(zip(train.drop(['Survived', 'PassengerId'], axis=1).columns.values, rf.feature_importances_))
features.sort(key=lambda f: f[1])
names = [f[0] for f in features]
lengths = [f[1] for f in features]
pos = np.arange(len(features)) + 0.5
plt.barh(pos, lengths, align='center', color='r', alpha=opacity)
plt.yticks(pos, names)
plt.xlabel('Gini importance')
plt.show() | code |
326886/cell_14 | [
"image_output_1.png"
] | from collections import Counter
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegression
pd.options.mode.chained_assignment = None
def get_title(name):
name = name.split(',')[1]
name = name.split('.')[0]
return name.strip()
def get_title_grouped(name):
title = get_title(name)
if title in ['Rev', 'Dr', 'Col', 'Major', 'the Countess', 'Sir', 'Lady', 'Jonkheer', 'Capt', 'Dona', 'Don']:
title = 'Rare'
elif title in ['Ms', 'Mlle']:
title = 'Miss'
elif title == 'Mme':
title = 'Mrs'
return title
def get_deck(cabin):
if isinstance(cabin, str):
if cabin[0] == 'T':
return np.nan
return cabin[0]
return cabin
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([train, test])
# feature engineering described in previous notebooks
full['Embarked'].fillna('C', inplace=True)
full['Fare'].fillna(8.05, inplace=True)
full['Title'] = full['Name'].apply(get_title_grouped)
full['Deck'] = full['Cabin'].apply(get_deck)
full['Family size'] = full['Parch'] + full['SibSp']
ticket_nums = [int(n.split()[-1]) for n in full['Ticket'].values if n.split()[-1].isdigit()]
ticket_nums = [num for num in ticket_nums if num < 2000000]
def get_ticket_num(ticket):
ticket_num = ticket.split()
ticket_num = ''.join((char for char in ticket_num[-1].strip() if char not in string.punctuation))
if not ticket_num.isdigit():
return np.nan
return int(ticket_num)
full['Ticket number'] = full['Ticket'].apply(get_ticket_num)
full['Ticket number'].fillna(np.nanmedian(full['Ticket number'].values), inplace=True)
full.drop(['Name', 'Ticket', 'Cabin', 'Parch', 'SibSp'], axis=1, inplace=True)
encoders = {}
to_encode = ['Embarked', 'Sex', 'Title']
for col in to_encode:
encoders[col] = LabelEncoder()
encoders[col].fit(full[col])
full[col] = encoders[col].transform(full[col])
age_train = full[full['Age'].notnull()]
age_predict = full[~full['Age'].notnull()]
lr = LinearRegression()
lr.fit(age_train.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1), age_train['Age'])
predicted_ages = lr.predict(age_predict.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1))
age_predict['Age'] = [max(0.0, age) for age in predicted_ages]
full = pd.concat([age_train, age_predict]).sort_values('PassengerId')
ages = age_train.Age
ages.plot.kde(label='Original')
ages = full.Age
ages.plot.kde(label='With predicted missing values')
Counter(full['Deck'].values) | code |
326886/cell_22 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegression
pd.options.mode.chained_assignment = None
def get_title(name):
name = name.split(',')[1]
name = name.split('.')[0]
return name.strip()
def get_title_grouped(name):
title = get_title(name)
if title in ['Rev', 'Dr', 'Col', 'Major', 'the Countess', 'Sir', 'Lady', 'Jonkheer', 'Capt', 'Dona', 'Don']:
title = 'Rare'
elif title in ['Ms', 'Mlle']:
title = 'Miss'
elif title == 'Mme':
title = 'Mrs'
return title
def get_deck(cabin):
if isinstance(cabin, str):
if cabin[0] == 'T':
return np.nan
return cabin[0]
return cabin
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([train, test])
# feature engineering described in previous notebooks
full['Embarked'].fillna('C', inplace=True)
full['Fare'].fillna(8.05, inplace=True)
full['Title'] = full['Name'].apply(get_title_grouped)
full['Deck'] = full['Cabin'].apply(get_deck)
full['Family size'] = full['Parch'] + full['SibSp']
ticket_nums = [int(n.split()[-1]) for n in full['Ticket'].values if n.split()[-1].isdigit()]
ticket_nums = [num for num in ticket_nums if num < 2000000]
def get_ticket_num(ticket):
ticket_num = ticket.split()
ticket_num = ''.join((char for char in ticket_num[-1].strip() if char not in string.punctuation))
if not ticket_num.isdigit():
return np.nan
return int(ticket_num)
full['Ticket number'] = full['Ticket'].apply(get_ticket_num)
full['Ticket number'].fillna(np.nanmedian(full['Ticket number'].values), inplace=True)
full.drop(['Name', 'Ticket', 'Cabin', 'Parch', 'SibSp'], axis=1, inplace=True)
encoders = {}
to_encode = ['Embarked', 'Sex', 'Title']
for col in to_encode:
encoders[col] = LabelEncoder()
encoders[col].fit(full[col])
full[col] = encoders[col].transform(full[col])
age_train = full[full['Age'].notnull()]
age_predict = full[~full['Age'].notnull()]
lr = LinearRegression()
lr.fit(age_train.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1), age_train['Age'])
predicted_ages = lr.predict(age_predict.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1))
age_predict['Age'] = [max(0.0, age) for age in predicted_ages]
full = pd.concat([age_train, age_predict]).sort_values('PassengerId')
ages = age_train.Age
ages.plot.kde(label='Original')
ages = full.Age
ages.plot.kde(label='With predicted missing values')
train = full[full.PassengerId < 892]
test = full[full.PassengerId >= 892]
rf = RandomForestClassifier(n_estimators=100, oob_score=True)
rf.fit(train.drop(['Survived', 'PassengerId'], axis=1), train['Survived'])
rf.score(train.drop(['Survived', 'PassengerId'], axis=1), train['Survived'])
rf.oob_score_ | code |
326886/cell_12 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegression
pd.options.mode.chained_assignment = None
def get_title(name):
name = name.split(',')[1]
name = name.split('.')[0]
return name.strip()
def get_title_grouped(name):
title = get_title(name)
if title in ['Rev', 'Dr', 'Col', 'Major', 'the Countess', 'Sir', 'Lady', 'Jonkheer', 'Capt', 'Dona', 'Don']:
title = 'Rare'
elif title in ['Ms', 'Mlle']:
title = 'Miss'
elif title == 'Mme':
title = 'Mrs'
return title
def get_deck(cabin):
if isinstance(cabin, str):
if cabin[0] == 'T':
return np.nan
return cabin[0]
return cabin
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([train, test])
# feature engineering described in previous notebooks
full['Embarked'].fillna('C', inplace=True)
full['Fare'].fillna(8.05, inplace=True)
full['Title'] = full['Name'].apply(get_title_grouped)
full['Deck'] = full['Cabin'].apply(get_deck)
full['Family size'] = full['Parch'] + full['SibSp']
ticket_nums = [int(n.split()[-1]) for n in full['Ticket'].values if n.split()[-1].isdigit()]
ticket_nums = [num for num in ticket_nums if num < 2000000]
def get_ticket_num(ticket):
ticket_num = ticket.split()
ticket_num = ''.join((char for char in ticket_num[-1].strip() if char not in string.punctuation))
if not ticket_num.isdigit():
return np.nan
return int(ticket_num)
full['Ticket number'] = full['Ticket'].apply(get_ticket_num)
full['Ticket number'].fillna(np.nanmedian(full['Ticket number'].values), inplace=True)
full.drop(['Name', 'Ticket', 'Cabin', 'Parch', 'SibSp'], axis=1, inplace=True)
encoders = {}
to_encode = ['Embarked', 'Sex', 'Title']
for col in to_encode:
encoders[col] = LabelEncoder()
encoders[col].fit(full[col])
full[col] = encoders[col].transform(full[col])
age_train = full[full['Age'].notnull()]
age_predict = full[~full['Age'].notnull()]
lr = LinearRegression()
lr.fit(age_train.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1), age_train['Age'])
predicted_ages = lr.predict(age_predict.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1))
age_predict['Age'] = [max(0.0, age) for age in predicted_ages]
full = pd.concat([age_train, age_predict]).sort_values('PassengerId')
ages = age_train.Age
ages.plot.kde(label='Original')
ages = full.Age
ages.plot.kde(label='With predicted missing values')
plt.xlabel('Age')
plt.legend(prop={'size': 9})
plt.show() | code |
2017953/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count')
counted['totals'] = counted.sum(axis='columns')
counted = counted.sort_values('totals', ascending=False)
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count', margins=True, margins_name='Totals_all')
counted = counted.sort_values('Totals_all', ascending=False)
medals_by_gender = medals.groupby(['Event_gender', 'Gender']).count()
medals_by_gender
boolean_filter = (medals.Event_gender == 'W') & (medals.Gender == 'Men')
medals[boolean_filter]
medals.iloc[[23675]]
medals.iloc[[23675], [6]] = 'Women'
medals.iloc[[23675]]
Nsports = medals[['NOC', 'Sport']].groupby('NOC', as_index=False).agg({'Sport': 'nunique'}).sort_values('Sport', ascending=False)
Nsports.head(15) | code |
2017953/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count')
counted['totals'] = counted.sum(axis='columns')
counted = counted.sort_values('totals', ascending=False)
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count', margins=True, margins_name='Totals_all')
counted = counted.sort_values('Totals_all', ascending=False)
medals_by_gender = medals.groupby(['Event_gender', 'Gender']).count()
medals_by_gender | code |
2017953/cell_9 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count')
counted['totals'] = counted.sum(axis='columns')
counted = counted.sort_values('totals', ascending=False)
counted.head(15) | code |
2017953/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
print(medals.info())
medals.head() | code |
2017953/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count')
counted['totals'] = counted.sum(axis='columns')
counted = counted.sort_values('totals', ascending=False)
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count', margins=True, margins_name='Totals_all')
counted = counted.sort_values('Totals_all', ascending=False)
medals_by_gender = medals.groupby(['Event_gender', 'Gender']).count()
medals_by_gender
boolean_filter = (medals.Event_gender == 'W') & (medals.Gender == 'Men')
medals[boolean_filter]
medals.iloc[[23675]]
medals.iloc[[23675], [6]] = 'Women'
medals.iloc[[23675]]
Nsports = medals[['NOC', 'Sport']].groupby('NOC', as_index=False).agg({'Sport': 'nunique'}).sort_values('Sport', ascending=False)
during_cold_war = (medals.Edition >= 1952) & (medals.Edition <= 1988)
is_usa_urs = medals.NOC.isin(['USA', 'URS'])
cold_war_medals = medals.loc[during_cold_war & is_usa_urs]
country_grouped = cold_war_medals.groupby('NOC')
Nsports = country_grouped['Sport'].nunique().sort_values(ascending=False)
print(Nsports) | code |
2017953/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
medal_counts = medals['NOC'].value_counts()
print('The total medals: %d' % medal_counts.sum())
print('\nTop 15 countries:\n', medal_counts.head(15)) | code |
2017953/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count')
counted['totals'] = counted.sum(axis='columns')
counted = counted.sort_values('totals', ascending=False)
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count', margins=True, margins_name='Totals_all')
counted = counted.sort_values('Totals_all', ascending=False)
medals_by_gender = medals.groupby(['Event_gender', 'Gender']).count()
medals_by_gender
boolean_filter = (medals.Event_gender == 'W') & (medals.Gender == 'Men')
medals[boolean_filter]
medals.iloc[[23675]]
medals.iloc[[23675], [6]] = 'Women'
medals.iloc[[23675]] | code |
2017953/cell_28 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count')
counted['totals'] = counted.sum(axis='columns')
counted = counted.sort_values('totals', ascending=False)
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count', margins=True, margins_name='Totals_all')
counted = counted.sort_values('Totals_all', ascending=False)
medals_by_gender = medals.groupby(['Event_gender', 'Gender']).count()
medals_by_gender
boolean_filter = (medals.Event_gender == 'W') & (medals.Gender == 'Men')
medals[boolean_filter]
medals.iloc[[23675]]
medals.iloc[[23675], [6]] = 'Women'
medals.iloc[[23675]]
Nsports = medals[['NOC', 'Sport']].groupby('NOC', as_index=False).agg({'Sport': 'nunique'}).sort_values('Sport', ascending=False)
