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stringlengths 13
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|---|---|---|---|
74048227/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import time
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col='id')
train_df = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
training_missing_val_count_by_column = train.isnull().values.sum()
test_missing_val_count_by_column = test.isnull().values.sum()
pd.set_option('display.max_rows', None)
train.describe().T
train2 = train.dropna(axis='rows')
train3 = train.dropna(axis='columns')
training_missing_val_count_by_column = train.isnull().sum()
imputer = SimpleImputer(strategy='mean')
train_imputed = pd.DataFrame(imputer.fit_transform(train))
train_imputed.columns = train.columns
train = train_imputed
print('Missing values imputed') | code |
74048227/cell_3 | [
"text_plain_output_1.png"
] | import os
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
from sklearn import ensemble, linear_model, metrics, model_selection, neighbors, preprocessing, svm, tree
from sklearn.impute import SimpleImputer
from sklearn.model_selection import cross_validate, train_test_split, KFold, GridSearchCV
from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler
from catboost import CatBoostClassifier
from xgboost import XGBClassifier
print('Libraries imported', time.time()) | code |
74048227/cell_22 | [
"text_plain_output_1.png"
] | from sklearn import ensemble, linear_model,metrics,model_selection,neighbors,preprocessing, svm, tree
from sklearn.impute import SimpleImputer
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import time
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col='id')
train_df = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
training_missing_val_count_by_column = train.isnull().values.sum()
test_missing_val_count_by_column = test.isnull().values.sum()
pd.set_option('display.max_rows', None)
train.describe().T
train2 = train.dropna(axis='rows')
train3 = train.dropna(axis='columns')
training_missing_val_count_by_column = train.isnull().sum()
imputer = SimpleImputer(strategy='mean')
train_imputed = pd.DataFrame(imputer.fit_transform(train))
train_imputed.columns = train.columns
train = train_imputed
corr = train.corr()
mask = np.triu(np.ones_like(corr, dtype=bool))
corr_matrix = train.corr().abs()
high_corr = np.where(corr_matrix > 0.02)
high_corr = [(corr_matrix.columns[x], corr_matrix.columns[y]) for x, y in zip(*high_corr) if x != y and x < y]
featuresofinterest = ['f6', 'f15', 'f32', 'f34', 'f36', 'f45', 'f46', 'f51', 'f57', 'f86', 'f90', 'f97', 'f111']
train_normalized = preprocessing.normalize(train_imputed, norm='l2')
train_normalized = pd.DataFrame(train_normalized)
train_normalized.columns = train.columns
train = train_normalized
print('Data normalised') | code |
74048227/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import time
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col='id')
train_df = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
training_missing_val_count_by_column = train.isnull().values.sum()
test_missing_val_count_by_column = test.isnull().values.sum()
pd.set_option('display.max_rows', None)
train.describe().T | code |
74048227/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import time
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col='id')
train_df = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
training_missing_val_count_by_column = train.isnull().values.sum()
test_missing_val_count_by_column = test.isnull().values.sum()
pd.set_option('display.max_rows', None)
train.describe().T
train2 = train.dropna(axis='rows')
print('rows ; ', train.shape[0], '\nrows with missing data : ', train.shape[0] - train2.shape[0])
train3 = train.dropna(axis='columns')
print('\ncolumns ; ', train.shape[1], '\ncolumns with missing data : ', train.shape[1] - train3.shape[1])
print('\n', time.time()) | code |
74048227/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import time
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col='id')
train_df = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
print('Data Import Complete', time.time()) | code |
130004107/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
data.plot(kind='scatter', x='Survived', y='Age', title='Scatter Plot of Survivors Separated by Age') | code |
130004107/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
data['Pclass'].value_counts() | code |
130004107/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count() | code |
130004107/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
a = data.filter(['AgeBin', 'Survived'])
b = a.pivot_table(index='AgeBin', columns=['Survived'], aggfunc=len)
b
data[(data['Sex'] == 'male') & (data['Survived'] == 1)]['Pclass'].value_counts().sort_index().plot(kind='bar', xlabel='Male Survivor', title='Male Survivors Separated by Cabin Class') | code |