during_cold_war = (medals.Edition >= 1952) & (medals.Edition <= 1988)
is_usa_urs = medals.NOC.isin(['USA', 'URS'])
cold_war_medals = medals.loc[during_cold_war & is_usa_urs]
country_grouped = cold_war_medals.groupby('NOC')
Nsports = country_grouped['Sport'].nunique().sort_values(ascending=False)
medals_won_by_country = medals.pivot_table(index='Edition', columns='NOC', values='Athlete', aggfunc='count')
cold_war_usa_usr_medals = medals_won_by_country.loc[1952:1988, ['USA', 'URS']]
medals.Medal = pd.Categorical(values=medals.Medal, categories=['Bronze', 'Silver', 'Gold'], ordered=True)
usa = medals[medals.NOC == 'USA']
usa_medals_by_year = usa.groupby(['Edition', 'Medal'])['Athlete'].count()
usa_medals_by_year = usa_medals_by_year.unstack(level='Medal')
usa_medals_by_year.plot.area(figsize=(12, 8), title='USA medals over time in Olympic games')
urs = medals[medals.NOC == 'URS']
usa_medals_by_year = urs.groupby(['Edition', 'Medal'])['Athlete'].count()
usa_medals_by_year = usa_medals_by_year.unstack(level='Medal')
usa_medals_by_year.plot.area(figsize=(12, 8), title='URS medals over time in Olympic games')
plt.show() | code |
2017953/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count')
counted['totals'] = counted.sum(axis='columns')
counted = counted.sort_values('totals', ascending=False)
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count', margins=True, margins_name='Totals_all')
counted = counted.sort_values('Totals_all', ascending=False)
medals_by_gender = medals.groupby(['Event_gender', 'Gender']).count()
medals_by_gender
boolean_filter = (medals.Event_gender == 'W') & (medals.Gender == 'Men')
medals[boolean_filter] | code |
2017953/cell_3 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2017953/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count')
counted['totals'] = counted.sum(axis='columns')
counted = counted.sort_values('totals', ascending=False)
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count', margins=True, margins_name='Totals_all')
counted = counted.sort_values('Totals_all', ascending=False)
medals_by_gender = medals.groupby(['Event_gender', 'Gender']).count()
medals_by_gender
boolean_filter = (medals.Event_gender == 'W') & (medals.Gender == 'Men')
medals[boolean_filter]
medals.iloc[[23675]] | code |
2017953/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count')
counted['totals'] = counted.sum(axis='columns')
counted = counted.sort_values('totals', ascending=False)
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count', margins=True, margins_name='Totals_all')
counted = counted.sort_values('Totals_all', ascending=False)
for place, country in enumerate(counted.index):
if country == 'HUN':
print('Hungary is the ' + str(place + 1) + ' country in the total Olympic medals ranking')
break | code |
2017953/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count')
counted['totals'] = counted.sum(axis='columns')
counted = counted.sort_values('totals', ascending=False)
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count', margins=True, margins_name='Totals_all')
counted = counted.sort_values('Totals_all', ascending=False)
medals_by_gender = medals.groupby(['Event_gender', 'Gender']).count()
medals_by_gender
boolean_filter = (medals.Event_gender == 'W') & (medals.Gender == 'Men')
medals[boolean_filter]
medals.iloc[[23675]]
medals.iloc[[23675], [6]] = 'Women'
medals.iloc[[23675]]
Nsports = medals[['NOC', 'Sport']].groupby('NOC', as_index=False).agg({'Sport': 'nunique'}).sort_values('Sport', ascending=False)
during_cold_war = (medals.Edition >= 1952) & (medals.Edition <= 1988)
is_usa_urs = medals.NOC.isin(['USA', 'URS'])
cold_war_medals = medals.loc[during_cold_war & is_usa_urs]
country_grouped = cold_war_medals.groupby('NOC')
Nsports = country_grouped['Sport'].nunique().sort_values(ascending=False)
medals_won_by_country = medals.pivot_table(index='Edition', columns='NOC', values='Athlete', aggfunc='count')
cold_war_usa_usr_medals = medals_won_by_country.loc[1952:1988, ['USA', 'URS']]
print('Consistency during cold war\n', cold_war_usa_usr_medals.idxmax(axis='columns'))
print('\nTotal counts\n', cold_war_usa_usr_medals.idxmax(axis='columns').value_counts()) | code |
2017953/cell_27 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count')
counted['totals'] = counted.sum(axis='columns')
counted = counted.sort_values('totals', ascending=False)
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count', margins=True, margins_name='Totals_all')
counted = counted.sort_values('Totals_all', ascending=False)
medals_by_gender = medals.groupby(['Event_gender', 'Gender']).count()
medals_by_gender
boolean_filter = (medals.Event_gender == 'W') & (medals.Gender == 'Men')
medals[boolean_filter]
medals.iloc[[23675]]
medals.iloc[[23675], [6]] = 'Women'
medals.iloc[[23675]]
Nsports = medals[['NOC', 'Sport']].groupby('NOC', as_index=False).agg({'Sport': 'nunique'}).sort_values('Sport', ascending=False)
during_cold_war = (medals.Edition >= 1952) & (medals.Edition <= 1988)
is_usa_urs = medals.NOC.isin(['USA', 'URS'])
cold_war_medals = medals.loc[during_cold_war & is_usa_urs]
country_grouped = cold_war_medals.groupby('NOC')
Nsports = country_grouped['Sport'].nunique().sort_values(ascending=False)
medals_won_by_country = medals.pivot_table(index='Edition', columns='NOC', values='Athlete', aggfunc='count')
cold_war_usa_usr_medals = medals_won_by_country.loc[1952:1988, ['USA', 'URS']]
medals.Medal = pd.Categorical(values=medals.Medal, categories=['Bronze', 'Silver', 'Gold'], ordered=True)
usa = medals[medals.NOC == 'USA']
usa_medals_by_year = usa.groupby(['Edition', 'Medal'])['Athlete'].count()
usa_medals_by_year = usa_medals_by_year.unstack(level='Medal')
usa_medals_by_year.plot.area(figsize=(12, 8), title='USA medals over time in Olympic games')
plt.show() | code |
2017953/cell_12 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count')
counted['totals'] = counted.sum(axis='columns')
counted = counted.sort_values('totals', ascending=False)
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count', margins=True, margins_name='Totals_all')
counted = counted.sort_values('Totals_all', ascending=False)
ev_gen_uniques = medals[['Event_gender', 'Gender']].drop_duplicates()
ev_gen_uniques | code |
73087956/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
av = pd.read_csv('../input/seti-adversarial-validation/oof.csv')
tmp = pd.read_csv('../input/seti-breakthrough-listen/train_labels.csv')
tmp['label'] = tmp['target']
av = pd.merge(av, tmp[['id', 'label']], on='id', how='left')
av.query('target == 0 and pred > 1e-4') | code |
73087956/cell_1 | [
"text_plain_output_1.png"
] | !pip install git+https://github.com/rwightman/pytorch-image-models
import timm | code |
73087956/cell_3 | [
"text_html_output_1.png"
] | import numpy as np
import os
import random
import torch
class CFG:
seed = 46
debug = False
n_fold = 4
n_epoch = 11
height = 480
width = 480
model_name = 'efficientnet_b0'
lr = 0.0001
min_lr = 1e-06
weight_decay = 0.0001
T_max = 10
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f'Using device {CFG.device}')
def seed_torch(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
seed_torch(CFG.seed) | code |
73087956/cell_5 | [
"text_plain_output_35.png",
"text_plain_output_43.png",
"text_plain_output_37.png",
"text_plain_output_5.png",
"text_plain_output_30.png",
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_15.png",
"text_plain_output_9.png",
"text_plain_output_44.png",
"text_plain_output_40.png",
"text_plain_output_31.png",
"text_plain_output_20.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"text_plain_output_45.png",
"text_plain_output_14.png",
"text_plain_output_32.png",
"text_plain_output_29.png",
"text_plain_output_27.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"text_plain_output_24.png",
"text_plain_output_21.png",
"text_plain_output_25.png",
"text_plain_output_18.png",
"text_plain_output_36.png",
"text_plain_output_3.png",
"text_plain_output_22.png",
"text_plain_output_38.png",
"text_plain_output_7.png",
"text_plain_output_16.png",
"text_plain_output_8.png",
"text_plain_output_26.png",
"text_plain_output_41.png",
"text_plain_output_34.png",
"text_plain_output_42.png",
"text_plain_output_23.png",
"text_plain_output_28.png",
"text_plain_output_1.png",
"text_plain_output_33.png",
"text_plain_output_39.png",
"text_plain_output_19.png",
"text_plain_output_17.png",
"text_plain_output_11.png",
"text_plain_output_12.png",
"text_plain_output_46.png"
] | from sklearn.model_selection import StratifiedKFold
import numpy as np
import os
import pandas as pd
import random
import torch
class CFG:
seed = 46
debug = False
n_fold = 4
n_epoch = 11
height = 480
width = 480
model_name = 'efficientnet_b0'
lr = 0.0001
min_lr = 1e-06
weight_decay = 0.0001
T_max = 10
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def seed_torch(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
seed_torch(CFG.seed)
av = pd.read_csv('../input/seti-adversarial-validation/oof.csv')
tmp = pd.read_csv('../input/seti-breakthrough-listen/train_labels.csv')
tmp['label'] = tmp['target']
av = pd.merge(av, tmp[['id', 'label']], on='id', how='left')
av.query('target == 0 and pred > 1e-4')
train = av.query('target == 0 and pred > 1e-4').reset_index(drop=True)
train['target'] = train['label']
train = train.drop(['label', 'pred'], axis=1)
test = pd.read_csv('../input/seti-breakthrough-listen/sample_submission.csv')
train['file_path'] = train['id'].apply(lambda x: f'../input/seti-breakthrough-listen/train/{x[0]}/{x}.npy')
test['file_path'] = test['id'].apply(lambda x: f'../input/seti-breakthrough-listen/test/{x[0]}/{x}.npy')
print(train['target'].value_counts())
if CFG.debug:
CFG.n_epoch = 2
train = train.sample(n=1000, random_state=CFG.seed).reset_index(drop=True)
test = test.sample(n=1000, random_state=CFG.seed).reset_index(drop=True)
skf = StratifiedKFold(n_splits=CFG.n_fold, shuffle=True, random_state=CFG.seed)
for fold, (_, val_index) in enumerate(skf.split(train, train['target'])):
train.loc[val_index, 'fold'] = fold
train['fold'] = train['fold'].astype(int)
train.groupby(['fold', 'target']).size() | code |
33097852/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv', header=0)
pd.set_option('display.max_rows', 100)
pd.set_option('display.max_columns', 100)
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
var = 'TotalBsmtSF'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
#box plot overallqual/saleprice
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
var = 'YearBuilt'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(16, 8))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
plt.xticks(rotation=90);
sns.set(font_scale=1)
correlation_train = train.corr()
train.corr() | code |
33097852/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv', header=0)
pd.set_option('display.max_rows', 100)
pd.set_option('display.max_columns', 100)
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
data.plot.scatter(x=var, y='SalePrice', ylim=(0, 800000)) | code |
33097852/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv', header=0)
train['SalePrice'].describe() | code |
33097852/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv', header=0)
pd.set_option('display.max_rows', 100)
pd.set_option('display.max_columns', 100)
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
var = 'TotalBsmtSF'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
#box plot overallqual/saleprice
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
var = 'YearBuilt'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(16, 8))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
plt.xticks(rotation=90);
sns.set(font_scale=1)
correlation_train = train.corr()
train.corr()
train_total = train.isnull().sum().sort_values(ascending=False)
train_percent = (train.isnull().sum() / train.isnull().count()).sort_values(ascending=False)
train_missing_data = pd.concat([train_total, train_percent], axis=1, keys=['Total', 'Percent'])
train_missing_data.head(20) | code |
33097852/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv', header=0)
pd.set_option('display.max_rows', 100)
pd.set_option('display.max_columns', 100)
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
var = 'TotalBsmtSF'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
#box plot overallqual/saleprice
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
var = 'YearBuilt'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(16, 8))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
plt.xticks(rotation=90);
sns.set(font_scale=1)
correlation_train = train.corr()
train.corr()
sns.set()
cols = ['SalePrice', 'OverallQual', 'GrLivArea', 'GarageCars', 'TotalBsmtSF', 'FullBath', 'YearBuilt']
sns.pairplot(train[cols], size=2.5)
plt.show() | code |
33097852/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv', header=0)
pd.set_option('display.max_rows', 100)
pd.set_option('display.max_columns', 100)
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
var = 'TotalBsmtSF'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
#box plot overallqual/saleprice
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
var = 'YearBuilt'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(16, 8))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
plt.xticks(rotation=90);
sns.set(font_scale=1)
correlation_train = train.corr()
plt.figure(figsize=(30, 20))
sns.heatmap(correlation_train, annot=True, fmt='.1f') | code |
33097852/cell_6 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv', header=0)
test.head() | code |
33097852/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv', header=0)
pd.set_option('display.max_rows', 100)
pd.set_option('display.max_columns', 100)
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
var = 'TotalBsmtSF'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
#box plot overallqual/saleprice
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
var = 'YearBuilt'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(16, 8))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
plt.xticks(rotation=90);
sns.set(font_scale=1)
correlation_train = train.corr()
train.corr()
train_total = train.isnull().sum().sort_values(ascending=False)
train_percent = (train.isnull().sum() / train.isnull().count()).sort_values(ascending=False)
train_missing_data = pd.concat([train_total, train_percent], axis=1, keys=['Total', 'Percent'])
test_total = test.isnull().sum().sort_values(ascending=False)
test_percent = (test.isnull().sum() / test.isnull().count()).sort_values(ascending=False)
test_missing_data = pd.concat([test_total, test_percent], axis=1, keys=['Total', 'Percent'])
test_missing_data.head(35) | code |
33097852/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv', header=0)
pd.set_option('display.max_rows', 100)
pd.set_option('display.max_columns', 100)
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
var = 'TotalBsmtSF'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
#box plot overallqual/saleprice
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
var = 'YearBuilt'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(16, 8))
fig = sns.boxplot(x=var, y='SalePrice', data=data)
fig.axis(ymin=0, ymax=800000)
plt.xticks(rotation=90) | code |
33097852/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv', header=0)
sample_submission.head() | code |
33097852/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv', header=0)
pd.set_option('display.max_rows', 100)
pd.set_option('display.max_columns', 100)
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
var = 'TotalBsmtSF'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y='SalePrice', data=data)
fig.axis(ymin=0, ymax=800000) | code |
33097852/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv', header=0)
print(train.shape)
print(test.shape)
print(sample_submission.shape) | code |
33097852/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv', header=0)
pd.set_option('display.max_rows', 100)
pd.set_option('display.max_columns', 100)
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
var = 'TotalBsmtSF'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
data.plot.scatter(x=var, y='SalePrice', ylim=(0, 800000)) | code |
33097852/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv', header=0)
sns.distplot(train['SalePrice']) | code |
33097852/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv', header=0)
train.head() | code |
2031298/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df1['Survived'].value_counts().plot.bar(color='r')
plt.show()
df1['Pclass'].value_counts().plot.bar()
plt.show()
df1['Sex'].value_counts().plot.bar(color='g')
plt.show()
df1['SibSp'].value_counts().plot.bar()
plt.show()
plt.show()
df1['Embarked'].value_counts().plot.bar()
plt.show() | code |
2031298/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df12 = df1.fillna(df1.mean())
i = df1[['Age', 'Fare', 'Survived']]
plt.style.use('seaborn-colorblind')
corr = df1.corr()
freq_1 = pd.crosstab(index=df1['Survived'], columns=df1['Pclass'])
freq_2 = pd.crosstab(index=df1['Survived'], columns=df1['Sex'])
freq_3 = pd.crosstab(index=df1['Survived'], columns=df1['SibSp'])
freq_4 = pd.crosstab(index=df1['Survived'], columns=df1['Parch'])
freq_5 = pd.crosstab(index=df1['Survived'], columns=df1['Pclass'])
freq_6 = pd.crosstab(index=df1['Survived'], columns=df1['Embarked'])
l = [freq_1, freq_2, freq_3, freq_4, freq_5, freq_6]
for i in l:
print(i) | code |
2031298/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df12 = df1.fillna(df1.mean())
i = df1[['Age', 'Fare', 'Survived']]
plt.style.use('seaborn-colorblind')
corr = df1.corr()
sns.heatmap(corr[['Age', 'Fare']], annot=True, linewidth=0.1)
plt.show() | code |
2031298/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df12 = df1.fillna(df1.mean())
plt.figure()
df1.plot.scatter('Age', 'Fare')
plt.show() | code |
2031298/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df1.describe() | code |
2031298/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df1.head() | code |
2031298/cell_8 | [
"image_output_5.png",
"image_output_4.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df2.head() | code |
2031298/cell_16 | [
"image_output_5.png",
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df12 = df1.fillna(df1.mean())
plt.hist(df12.Age, alpha=0.3)
sns.rugplot(df12.Age)
plt.show() | code |
2031298/cell_17 | [
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df12 = df1.fillna(df1.mean())
plt.hist(df12.Fare)
sns.rugplot(df12.Fare, alpha=0.3)
plt.show() | code |
2031298/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df1[['Age', 'Fare']].describe()
df1['Age'].value_counts().plot.hist(grid=True, color='b', alpha=0.7)
plt.show()
df1['Fare'].value_counts().plot.hist(grid=True, color='r')
plt.show() | code |
2031298/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df12 = df1.fillna(df1.mean())
i = df1[['Age', 'Fare', 'Survived']]
plt.style.use('seaborn-colorblind')
pd.tools.plotting.scatter_matrix(i)
plt.show() | code |
2031298/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df1.describe(include=['O']) | code |
1006119/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_extraction import DictVectorizer
from sklearn.feature_extraction import DictVectorizer
vec = DictVectorizer(sparse=False)
x_train = vec.fit_transform(x_train.to_dict(orient='record'))
x_test = vec.transform(x_test.to_dict(orient='record'))
vec.get_feature_names() | code |
1006119/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_extraction import DictVectorizer
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('train.csv')
data_test = pd.read_csv('test.csv')
data_x = data_train[['Pclass', 'Age', 'Sex']]
data_y = data_train['Survived']
age_mean = data_x.mean()['Age']
data_x = data_x.fillna(age_mean)
for i in range(1, 4):
data_x.loc[data_x.Pclass == i, 'Pclass'] = str(i)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(data_x, data_y, test_size=0.25, random_state=33)
y_train.value_counts()
from sklearn.feature_extraction import DictVectorizer
vec = DictVectorizer(sparse=False)
x_train = vec.fit_transform(x_train.to_dict(orient='record'))
x_test = vec.transform(x_test.to_dict(orient='record'))
vec.get_feature_names()
from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier()
dtc.fit(x_train, y_train)
dtc_y_predict = dtc.predict(x_test) | code |
1006119/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_extraction import DictVectorizer
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('train.csv')
data_test = pd.read_csv('test.csv')
data_x = data_train[['Pclass', 'Age', 'Sex']]
data_y = data_train['Survived']
age_mean = data_x.mean()['Age']
data_x = data_x.fillna(age_mean)
for i in range(1, 4):
data_x.loc[data_x.Pclass == i, 'Pclass'] = str(i)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(data_x, data_y, test_size=0.25, random_state=33)
y_train.value_counts()
from sklearn.feature_extraction import DictVectorizer
vec = DictVectorizer(sparse=False)
x_train = vec.fit_transform(x_train.to_dict(orient='record'))
x_test = vec.transform(x_test.to_dict(orient='record'))
vec.get_feature_names()
from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier()
dtc.fit(x_train, y_train)
dtc_y_predict = dtc.predict(x_test)
dtc.score(x_test, y_test)
run_x = data_test[['Pclass', 'Age', 'Sex']]
run_x = run_x.fillna(age_mean)
for i in range(1, 4):
run_x.loc[run_x.Pclass == i, 'Pclass'] = str(i)
run_x = vec.transform(run_x.to_dict(orient='record'))
run_y_predict = dtc.predict(run_x)
pd.DataFrame({'PassengerId': data_test.PassengerId, 'Survived': run_y_predict}).to_csv('gender_submission.csv', index=False) | code |
1006119/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1006119/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('train.csv')
data_test = pd.read_csv('test.csv')
data_x = data_train[['Pclass', 'Age', 'Sex']]
data_y = data_train['Survived']
age_mean = data_x.mean()['Age']
data_x = data_x.fillna(age_mean)
for i in range(1, 4):
data_x.loc[data_x.Pclass == i, 'Pclass'] = str(i)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(data_x, data_y, test_size=0.25, random_state=33)
y_train.value_counts() | code |
1006119/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_extraction import DictVectorizer
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('train.csv')
data_test = pd.read_csv('test.csv')
data_x = data_train[['Pclass', 'Age', 'Sex']]
data_y = data_train['Survived']
age_mean = data_x.mean()['Age']
data_x = data_x.fillna(age_mean)
for i in range(1, 4):
data_x.loc[data_x.Pclass == i, 'Pclass'] = str(i)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(data_x, data_y, test_size=0.25, random_state=33)
y_train.value_counts()
from sklearn.feature_extraction import DictVectorizer
vec = DictVectorizer(sparse=False)
x_train = vec.fit_transform(x_train.to_dict(orient='record'))
x_test = vec.transform(x_test.to_dict(orient='record'))
vec.get_feature_names()
from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier()
dtc.fit(x_train, y_train)
dtc_y_predict = dtc.predict(x_test)
dtc.score(x_test, y_test)
run_x = data_test[['Pclass', 'Age', 'Sex']]
run_x = run_x.fillna(age_mean)
for i in range(1, 4):
run_x.loc[run_x.Pclass == i, 'Pclass'] = str(i)
run_x = vec.transform(run_x.to_dict(orient='record'))
run_y_predict = dtc.predict(run_x) | code |
1006119/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_extraction import DictVectorizer
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('train.csv')
data_test = pd.read_csv('test.csv')
data_x = data_train[['Pclass', 'Age', 'Sex']]
data_y = data_train['Survived']
age_mean = data_x.mean()['Age']
data_x = data_x.fillna(age_mean)