130004107/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
data[data['Survived'] == 1]['Age'].value_counts().sort_index().plot(kind='bar', title='Survivors by Age') | code |
130004107/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
data[data['Survived'] == 1]['Pclass'].value_counts().sort_index().plot(kind='bar', title='Survivors Separated by Cabin Class') | code |
130004107/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
(data['Age'].min(), data['Age'].max()) | code |
130004107/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
a = data.filter(['AgeBin', 'Survived'])
b = a.pivot_table(index='AgeBin', columns=['Survived'], aggfunc=len)
b
b.plot(kind='bar', stacked=True, xlabel='Age Group', ylabel='Count', title='Survivors by Age Group') | code |
130004107/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
data['AgeBin'].value_counts().sort_index().plot(kind='bar', title='Passenger by Age Groups') | code |
130004107/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data | code |
130004107/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
data['Sex'].value_counts() | code |
130004107/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
alpha_color = 0.5
data['Pclass'].value_counts().sort_index().plot(kind='bar', alpha=alpha_color, title='Passengers Separated by Cabin Class') | code |
130004107/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
alpha_color = 0.5
data['Sex'].value_counts().sort_index().plot(kind='bar', color=['b', 'r'], alpha=alpha_color, title='Passengers Separated by Sex') | code |
130004107/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
a = data.filter(['AgeBin', 'Survived'])
b = a.pivot_table(index='AgeBin', columns=['Survived'], aggfunc=len)
b
data[data['Sex'] == 'female']['Survived'].value_counts().plot(kind='bar', xlabel='Female Survivor', title='Number of Female Survivors and Death') | code |
130004107/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
data[data['Survived'] == 0]['AgeBin'].value_counts().sort_index().plot(kind='bar', title='Death by Age Groups') | code |
130004107/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
data['Survived'].value_counts() | code |
130004107/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
alive = data[data['Survived'].eq(1)]['Survived'].value_counts()
round(data[data['Survived'].eq(1)]['Sex'].value_counts().astype(int) * 100 / alive[1], 2) | code |
130004107/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
len(data) | code |
130004107/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
alpha_color = 0.5
data['Survived'].value_counts().plot(kind='bar', title='Number of Survivors') | code |
130004107/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
a = data.filter(['AgeBin', 'Survived'])
b = a.pivot_table(index='AgeBin', columns=['Survived'], aggfunc=len)
b
data[(data['Sex'] == 'female') & (data['Survived'] == 1)]['Pclass'].value_counts().sort_index().plot(kind='bar', xlabel='Female Survivor', title='Female Survivors Separated by Cabin Class') | code |
130004107/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
a = data.filter(['AgeBin', 'Survived'])
b = a.pivot_table(index='AgeBin', columns=['Survived'], aggfunc=len)
b
data[data['Sex'] == 'male']['Survived'].value_counts().plot(kind='bar', xlabel='Male Survivor', title='Number of Male Survivors and Death') | code |
130004107/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
data['Survived'].value_counts() * 100 / len(data) | code |
130004107/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/titanicdataset-traincsv/train.csv')
data
data.count()
data[data['Survived'] == 1]['AgeBin'].value_counts().sort_index().plot(kind='bar', title='Survivors by Age Groups') | code |
72120651/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
countries_dataset = pd.read_csv('../input/countries-of-the-world/countries of the world.csv', decimal=',')
countries_dataset.head() | code |
72120651/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
countries_dataset = pd.read_csv('../input/countries-of-the-world/countries of the world.csv', decimal=',')
print('Shape:', countries_dataset.shape, '\n')
print('Missing values:')
print(countries_dataset.isnull().sum(), '\n')
print('Data types:')
print(countries_dataset.dtypes, '\n') | code |
105201140/cell_2 | [
"text_plain_output_1.png"
] | # build dependency wheels
!pip wheel --verbose --no-binary cython-bbox==0.1.3 cython-bbox -w /kaggle/working/
!pip wheel --verbose --no-binary lap==0.4.0 lap -w /kaggle/working/
!pip wheel --verbose --no-binary loguru-0.6.0 loguru -w /kaggle/working/
!pip wheel --verbose --no-binary thop-0.1.1.post2209072238 thop -w /kaggle/working/
# build yolox wheel
!git clone https://github.com/ifzhang/ByteTrack.git
!cd ByteTrack && python3 setup.py bdist_wheel && cp -r ./dist/* /kaggle/working/