for i in range(1, 4):
data_x.loc[data_x.Pclass == i, 'Pclass'] = str(i)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(data_x, data_y, test_size=0.25, random_state=33)
y_train.value_counts()
from sklearn.feature_extraction import DictVectorizer
vec = DictVectorizer(sparse=False)
x_train = vec.fit_transform(x_train.to_dict(orient='record'))
x_test = vec.transform(x_test.to_dict(orient='record'))
vec.get_feature_names()
from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier()
dtc.fit(x_train, y_train)
dtc_y_predict = dtc.predict(x_test)
dtc.score(x_test, y_test) | code |
1006119/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_extraction import DictVectorizer
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('train.csv')
data_test = pd.read_csv('test.csv')
data_x = data_train[['Pclass', 'Age', 'Sex']]
data_y = data_train['Survived']
age_mean = data_x.mean()['Age']
data_x = data_x.fillna(age_mean)
for i in range(1, 4):
data_x.loc[data_x.Pclass == i, 'Pclass'] = str(i)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(data_x, data_y, test_size=0.25, random_state=33)
y_train.value_counts()
from sklearn.feature_extraction import DictVectorizer
vec = DictVectorizer(sparse=False)
x_train = vec.fit_transform(x_train.to_dict(orient='record'))
x_test = vec.transform(x_test.to_dict(orient='record'))
vec.get_feature_names()
from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier()
dtc.fit(x_train, y_train)
dtc_y_predict = dtc.predict(x_test)
print(classification_report(y_test, dtc_y_predict, target_names=['died', 'surived'])) | code |
1006119/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('train.csv')
data_test = pd.read_csv('test.csv') | code |
1006119/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('train.csv')
data_test = pd.read_csv('test.csv')
data_x = data_train[['Pclass', 'Age', 'Sex']]
data_y = data_train['Survived']
age_mean = data_x.mean()['Age']
data_x = data_x.fillna(age_mean)
for i in range(1, 4):
data_x.loc[data_x.Pclass == i, 'Pclass'] = str(i)
data_x.head() | code |
33101666/cell_13 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from fastai.vision import *
data = ImageList.from_folder('/kaggle/input/turkish-lira-banknote-dataset/').random_split_by_pct().label_from_folder().transform(get_transforms(), size=122).databunch()
learner = cnn_learner(data, models.resnet18, metrics=accuracy)
learner.fit(5)
interp = ClassificationInterpretation.from_learner(learner)
learner2 = cnn_learner(data, models.resnet152, metrics=accuracy)
learner2.fit(3)
interp2 = ClassificationInterpretation.from_learner(learner2)
interp2.plot_top_losses(9, figsize=(10, 10)) | code |
33101666/cell_9 | [
"image_output_1.png"
] | from fastai.vision import *
data = ImageList.from_folder('/kaggle/input/turkish-lira-banknote-dataset/').random_split_by_pct().label_from_folder().transform(get_transforms(), size=122).databunch()
learner = cnn_learner(data, models.resnet18, metrics=accuracy)
learner.fit(5)
interp = ClassificationInterpretation.from_learner(learner)
interp.most_confused() | code |
33101666/cell_4 | [
"image_output_1.png"
] | from fastai.vision import *
data = ImageList.from_folder('/kaggle/input/turkish-lira-banknote-dataset/').random_split_by_pct().label_from_folder().transform(get_transforms(), size=122).databunch()
learner = cnn_learner(data, models.resnet18, metrics=accuracy)
learner.fit(5) | code |
33101666/cell_2 | [
"image_output_1.png"
] | from fastai.vision import *
data = ImageList.from_folder('/kaggle/input/turkish-lira-banknote-dataset/').random_split_by_pct().label_from_folder().transform(get_transforms(), size=122).databunch() | code |
33101666/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
break | code |
33101666/cell_7 | [
"text_plain_output_1.png"
] | from fastai.vision import *
data = ImageList.from_folder('/kaggle/input/turkish-lira-banknote-dataset/').random_split_by_pct().label_from_folder().transform(get_transforms(), size=122).databunch()
learner = cnn_learner(data, models.resnet18, metrics=accuracy)
learner.fit(5)
interp = ClassificationInterpretation.from_learner(learner)
interp.plot_confusion_matrix() | code |
33101666/cell_15 | [
"image_output_1.png"
] | from fastai.vision import *
data = ImageList.from_folder('/kaggle/input/turkish-lira-banknote-dataset/').random_split_by_pct().label_from_folder().transform(get_transforms(), size=122).databunch()
learner = cnn_learner(data, models.resnet18, metrics=accuracy)
learner.fit(5)
interp = ClassificationInterpretation.from_learner(learner)
learner2 = cnn_learner(data, models.resnet152, metrics=accuracy)
learner2.fit(3)
interp2 = ClassificationInterpretation.from_learner(learner2)
interp2.most_confused() | code |
33101666/cell_3 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from fastai.vision import *
data = ImageList.from_folder('/kaggle/input/turkish-lira-banknote-dataset/').random_split_by_pct().label_from_folder().transform(get_transforms(), size=122).databunch()
data.show_batch() | code |
33101666/cell_17 | [
"text_plain_output_1.png"
] | from fastai.vision import *
data = ImageList.from_folder('/kaggle/input/turkish-lira-banknote-dataset/').random_split_by_pct().label_from_folder().transform(get_transforms(), size=122).databunch()
learner = cnn_learner(data, models.resnet18, metrics=accuracy)
learner.fit(5)
interp = ClassificationInterpretation.from_learner(learner)
learner2 = cnn_learner(data, models.resnet152, metrics=accuracy)
learner2.fit(3)
interp2 = ClassificationInterpretation.from_learner(learner2)
interp2.most_confused()
interp2.plot_top_losses(k=8) | code |
33101666/cell_14 | [
"image_output_1.png"
] | from fastai.vision import *
data = ImageList.from_folder('/kaggle/input/turkish-lira-banknote-dataset/').random_split_by_pct().label_from_folder().transform(get_transforms(), size=122).databunch()
learner = cnn_learner(data, models.resnet18, metrics=accuracy)
learner.fit(5)
interp = ClassificationInterpretation.from_learner(learner)
learner2 = cnn_learner(data, models.resnet152, metrics=accuracy)
learner2.fit(3)
interp2 = ClassificationInterpretation.from_learner(learner2)
interp2.plot_confusion_matrix() | code |
33101666/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from fastai.vision import *
data = ImageList.from_folder('/kaggle/input/turkish-lira-banknote-dataset/').random_split_by_pct().label_from_folder().transform(get_transforms(), size=122).databunch()
learner = cnn_learner(data, models.resnet18, metrics=accuracy)
learner.fit(5)
interp = ClassificationInterpretation.from_learner(learner)
interp.most_confused()
interp.plot_top_losses(k=8) | code |
33101666/cell_12 | [
"image_output_1.png"
] | from fastai.vision import *
data = ImageList.from_folder('/kaggle/input/turkish-lira-banknote-dataset/').random_split_by_pct().label_from_folder().transform(get_transforms(), size=122).databunch()
learner = cnn_learner(data, models.resnet18, metrics=accuracy)
learner.fit(5)
learner2 = cnn_learner(data, models.resnet152, metrics=accuracy)
learner2.fit(3) | code |
33101666/cell_5 | [
"image_output_1.png"
] | from fastai.vision import *
data = ImageList.from_folder('/kaggle/input/turkish-lira-banknote-dataset/').random_split_by_pct().label_from_folder().transform(get_transforms(), size=122).databunch()
learner = cnn_learner(data, models.resnet18, metrics=accuracy)
learner.fit(5)
interp = ClassificationInterpretation.from_learner(learner)
interp.plot_top_losses(9, figsize=(10, 10)) | code |
320604/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
c.execute(s)
result = c.fetchall()
conn.close()
return result
def print_details():
"""Print database details including table names and the number of rows.
"""
table_names = sql_query('SELECT name FROM sqlite_master ' + "WHERE type='table' " + 'ORDER BY name;')[0][0]
num_rows = sql_query('SELECT COUNT(*) FROM loan;')[0][0]
def print_column_names():
"""Print the column names in the 'loan' table.
Note that the "index" column name is specific to Python and is not part of
the original SQLite database.
"""
conn = sqlite3.connect('../input/database.sqlite')
conn.row_factory = sqlite3.Row
c = conn.cursor()
c.execute('SELECT * FROM loan LIMIT 2;')
r = c.fetchone()
i = 1
for k in r.keys():
i += 1
conn.close()
emp_length_dict = {'n/a': 0, '< 1 year': 0, '1 year': 1, '2 years': 2, '3 years': 3, '4 years': 4, '5 years': 5, '6 years': 6, '7 years': 7, '8 years': 8, '9 years': 9, '10+ years': 10}
home_ownership_dict = {'MORTGAGE': 0, 'OWN': 1, 'RENT': 2, 'OTHER': 3, 'NONE': 4, 'ANY': 5}
features_dict = {'loan_amnt': 0, 'int_rate': 1, 'annual_inc': 2, 'delinq_2yrs': 3, 'open_acc': 4, 'dti': 5, 'emp_length': 6, 'funded_amnt': 7, 'tot_cur_bal': 8, 'home_ownership': 9}
def get_data(s):
"""Return features and targets for a specific search term.
Arguments:
s (str) -- string to search for in loan "title" field
Returns:
(list of lists) -- [list of feature tuples, list of targets]
(features) -- [(sample1 features), (sample2 features),...]
(target) -- [sample1 target, sample2 target,...]
"""
data = sql_query('SELECT ' + 'loan_amnt,int_rate,annual_inc,' + 'loan_status,title,delinq_2yrs,' + 'open_acc,dti,emp_length,' + 'funded_amnt,tot_cur_bal,home_ownership ' + 'FROM loan ' + "WHERE application_type='INDIVIDUAL';")
features_list = []
target_list = []
n = 0
n0 = 0
n1 = 0
for d in data:
test0 = isinstance(d[0], float)
test1 = isinstance(d[1], str)
test2 = isinstance(d[2], float)
test3 = isinstance(d[3], str)
test4 = isinstance(d[4], str)
test5 = isinstance(d[5], float)
test6 = isinstance(d[6], float)
test7 = isinstance(d[7], float)
test8 = isinstance(d[8], str)
test9 = isinstance(d[9], float)
test10 = isinstance(d[10], float)
if test0 and test1 and test2 and test3 and test4 and test5 and test6 and test7 and test8 and test9 and test10:
try:
d1_float = float(d[1].replace('%', ''))
except:
continue
try:
e = emp_length_dict[d[8]]
except:
continue
try:
h = home_ownership_dict[d[11]]
except:
continue
if s.lower() in d[4].lower():
if d[3] == 'Fully Paid' or d[3] == 'Current':
target = 0
n += 1
n0 += 1
elif 'Late' in d[3] or d[3] == 'Charged Off':
target = 1
n += 1
n1 += 1
else:
continue
features = (d[0], float(d[1].replace('%', '')), d[2], d[5], d[6], d[7], emp_length_dict[d[8]], d[9], d[10], home_ownership_dict[d[11]])
features_list.append(features)
target_list.append(target)
else:
pass
result = [features_list, target_list]
return result
def create_scatter_plot(x0_data, y0_data, x1_data, y1_data, pt, pa, x_label, y_label, axis_type):
ax = plt.gca()
ax.set_axis_bgcolor('#BBBBBB')
ax.set_axisbelow(True)
plt.axis(pa)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
if axis_type == 'semilogx':
plt.semilogx(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogx(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'semilogy':
plt.semilogy(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogy(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'loglog':
plt.loglog(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.loglog(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
plt.clf()
def plot_two_fields(data, s, f1, f2, pa, x_label, y_label, axis_type):
x0_list = []
y0_list = []
x1_list = []
y1_list = []
features_list = data[0]
target_list = data[1]
for i in range(len(features_list)):
x = features_list[i][features_dict[f1]]
y = features_list[i][features_dict[f2]]
if target_list[i] == 0:
x0_list.append(x)
y0_list.append(y)
elif target_list[i] == 1:
x1_list.append(x)
y1_list.append(y)
else:
pass
debt_data = get_data('debt')
plot_two_fields(debt_data, 'debt', 'loan_amnt', 'int_rate', [100.0, 100000.0, 5.0, 30.0], 'loan amount', 'interest rate', 'semilogx') | code |
320604/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
c.execute(s)
result = c.fetchall()
conn.close()
return result
def print_details():
"""Print database details including table names and the number of rows.
"""
table_names = sql_query('SELECT name FROM sqlite_master ' + "WHERE type='table' " + 'ORDER BY name;')[0][0]
num_rows = sql_query('SELECT COUNT(*) FROM loan;')[0][0]
def print_column_names():
"""Print the column names in the 'loan' table.
Note that the "index" column name is specific to Python and is not part of
the original SQLite database.
"""
conn = sqlite3.connect('../input/database.sqlite')
conn.row_factory = sqlite3.Row
c = conn.cursor()
c.execute('SELECT * FROM loan LIMIT 2;')
r = c.fetchone()
i = 1
for k in r.keys():
i += 1
conn.close()
emp_length_dict = {'n/a': 0, '< 1 year': 0, '1 year': 1, '2 years': 2, '3 years': 3, '4 years': 4, '5 years': 5, '6 years': 6, '7 years': 7, '8 years': 8, '9 years': 9, '10+ years': 10}
home_ownership_dict = {'MORTGAGE': 0, 'OWN': 1, 'RENT': 2, 'OTHER': 3, 'NONE': 4, 'ANY': 5}
features_dict = {'loan_amnt': 0, 'int_rate': 1, 'annual_inc': 2, 'delinq_2yrs': 3, 'open_acc': 4, 'dti': 5, 'emp_length': 6, 'funded_amnt': 7, 'tot_cur_bal': 8, 'home_ownership': 9}
def get_data(s):
"""Return features and targets for a specific search term.
Arguments:
s (str) -- string to search for in loan "title" field
Returns:
(list of lists) -- [list of feature tuples, list of targets]
(features) -- [(sample1 features), (sample2 features),...]
(target) -- [sample1 target, sample2 target,...]
"""
data = sql_query('SELECT ' + 'loan_amnt,int_rate,annual_inc,' + 'loan_status,title,delinq_2yrs,' + 'open_acc,dti,emp_length,' + 'funded_amnt,tot_cur_bal,home_ownership ' + 'FROM loan ' + "WHERE application_type='INDIVIDUAL';")
features_list = []
target_list = []
n = 0
n0 = 0
n1 = 0
for d in data:
test0 = isinstance(d[0], float)
test1 = isinstance(d[1], str)
test2 = isinstance(d[2], float)
test3 = isinstance(d[3], str)
test4 = isinstance(d[4], str)
test5 = isinstance(d[5], float)
test6 = isinstance(d[6], float)
test7 = isinstance(d[7], float)
test8 = isinstance(d[8], str)
test9 = isinstance(d[9], float)
test10 = isinstance(d[10], float)
if test0 and test1 and test2 and test3 and test4 and test5 and test6 and test7 and test8 and test9 and test10:
try:
d1_float = float(d[1].replace('%', ''))
except:
continue
try:
e = emp_length_dict[d[8]]
except:
continue
try:
h = home_ownership_dict[d[11]]
except:
continue
if s.lower() in d[4].lower():
if d[3] == 'Fully Paid' or d[3] == 'Current':
target = 0
n += 1
n0 += 1
elif 'Late' in d[3] or d[3] == 'Charged Off':
target = 1
n += 1
n1 += 1
else:
continue
features = (d[0], float(d[1].replace('%', '')), d[2], d[5], d[6], d[7], emp_length_dict[d[8]], d[9], d[10], home_ownership_dict[d[11]])
features_list.append(features)
target_list.append(target)
else:
pass
result = [features_list, target_list]
return result
def create_scatter_plot(x0_data, y0_data, x1_data, y1_data, pt, pa, x_label, y_label, axis_type):
ax = plt.gca()
ax.set_axis_bgcolor('#BBBBBB')
ax.set_axisbelow(True)
plt.axis(pa)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
if axis_type == 'semilogx':
plt.semilogx(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogx(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'semilogy':
plt.semilogy(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogy(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'loglog':
plt.loglog(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.loglog(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
plt.clf()
def plot_two_fields(data, s, f1, f2, pa, x_label, y_label, axis_type):
x0_list = []
y0_list = []
x1_list = []
y1_list = []
features_list = data[0]
target_list = data[1]
for i in range(len(features_list)):
x = features_list[i][features_dict[f1]]
y = features_list[i][features_dict[f2]]
if target_list[i] == 0:
x0_list.append(x)
y0_list.append(y)
elif target_list[i] == 1:
x1_list.append(x)
y1_list.append(y)
else:
pass
medical_data = get_data('medical') | code |
320604/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
c.execute(s)
result = c.fetchall()
conn.close()
return result
def print_details():
"""Print database details including table names and the number of rows.
"""
table_names = sql_query('SELECT name FROM sqlite_master ' + "WHERE type='table' " + 'ORDER BY name;')[0][0]
num_rows = sql_query('SELECT COUNT(*) FROM loan;')[0][0]
def print_column_names():
"""Print the column names in the 'loan' table.
Note that the "index" column name is specific to Python and is not part of
the original SQLite database.
"""
conn = sqlite3.connect('../input/database.sqlite')
conn.row_factory = sqlite3.Row
c = conn.cursor()
c.execute('SELECT * FROM loan LIMIT 2;')
r = c.fetchone()
i = 1
for k in r.keys():
i += 1
conn.close()
emp_length_dict = {'n/a': 0, '< 1 year': 0, '1 year': 1, '2 years': 2, '3 years': 3, '4 years': 4, '5 years': 5, '6 years': 6, '7 years': 7, '8 years': 8, '9 years': 9, '10+ years': 10}
home_ownership_dict = {'MORTGAGE': 0, 'OWN': 1, 'RENT': 2, 'OTHER': 3, 'NONE': 4, 'ANY': 5}
features_dict = {'loan_amnt': 0, 'int_rate': 1, 'annual_inc': 2, 'delinq_2yrs': 3, 'open_acc': 4, 'dti': 5, 'emp_length': 6, 'funded_amnt': 7, 'tot_cur_bal': 8, 'home_ownership': 9}
def get_data(s):
"""Return features and targets for a specific search term.
Arguments:
s (str) -- string to search for in loan "title" field
Returns:
(list of lists) -- [list of feature tuples, list of targets]
(features) -- [(sample1 features), (sample2 features),...]
(target) -- [sample1 target, sample2 target,...]
"""
data = sql_query('SELECT ' + 'loan_amnt,int_rate,annual_inc,' + 'loan_status,title,delinq_2yrs,' + 'open_acc,dti,emp_length,' + 'funded_amnt,tot_cur_bal,home_ownership ' + 'FROM loan ' + "WHERE application_type='INDIVIDUAL';")
features_list = []
target_list = []
n = 0
n0 = 0
n1 = 0
for d in data:
test0 = isinstance(d[0], float)
test1 = isinstance(d[1], str)
test2 = isinstance(d[2], float)
test3 = isinstance(d[3], str)
test4 = isinstance(d[4], str)
test5 = isinstance(d[5], float)
test6 = isinstance(d[6], float)
test7 = isinstance(d[7], float)
test8 = isinstance(d[8], str)
test9 = isinstance(d[9], float)
test10 = isinstance(d[10], float)
if test0 and test1 and test2 and test3 and test4 and test5 and test6 and test7 and test8 and test9 and test10:
try:
d1_float = float(d[1].replace('%', ''))
except:
continue
try:
e = emp_length_dict[d[8]]
except:
continue
try:
h = home_ownership_dict[d[11]]
except:
continue
if s.lower() in d[4].lower():
if d[3] == 'Fully Paid' or d[3] == 'Current':
target = 0
n += 1
n0 += 1
elif 'Late' in d[3] or d[3] == 'Charged Off':
target = 1
n += 1
n1 += 1
else:
continue
features = (d[0], float(d[1].replace('%', '')), d[2], d[5], d[6], d[7], emp_length_dict[d[8]], d[9], d[10], home_ownership_dict[d[11]])
features_list.append(features)
target_list.append(target)
else:
pass
result = [features_list, target_list]
return result
def create_scatter_plot(x0_data, y0_data, x1_data, y1_data, pt, pa, x_label, y_label, axis_type):
ax = plt.gca()
ax.set_axis_bgcolor('#BBBBBB')
ax.set_axisbelow(True)
plt.axis(pa)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
if axis_type == 'semilogx':
plt.semilogx(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogx(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'semilogy':
plt.semilogy(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogy(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'loglog':
plt.loglog(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.loglog(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
plt.clf()
def plot_two_fields(data, s, f1, f2, pa, x_label, y_label, axis_type):
x0_list = []
y0_list = []
x1_list = []
y1_list = []
features_list = data[0]
target_list = data[1]
for i in range(len(features_list)):
x = features_list[i][features_dict[f1]]
y = features_list[i][features_dict[f2]]
if target_list[i] == 0:
x0_list.append(x)
y0_list.append(y)
elif target_list[i] == 1:
x1_list.append(x)
y1_list.append(y)
else:
pass
cc_data = get_data('credit card')
plot_two_fields(cc_data, 'credit card', 'annual_inc', 'loan_amnt', [1000.0, 10000000.0, 0.0, 35000.0], 'annual income', 'loan amount', 'semilogx') | code |
320604/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
c.execute(s)
result = c.fetchall()
conn.close()
return result
def print_details():
"""Print database details including table names and the number of rows.
"""
table_names = sql_query('SELECT name FROM sqlite_master ' + "WHERE type='table' " + 'ORDER BY name;')[0][0]
num_rows = sql_query('SELECT COUNT(*) FROM loan;')[0][0]
def print_column_names():
"""Print the column names in the 'loan' table.
Note that the "index" column name is specific to Python and is not part of
the original SQLite database.
"""
conn = sqlite3.connect('../input/database.sqlite')
conn.row_factory = sqlite3.Row
c = conn.cursor()
c.execute('SELECT * FROM loan LIMIT 2;')
r = c.fetchone()
i = 1
for k in r.keys():
i += 1
conn.close()
emp_length_dict = {'n/a': 0, '< 1 year': 0, '1 year': 1, '2 years': 2, '3 years': 3, '4 years': 4, '5 years': 5, '6 years': 6, '7 years': 7, '8 years': 8, '9 years': 9, '10+ years': 10}
home_ownership_dict = {'MORTGAGE': 0, 'OWN': 1, 'RENT': 2, 'OTHER': 3, 'NONE': 4, 'ANY': 5}
features_dict = {'loan_amnt': 0, 'int_rate': 1, 'annual_inc': 2, 'delinq_2yrs': 3, 'open_acc': 4, 'dti': 5, 'emp_length': 6, 'funded_amnt': 7, 'tot_cur_bal': 8, 'home_ownership': 9}
def get_data(s):
"""Return features and targets for a specific search term.
Arguments:
s (str) -- string to search for in loan "title" field
Returns:
(list of lists) -- [list of feature tuples, list of targets]
(features) -- [(sample1 features), (sample2 features),...]
(target) -- [sample1 target, sample2 target,...]
"""
data = sql_query('SELECT ' + 'loan_amnt,int_rate,annual_inc,' + 'loan_status,title,delinq_2yrs,' + 'open_acc,dti,emp_length,' + 'funded_amnt,tot_cur_bal,home_ownership ' + 'FROM loan ' + "WHERE application_type='INDIVIDUAL';")
features_list = []
target_list = []
n = 0
n0 = 0
n1 = 0
for d in data:
test0 = isinstance(d[0], float)
test1 = isinstance(d[1], str)
test2 = isinstance(d[2], float)
test3 = isinstance(d[3], str)
test4 = isinstance(d[4], str)
test5 = isinstance(d[5], float)
test6 = isinstance(d[6], float)
test7 = isinstance(d[7], float)
test8 = isinstance(d[8], str)
test9 = isinstance(d[9], float)
test10 = isinstance(d[10], float)
if test0 and test1 and test2 and test3 and test4 and test5 and test6 and test7 and test8 and test9 and test10:
try:
d1_float = float(d[1].replace('%', ''))
except:
continue
try:
e = emp_length_dict[d[8]]
except:
continue
try:
h = home_ownership_dict[d[11]]
except:
continue
if s.lower() in d[4].lower():
if d[3] == 'Fully Paid' or d[3] == 'Current':
target = 0
n += 1
n0 += 1
elif 'Late' in d[3] or d[3] == 'Charged Off':
target = 1
n += 1
n1 += 1
else:
continue
features = (d[0], float(d[1].replace('%', '')), d[2], d[5], d[6], d[7], emp_length_dict[d[8]], d[9], d[10], home_ownership_dict[d[11]])
features_list.append(features)
target_list.append(target)
else:
pass
result = [features_list, target_list]
return result
def create_scatter_plot(x0_data, y0_data, x1_data, y1_data, pt, pa, x_label, y_label, axis_type):
ax = plt.gca()
ax.set_axis_bgcolor('#BBBBBB')
ax.set_axisbelow(True)
plt.axis(pa)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
if axis_type == 'semilogx':
plt.semilogx(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogx(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'semilogy':
plt.semilogy(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogy(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'loglog':
plt.loglog(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.loglog(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
plt.clf()
def plot_two_fields(data, s, f1, f2, pa, x_label, y_label, axis_type):
x0_list = []
y0_list = []
x1_list = []
y1_list = []
features_list = data[0]
target_list = data[1]
for i in range(len(features_list)):
x = features_list[i][features_dict[f1]]
y = features_list[i][features_dict[f2]]
if target_list[i] == 0:
x0_list.append(x)
y0_list.append(y)
elif target_list[i] == 1:
x1_list.append(x)
y1_list.append(y)
else:
pass
debt_data = get_data('debt')
plot_two_fields(debt_data, 'debt', 'home_ownership', 'funded_amnt', [-1, 6, 0.0, 35000.0], 'home ownership', 'funded amount', 'standard') | code |
320604/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
c.execute(s)
result = c.fetchall()
conn.close()
return result
def print_details():
"""Print database details including table names and the number of rows.
"""
table_names = sql_query('SELECT name FROM sqlite_master ' + "WHERE type='table' " + 'ORDER BY name;')[0][0]
num_rows = sql_query('SELECT COUNT(*) FROM loan;')[0][0]
def print_column_names():
"""Print the column names in the 'loan' table.
Note that the "index" column name is specific to Python and is not part of
the original SQLite database.
"""
conn = sqlite3.connect('../input/database.sqlite')
conn.row_factory = sqlite3.Row
c = conn.cursor()
c.execute('SELECT * FROM loan LIMIT 2;')
r = c.fetchone()
i = 1
for k in r.keys():
i += 1
conn.close()
emp_length_dict = {'n/a': 0, '< 1 year': 0, '1 year': 1, '2 years': 2, '3 years': 3, '4 years': 4, '5 years': 5, '6 years': 6, '7 years': 7, '8 years': 8, '9 years': 9, '10+ years': 10}
home_ownership_dict = {'MORTGAGE': 0, 'OWN': 1, 'RENT': 2, 'OTHER': 3, 'NONE': 4, 'ANY': 5}
features_dict = {'loan_amnt': 0, 'int_rate': 1, 'annual_inc': 2, 'delinq_2yrs': 3, 'open_acc': 4, 'dti': 5, 'emp_length': 6, 'funded_amnt': 7, 'tot_cur_bal': 8, 'home_ownership': 9}
def get_data(s):
"""Return features and targets for a specific search term.
Arguments:
s (str) -- string to search for in loan "title" field
Returns:
(list of lists) -- [list of feature tuples, list of targets]
(features) -- [(sample1 features), (sample2 features),...]
(target) -- [sample1 target, sample2 target,...]
"""
data = sql_query('SELECT ' + 'loan_amnt,int_rate,annual_inc,' + 'loan_status,title,delinq_2yrs,' + 'open_acc,dti,emp_length,' + 'funded_amnt,tot_cur_bal,home_ownership ' + 'FROM loan ' + "WHERE application_type='INDIVIDUAL';")
features_list = []
target_list = []
n = 0
n0 = 0
n1 = 0
for d in data:
test0 = isinstance(d[0], float)
test1 = isinstance(d[1], str)
test2 = isinstance(d[2], float)
test3 = isinstance(d[3], str)
test4 = isinstance(d[4], str)
test5 = isinstance(d[5], float)
test6 = isinstance(d[6], float)
test7 = isinstance(d[7], float)
test8 = isinstance(d[8], str)
test9 = isinstance(d[9], float)
test10 = isinstance(d[10], float)
if test0 and test1 and test2 and test3 and test4 and test5 and test6 and test7 and test8 and test9 and test10:
try:
d1_float = float(d[1].replace('%', ''))
except:
continue
try:
e = emp_length_dict[d[8]]
except:
continue
try:
h = home_ownership_dict[d[11]]
except:
continue
if s.lower() in d[4].lower():
if d[3] == 'Fully Paid' or d[3] == 'Current':
target = 0
n += 1
n0 += 1
elif 'Late' in d[3] or d[3] == 'Charged Off':
target = 1
n += 1
n1 += 1
else:
continue
features = (d[0], float(d[1].replace('%', '')), d[2], d[5], d[6], d[7], emp_length_dict[d[8]], d[9], d[10], home_ownership_dict[d[11]])
features_list.append(features)
target_list.append(target)
else:
pass
result = [features_list, target_list]
return result
def create_scatter_plot(x0_data, y0_data, x1_data, y1_data, pt, pa, x_label, y_label, axis_type):
ax = plt.gca()
ax.set_axis_bgcolor('#BBBBBB')
ax.set_axisbelow(True)
plt.axis(pa)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
if axis_type == 'semilogx':
plt.semilogx(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogx(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'semilogy':
plt.semilogy(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogy(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'loglog':
plt.loglog(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.loglog(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
plt.clf()
def plot_two_fields(data, s, f1, f2, pa, x_label, y_label, axis_type):
x0_list = []
y0_list = []
x1_list = []
y1_list = []
features_list = data[0]
target_list = data[1]
for i in range(len(features_list)):
x = features_list[i][features_dict[f1]]
y = features_list[i][features_dict[f2]]
if target_list[i] == 0:
x0_list.append(x)
y0_list.append(y)
elif target_list[i] == 1:
x1_list.append(x)
y1_list.append(y)
else:
pass
debt_data = get_data('debt')
plot_two_fields(debt_data, 'debt', 'annual_inc', 'loan_amnt', [1000.0, 10000000.0, 0.0, 35000.0], 'annual income', 'loan amount', 'semilogx') | code |
320604/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import tree
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
c.execute(s)
result = c.fetchall()
conn.close()
return result
def print_details():
"""Print database details including table names and the number of rows.
"""
table_names = sql_query('SELECT name FROM sqlite_master ' + "WHERE type='table' " + 'ORDER BY name;')[0][0]
num_rows = sql_query('SELECT COUNT(*) FROM loan;')[0][0]
def print_column_names():
"""Print the column names in the 'loan' table.
Note that the "index" column name is specific to Python and is not part of
the original SQLite database.
"""
conn = sqlite3.connect('../input/database.sqlite')
conn.row_factory = sqlite3.Row
c = conn.cursor()
c.execute('SELECT * FROM loan LIMIT 2;')
r = c.fetchone()
i = 1
for k in r.keys():
i += 1
conn.close()
emp_length_dict = {'n/a': 0, '< 1 year': 0, '1 year': 1, '2 years': 2, '3 years': 3, '4 years': 4, '5 years': 5, '6 years': 6, '7 years': 7, '8 years': 8, '9 years': 9, '10+ years': 10}
home_ownership_dict = {'MORTGAGE': 0, 'OWN': 1, 'RENT': 2, 'OTHER': 3, 'NONE': 4, 'ANY': 5}
features_dict = {'loan_amnt': 0, 'int_rate': 1, 'annual_inc': 2, 'delinq_2yrs': 3, 'open_acc': 4, 'dti': 5, 'emp_length': 6, 'funded_amnt': 7, 'tot_cur_bal': 8, 'home_ownership': 9}
def get_data(s):
"""Return features and targets for a specific search term.
Arguments:
s (str) -- string to search for in loan "title" field
Returns:
(list of lists) -- [list of feature tuples, list of targets]
(features) -- [(sample1 features), (sample2 features),...]
(target) -- [sample1 target, sample2 target,...]
"""
data = sql_query('SELECT ' + 'loan_amnt,int_rate,annual_inc,' + 'loan_status,title,delinq_2yrs,' + 'open_acc,dti,emp_length,' + 'funded_amnt,tot_cur_bal,home_ownership ' + 'FROM loan ' + "WHERE application_type='INDIVIDUAL';")
features_list = []
target_list = []
n = 0
n0 = 0
n1 = 0
for d in data:
test0 = isinstance(d[0], float)
test1 = isinstance(d[1], str)
test2 = isinstance(d[2], float)
test3 = isinstance(d[3], str)
test4 = isinstance(d[4], str)
test5 = isinstance(d[5], float)
test6 = isinstance(d[6], float)
test7 = isinstance(d[7], float)
test8 = isinstance(d[8], str)
test9 = isinstance(d[9], float)
test10 = isinstance(d[10], float)
if test0 and test1 and test2 and test3 and test4 and test5 and test6 and test7 and test8 and test9 and test10:
try:
d1_float = float(d[1].replace('%', ''))
except:
continue
try:
e = emp_length_dict[d[8]]
except:
continue
try:
h = home_ownership_dict[d[11]]
except:
continue
if s.lower() in d[4].lower():
if d[3] == 'Fully Paid' or d[3] == 'Current':
target = 0
n += 1
n0 += 1
elif 'Late' in d[3] or d[3] == 'Charged Off':
target = 1
n += 1
n1 += 1
else:
continue
features = (d[0], float(d[1].replace('%', '')), d[2], d[5], d[6], d[7], emp_length_dict[d[8]], d[9], d[10], home_ownership_dict[d[11]])
features_list.append(features)
target_list.append(target)
else:
pass
result = [features_list, target_list]
return result
def create_scatter_plot(x0_data, y0_data, x1_data, y1_data, pt, pa, x_label, y_label, axis_type):
ax = plt.gca()
ax.set_axis_bgcolor('#BBBBBB')
ax.set_axisbelow(True)
plt.axis(pa)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
if axis_type == 'semilogx':
plt.semilogx(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogx(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'semilogy':
plt.semilogy(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogy(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'loglog':
plt.loglog(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.loglog(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
plt.clf()
def plot_two_fields(data, s, f1, f2, pa, x_label, y_label, axis_type):
x0_list = []
y0_list = []
x1_list = []
y1_list = []
features_list = data[0]
target_list = data[1]
for i in range(len(features_list)):
x = features_list[i][features_dict[f1]]
y = features_list[i][features_dict[f2]]
if target_list[i] == 0:
x0_list.append(x)
y0_list.append(y)
elif target_list[i] == 1:
x1_list.append(x)
y1_list.append(y)
else:
pass
debt_data = get_data('debt')
def create_classifier(f, t, nt):
"""Create classifier for predicting loan status. Print accuracy.
Arguments:
f (list of tuples) -- [(sample 1 features), (sample 2 features),...]
t (list) -- [sample 1 target, sample 2 target,...]
nt (int) -- number of samples to use in training set
"""
training_set_features = []
training_set_target = []
testing_set_features = []
testing_set_target = []
for i in np.arange(0, nt, 1):
training_set_features.append(f[i])
training_set_target.append(t[i])
for i in np.arange(nt, len(f), 1):
testing_set_features.append(f[i])
testing_set_target.append(t[i])
clf = tree.DecisionTreeClassifier()
clf = clf.fit(training_set_features, training_set_target)
n = 0
n_correct = 0
n0 = 0
n0_correct = 0
n1 = 0
n1_correct = 0
for i in range(len(testing_set_features)):
t = testing_set_target[i]
p = clf.predict(np.asarray(testing_set_features[i]).reshape(1, -1))
if t == 0:
if t == p[0]:
equal = 'yes'
n_correct += 1
n0_correct += 1
else:
equal = 'no'
n += 1
n0 += 1
elif t == 1:
if t == p[0]:
equal = 'yes'
n_correct += 1
n1_correct += 1
else:
equal = 'no'
n += 1
n1 += 1
else:
pass
n_accuracy = 100.0 * n_correct / n
n0_accuracy = 100.0 * n0_correct / n0
n1_accuracy = 100.0 * n1_correct / n1
create_classifier(debt_data[0], debt_data[1], 2000) | code |
320604/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
c.execute(s)
result = c.fetchall()
conn.close()
return result
def print_details():
"""Print database details including table names and the number of rows.
"""
table_names = sql_query('SELECT name FROM sqlite_master ' + "WHERE type='table' " + 'ORDER BY name;')[0][0]
num_rows = sql_query('SELECT COUNT(*) FROM loan;')[0][0]
def print_column_names():
"""Print the column names in the 'loan' table.
Note that the "index" column name is specific to Python and is not part of
the original SQLite database.
"""
conn = sqlite3.connect('../input/database.sqlite')
conn.row_factory = sqlite3.Row
c = conn.cursor()
c.execute('SELECT * FROM loan LIMIT 2;')
r = c.fetchone()
i = 1
for k in r.keys():
i += 1
conn.close()
emp_length_dict = {'n/a': 0, '< 1 year': 0, '1 year': 1, '2 years': 2, '3 years': 3, '4 years': 4, '5 years': 5, '6 years': 6, '7 years': 7, '8 years': 8, '9 years': 9, '10+ years': 10}
home_ownership_dict = {'MORTGAGE': 0, 'OWN': 1, 'RENT': 2, 'OTHER': 3, 'NONE': 4, 'ANY': 5}
features_dict = {'loan_amnt': 0, 'int_rate': 1, 'annual_inc': 2, 'delinq_2yrs': 3, 'open_acc': 4, 'dti': 5, 'emp_length': 6, 'funded_amnt': 7, 'tot_cur_bal': 8, 'home_ownership': 9}
def get_data(s):
"""Return features and targets for a specific search term.
Arguments:
s (str) -- string to search for in loan "title" field
Returns:
(list of lists) -- [list of feature tuples, list of targets]
(features) -- [(sample1 features), (sample2 features),...]
(target) -- [sample1 target, sample2 target,...]
"""
data = sql_query('SELECT ' + 'loan_amnt,int_rate,annual_inc,' + 'loan_status,title,delinq_2yrs,' + 'open_acc,dti,emp_length,' + 'funded_amnt,tot_cur_bal,home_ownership ' + 'FROM loan ' + "WHERE application_type='INDIVIDUAL';")
features_list = []
target_list = []
n = 0
n0 = 0
n1 = 0
for d in data:
test0 = isinstance(d[0], float)
test1 = isinstance(d[1], str)
test2 = isinstance(d[2], float)
test3 = isinstance(d[3], str)
test4 = isinstance(d[4], str)
test5 = isinstance(d[5], float)
test6 = isinstance(d[6], float)
test7 = isinstance(d[7], float)
test8 = isinstance(d[8], str)
test9 = isinstance(d[9], float)
test10 = isinstance(d[10], float)
if test0 and test1 and test2 and test3 and test4 and test5 and test6 and test7 and test8 and test9 and test10:
try:
d1_float = float(d[1].replace('%', ''))
except:
continue
try:
e = emp_length_dict[d[8]]
except:
continue
try:
h = home_ownership_dict[d[11]]
except:
continue
if s.lower() in d[4].lower():
if d[3] == 'Fully Paid' or d[3] == 'Current':
target = 0
n += 1
n0 += 1
elif 'Late' in d[3] or d[3] == 'Charged Off':
target = 1
n += 1
n1 += 1
else:
continue
features = (d[0], float(d[1].replace('%', '')), d[2], d[5], d[6], d[7], emp_length_dict[d[8]], d[9], d[10], home_ownership_dict[d[11]])
features_list.append(features)
target_list.append(target)
else:
pass
result = [features_list, target_list]
return result
def create_scatter_plot(x0_data, y0_data, x1_data, y1_data, pt, pa, x_label, y_label, axis_type):
ax = plt.gca()
ax.set_axis_bgcolor('#BBBBBB')
ax.set_axisbelow(True)
plt.axis(pa)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
if axis_type == 'semilogx':
plt.semilogx(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogx(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'semilogy':
plt.semilogy(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogy(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'loglog':
plt.loglog(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.loglog(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
plt.clf()
def plot_two_fields(data, s, f1, f2, pa, x_label, y_label, axis_type):
x0_list = []
y0_list = []
x1_list = []
y1_list = []
features_list = data[0]
target_list = data[1]
for i in range(len(features_list)):
x = features_list[i][features_dict[f1]]
y = features_list[i][features_dict[f2]]
if target_list[i] == 0:
x0_list.append(x)
y0_list.append(y)
elif target_list[i] == 1:
x1_list.append(x)
y1_list.append(y)
else:
pass
debt_data = get_data('debt') | code |
320604/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
c.execute(s)
result = c.fetchall()
conn.close()
return result
def print_details():
"""Print database details including table names and the number of rows.
"""
table_names = sql_query('SELECT name FROM sqlite_master ' + "WHERE type='table' " + 'ORDER BY name;')[0][0]
num_rows = sql_query('SELECT COUNT(*) FROM loan;')[0][0]
def print_column_names():
"""Print the column names in the 'loan' table.
Note that the "index" column name is specific to Python and is not part of
the original SQLite database.
"""
conn = sqlite3.connect('../input/database.sqlite')
conn.row_factory = sqlite3.Row
c = conn.cursor()
c.execute('SELECT * FROM loan LIMIT 2;')
r = c.fetchone()
i = 1
for k in r.keys():
i += 1
conn.close()
emp_length_dict = {'n/a': 0, '< 1 year': 0, '1 year': 1, '2 years': 2, '3 years': 3, '4 years': 4, '5 years': 5, '6 years': 6, '7 years': 7, '8 years': 8, '9 years': 9, '10+ years': 10}
home_ownership_dict = {'MORTGAGE': 0, 'OWN': 1, 'RENT': 2, 'OTHER': 3, 'NONE': 4, 'ANY': 5}
features_dict = {'loan_amnt': 0, 'int_rate': 1, 'annual_inc': 2, 'delinq_2yrs': 3, 'open_acc': 4, 'dti': 5, 'emp_length': 6, 'funded_amnt': 7, 'tot_cur_bal': 8, 'home_ownership': 9}
def get_data(s):
"""Return features and targets for a specific search term.
Arguments:
s (str) -- string to search for in loan "title" field
Returns:
(list of lists) -- [list of feature tuples, list of targets]
(features) -- [(sample1 features), (sample2 features),...]
(target) -- [sample1 target, sample2 target,...]
"""
data = sql_query('SELECT ' + 'loan_amnt,int_rate,annual_inc,' + 'loan_status,title,delinq_2yrs,' + 'open_acc,dti,emp_length,' + 'funded_amnt,tot_cur_bal,home_ownership ' + 'FROM loan ' + "WHERE application_type='INDIVIDUAL';")
features_list = []
target_list = []
n = 0
n0 = 0
n1 = 0
for d in data:
test0 = isinstance(d[0], float)
test1 = isinstance(d[1], str)
test2 = isinstance(d[2], float)
test3 = isinstance(d[3], str)
test4 = isinstance(d[4], str)
test5 = isinstance(d[5], float)
test6 = isinstance(d[6], float)
test7 = isinstance(d[7], float)
test8 = isinstance(d[8], str)
test9 = isinstance(d[9], float)
test10 = isinstance(d[10], float)
if test0 and test1 and test2 and test3 and test4 and test5 and test6 and test7 and test8 and test9 and test10:
try:
d1_float = float(d[1].replace('%', ''))
except:
continue
try:
e = emp_length_dict[d[8]]
except:
continue
try:
h = home_ownership_dict[d[11]]
except:
continue
if s.lower() in d[4].lower():
if d[3] == 'Fully Paid' or d[3] == 'Current':
target = 0
n += 1
n0 += 1
elif 'Late' in d[3] or d[3] == 'Charged Off':
target = 1
n += 1
n1 += 1
else:
continue
features = (d[0], float(d[1].replace('%', '')), d[2], d[5], d[6], d[7], emp_length_dict[d[8]], d[9], d[10], home_ownership_dict[d[11]])
features_list.append(features)
target_list.append(target)
else:
pass
result = [features_list, target_list]
return result
def create_scatter_plot(x0_data, y0_data, x1_data, y1_data, pt, pa, x_label, y_label, axis_type):
ax = plt.gca()
ax.set_axis_bgcolor('#BBBBBB')
ax.set_axisbelow(True)
plt.axis(pa)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
if axis_type == 'semilogx':
plt.semilogx(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogx(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'semilogy':
plt.semilogy(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogy(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'loglog':
plt.loglog(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.loglog(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
plt.clf()
def plot_two_fields(data, s, f1, f2, pa, x_label, y_label, axis_type):
x0_list = []
y0_list = []
x1_list = []
y1_list = []
features_list = data[0]
target_list = data[1]
for i in range(len(features_list)):
x = features_list[i][features_dict[f1]]
y = features_list[i][features_dict[f2]]
if target_list[i] == 0:
x0_list.append(x)
y0_list.append(y)
elif target_list[i] == 1:
x1_list.append(x)
y1_list.append(y)
else:
pass
cc_data = get_data('credit card') | code |
320604/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import tree
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
c.execute(s)
result = c.fetchall()
conn.close()
return result
def print_details():
"""Print database details including table names and the number of rows.
"""
table_names = sql_query('SELECT name FROM sqlite_master ' + "WHERE type='table' " + 'ORDER BY name;')[0][0]
num_rows = sql_query('SELECT COUNT(*) FROM loan;')[0][0]
def print_column_names():
"""Print the column names in the 'loan' table.
Note that the "index" column name is specific to Python and is not part of
the original SQLite database.
"""
conn = sqlite3.connect('../input/database.sqlite')
conn.row_factory = sqlite3.Row
c = conn.cursor()
c.execute('SELECT * FROM loan LIMIT 2;')
r = c.fetchone()
i = 1
for k in r.keys():
i += 1
conn.close()
emp_length_dict = {'n/a': 0, '< 1 year': 0, '1 year': 1, '2 years': 2, '3 years': 3, '4 years': 4, '5 years': 5, '6 years': 6, '7 years': 7, '8 years': 8, '9 years': 9, '10+ years': 10}
home_ownership_dict = {'MORTGAGE': 0, 'OWN': 1, 'RENT': 2, 'OTHER': 3, 'NONE': 4, 'ANY': 5}
features_dict = {'loan_amnt': 0, 'int_rate': 1, 'annual_inc': 2, 'delinq_2yrs': 3, 'open_acc': 4, 'dti': 5, 'emp_length': 6, 'funded_amnt': 7, 'tot_cur_bal': 8, 'home_ownership': 9}
def get_data(s):
"""Return features and targets for a specific search term.
Arguments:
s (str) -- string to search for in loan "title" field
Returns:
(list of lists) -- [list of feature tuples, list of targets]
(features) -- [(sample1 features), (sample2 features),...]
(target) -- [sample1 target, sample2 target,...]
"""
data = sql_query('SELECT ' + 'loan_amnt,int_rate,annual_inc,' + 'loan_status,title,delinq_2yrs,' + 'open_acc,dti,emp_length,' + 'funded_amnt,tot_cur_bal,home_ownership ' + 'FROM loan ' + "WHERE application_type='INDIVIDUAL';")
features_list = []
target_list = []
n = 0
n0 = 0
n1 = 0
for d in data:
test0 = isinstance(d[0], float)
test1 = isinstance(d[1], str)
test2 = isinstance(d[2], float)
test3 = isinstance(d[3], str)
test4 = isinstance(d[4], str)
test5 = isinstance(d[5], float)
test6 = isinstance(d[6], float)
test7 = isinstance(d[7], float)
test8 = isinstance(d[8], str)
test9 = isinstance(d[9], float)
test10 = isinstance(d[10], float)
if test0 and test1 and test2 and test3 and test4 and test5 and test6 and test7 and test8 and test9 and test10:
try:
d1_float = float(d[1].replace('%', ''))
except:
continue
try:
e = emp_length_dict[d[8]]
except:
continue
try:
h = home_ownership_dict[d[11]]
except:
continue
if s.lower() in d[4].lower():
if d[3] == 'Fully Paid' or d[3] == 'Current':
target = 0
n += 1
n0 += 1
elif 'Late' in d[3] or d[3] == 'Charged Off':
target = 1
n += 1
n1 += 1
else:
continue
features = (d[0], float(d[1].replace('%', '')), d[2], d[5], d[6], d[7], emp_length_dict[d[8]], d[9], d[10], home_ownership_dict[d[11]])
features_list.append(features)
target_list.append(target)
else:
pass
result = [features_list, target_list]
return result
def create_scatter_plot(x0_data, y0_data, x1_data, y1_data, pt, pa, x_label, y_label, axis_type):
ax = plt.gca()
ax.set_axis_bgcolor('#BBBBBB')
ax.set_axisbelow(True)
plt.axis(pa)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
if axis_type == 'semilogx':
plt.semilogx(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogx(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'semilogy':
plt.semilogy(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogy(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'loglog':
plt.loglog(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.loglog(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
plt.clf()
def plot_two_fields(data, s, f1, f2, pa, x_label, y_label, axis_type):
x0_list = []
y0_list = []
x1_list = []
y1_list = []
features_list = data[0]
target_list = data[1]
for i in range(len(features_list)):
x = features_list[i][features_dict[f1]]
y = features_list[i][features_dict[f2]]
if target_list[i] == 0:
x0_list.append(x)
y0_list.append(y)
elif target_list[i] == 1:
x1_list.append(x)
y1_list.append(y)
else:
pass
cc_data = get_data('credit card')
def create_classifier(f, t, nt):
"""Create classifier for predicting loan status. Print accuracy.
Arguments:
f (list of tuples) -- [(sample 1 features), (sample 2 features),...]
t (list) -- [sample 1 target, sample 2 target,...]
nt (int) -- number of samples to use in training set
"""
training_set_features = []
training_set_target = []
testing_set_features = []
testing_set_target = []
for i in np.arange(0, nt, 1):
training_set_features.append(f[i])
training_set_target.append(t[i])
for i in np.arange(nt, len(f), 1):
testing_set_features.append(f[i])
testing_set_target.append(t[i])
clf = tree.DecisionTreeClassifier()
clf = clf.fit(training_set_features, training_set_target)
n = 0
n_correct = 0
n0 = 0
n0_correct = 0
n1 = 0
n1_correct = 0
for i in range(len(testing_set_features)):
t = testing_set_target[i]
p = clf.predict(np.asarray(testing_set_features[i]).reshape(1, -1))
if t == 0:
if t == p[0]:
equal = 'yes'
n_correct += 1
n0_correct += 1
else:
equal = 'no'
n += 1
n0 += 1
elif t == 1:
if t == p[0]:
equal = 'yes'
n_correct += 1
n1_correct += 1
else:
equal = 'no'
n += 1
n1 += 1
else:
pass
n_accuracy = 100.0 * n_correct / n
n0_accuracy = 100.0 * n0_correct / n0
n1_accuracy = 100.0 * n1_correct / n1
create_classifier(cc_data[0], cc_data[1], 2000) | code |
320604/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
c.execute(s)
result = c.fetchall()
conn.close()
return result
def print_details():
"""Print database details including table names and the number of rows.
"""
table_names = sql_query('SELECT name FROM sqlite_master ' + "WHERE type='table' " + 'ORDER BY name;')[0][0]
print('Names of tables in SQLite database: {0}'.format(table_names))
num_rows = sql_query('SELECT COUNT(*) FROM loan;')[0][0]
print('Number of records in table: {0}'.format(num_rows))
def print_column_names():
"""Print the column names in the 'loan' table.
Note that the "index" column name is specific to Python and is not part of
the original SQLite database.
"""
conn = sqlite3.connect('../input/database.sqlite')
conn.row_factory = sqlite3.Row
c = conn.cursor()
c.execute('SELECT * FROM loan LIMIT 2;')
r = c.fetchone()
i = 1
print('Column names:')
for k in r.keys():
print('{0:d}\t{1}'.format(i, k))
i += 1
conn.close()
print_details()
print_column_names() | code |
320604/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.connect('../input/database.sqlite')
c = conn.cursor()
c.execute(s)
result = c.fetchall()
conn.close()
return result
def print_details():
"""Print database details including table names and the number of rows.
"""
table_names = sql_query('SELECT name FROM sqlite_master ' + "WHERE type='table' " + 'ORDER BY name;')[0][0]
num_rows = sql_query('SELECT COUNT(*) FROM loan;')[0][0]
def print_column_names():
"""Print the column names in the 'loan' table.
Note that the "index" column name is specific to Python and is not part of
the original SQLite database.
"""
conn = sqlite3.connect('../input/database.sqlite')
conn.row_factory = sqlite3.Row
c = conn.cursor()
c.execute('SELECT * FROM loan LIMIT 2;')
r = c.fetchone()
i = 1
for k in r.keys():
i += 1
conn.close()
emp_length_dict = {'n/a': 0, '< 1 year': 0, '1 year': 1, '2 years': 2, '3 years': 3, '4 years': 4, '5 years': 5, '6 years': 6, '7 years': 7, '8 years': 8, '9 years': 9, '10+ years': 10}
home_ownership_dict = {'MORTGAGE': 0, 'OWN': 1, 'RENT': 2, 'OTHER': 3, 'NONE': 4, 'ANY': 5}
features_dict = {'loan_amnt': 0, 'int_rate': 1, 'annual_inc': 2, 'delinq_2yrs': 3, 'open_acc': 4, 'dti': 5, 'emp_length': 6, 'funded_amnt': 7, 'tot_cur_bal': 8, 'home_ownership': 9}
def get_data(s):
"""Return features and targets for a specific search term.
Arguments:
s (str) -- string to search for in loan "title" field
Returns:
(list of lists) -- [list of feature tuples, list of targets]
(features) -- [(sample1 features), (sample2 features),...]
(target) -- [sample1 target, sample2 target,...]
"""
data = sql_query('SELECT ' + 'loan_amnt,int_rate,annual_inc,' + 'loan_status,title,delinq_2yrs,' + 'open_acc,dti,emp_length,' + 'funded_amnt,tot_cur_bal,home_ownership ' + 'FROM loan ' + "WHERE application_type='INDIVIDUAL';")
features_list = []
target_list = []
n = 0
n0 = 0
n1 = 0
for d in data:
test0 = isinstance(d[0], float)
test1 = isinstance(d[1], str)
test2 = isinstance(d[2], float)
test3 = isinstance(d[3], str)
test4 = isinstance(d[4], str)
test5 = isinstance(d[5], float)
test6 = isinstance(d[6], float)
test7 = isinstance(d[7], float)
test8 = isinstance(d[8], str)
test9 = isinstance(d[9], float)
test10 = isinstance(d[10], float)
if test0 and test1 and test2 and test3 and test4 and test5 and test6 and test7 and test8 and test9 and test10:
try:
d1_float = float(d[1].replace('%', ''))
except:
continue
try:
e = emp_length_dict[d[8]]
except:
continue
try:
h = home_ownership_dict[d[11]]
except:
continue
if s.lower() in d[4].lower():
if d[3] == 'Fully Paid' or d[3] == 'Current':
target = 0
n += 1
n0 += 1
elif 'Late' in d[3] or d[3] == 'Charged Off':
target = 1
n += 1
n1 += 1
else:
continue
features = (d[0], float(d[1].replace('%', '')), d[2], d[5], d[6], d[7], emp_length_dict[d[8]], d[9], d[10], home_ownership_dict[d[11]])
features_list.append(features)
target_list.append(target)
else:
pass
result = [features_list, target_list]
return result
def create_scatter_plot(x0_data, y0_data, x1_data, y1_data, pt, pa, x_label, y_label, axis_type):
ax = plt.gca()
ax.set_axis_bgcolor('#BBBBBB')
ax.set_axisbelow(True)
plt.axis(pa)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
if axis_type == 'semilogx':
plt.semilogx(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogx(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'semilogy':
plt.semilogy(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.semilogy(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
elif axis_type == 'loglog':
plt.loglog(x0_data, y0_data, label='0: "Fully Paid" or "Current"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='b')
plt.loglog(x1_data, y1_data, label='1: "Late" or "Charged Off"', linestyle='None', marker='.', markersize=8, alpha=0.5, color='r')
plt.clf()
def plot_two_fields(data, s, f1, f2, pa, x_label, y_label, axis_type):
x0_list = []
y0_list = []
x1_list = []
y1_list = []
features_list = data[0]
target_list = data[1]
for i in range(len(features_list)):
x = features_list[i][features_dict[f1]]
y = features_list[i][features_dict[f2]]
if target_list[i] == 0:
x0_list.append(x)
y0_list.append(y)
elif target_list[i] == 1:
x1_list.append(x)
y1_list.append(y)
else:
pass
cc_data = get_data('credit card')
plot_two_fields(cc_data, 'credit card', 'home_ownership', 'funded_amnt', [-1, 6, 0.0, 35000.0], 'home ownership', 'funded amount', 'standard') | code |
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