# clean up
!rm -rf /kaggle/working/ByteTrack
!rm torch-1.12.1-cp37-cp37m-manylinux1_x86_64.whl
!rm typing_extensions-4.3.0-py3-none-any.whl | code |
328596/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics
import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()
group_by_age = pd.cut(titanic_df['Age'], np.arange(0, 90, 10))
age_grouping = titanic_df.groupby(group_by_age).mean()
titanic_df.count()
titanic_df = titanic_df.drop(['Cabin'], axis=1)
titanic_df = titanic_df.dropna()
titanic_df.count()
def preprocess_titanic_df(df):
processed_df = df.copy()
le = preprocessing.LabelEncoder()
processed_df.Sex = le.fit_transform(processed_df.Sex)
processed_df.Embarked = le.fit_transform(processed_df.Embarked)
processed_df = processed_df.drop(['Name', 'Ticket'], axis=1)
return processed_df
processed_df = preprocess_titanic_df(titanic_df)
processed_df.count()
processed_df
X = processed_df.drop(['Survived'], axis=1).values
Y = processed_df['Survived'].values
print(X) | code |
328596/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()
group_by_age = pd.cut(titanic_df['Age'], np.arange(0, 90, 10))
age_grouping = titanic_df.groupby(group_by_age).mean()
age_grouping['Survived'].plot.bar() | code |
328596/cell_25 | [
"text_plain_output_1.png"
] | from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics
import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()
group_by_age = pd.cut(titanic_df['Age'], np.arange(0, 90, 10))
age_grouping = titanic_df.groupby(group_by_age).mean()
titanic_df.count()
test_df.count()
titanic_df = titanic_df.drop(['Cabin'], axis=1)
test_df = test_df.drop(['Cabin'], axis=1)
titanic_df = titanic_df.dropna()
test_df = test_df.dropna()
titanic_df.count()
test_df.count()
def preprocess_titanic_df(df):
processed_df = df.copy()
le = preprocessing.LabelEncoder()
processed_df.Sex = le.fit_transform(processed_df.Sex)
processed_df.Embarked = le.fit_transform(processed_df.Embarked)
processed_df = processed_df.drop(['Name', 'Ticket'], axis=1)
return processed_df
processed_df = preprocess_titanic_df(titanic_df)
processed_df.count()
processed_df
processed_test_df = preprocess_titanic_df(test_df)
processed_test_df.count()
processed_test_df
X = processed_df.drop(['Survived'], axis=1).values
Y = processed_df['Survived'].values
X_test = processed_test_df.values
clf_dt = tree.DecisionTreeClassifier(max_depth=10)
clf_dt.fit(X, Y)
Y_test = clf_dt.predict(X_test)
clf_dt.score(X_test, Y_test) | code |
328596/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
test_df.head() | code |
328596/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics
import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
test_df.count()
test_df = test_df.drop(['Cabin'], axis=1)
test_df = test_df.dropna()
test_df.count()
def preprocess_titanic_df(df):
processed_df = df.copy()
le = preprocessing.LabelEncoder()
processed_df.Sex = le.fit_transform(processed_df.Sex)
processed_df.Embarked = le.fit_transform(processed_df.Embarked)
processed_df = processed_df.drop(['Name', 'Ticket'], axis=1)
return processed_df
processed_test_df = preprocess_titanic_df(test_df)
processed_test_df.count()
processed_test_df | code |
328596/cell_6 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean() | code |
328596/cell_11 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
test_df.count() | code |
328596/cell_19 | [
"text_plain_output_1.png"
] | from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics
import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()
group_by_age = pd.cut(titanic_df['Age'], np.arange(0, 90, 10))
age_grouping = titanic_df.groupby(group_by_age).mean()
titanic_df.count()
titanic_df = titanic_df.drop(['Cabin'], axis=1)
titanic_df = titanic_df.dropna()
titanic_df.count()
def preprocess_titanic_df(df):
processed_df = df.copy()
le = preprocessing.LabelEncoder()
processed_df.Sex = le.fit_transform(processed_df.Sex)
processed_df.Embarked = le.fit_transform(processed_df.Embarked)
processed_df = processed_df.drop(['Name', 'Ticket'], axis=1)
return processed_df
processed_df = preprocess_titanic_df(titanic_df)
processed_df.count()
processed_df | code |
328596/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import random
import numpy as np
import pandas as pd
from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics
import sklearn.ensemble as ske
import tensorflow as tf
from tensorflow.contrib import skflow | code |
328596/cell_7 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()
print(class_sex_grouping['Survived']) | code |
328596/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()
class_sex_grouping['Survived'].plot.bar() | code |
328596/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()
group_by_age = pd.cut(titanic_df['Age'], np.arange(0, 90, 10))
age_grouping = titanic_df.groupby(group_by_age).mean()
titanic_df.count()
titanic_df = titanic_df.drop(['Cabin'], axis=1)
titanic_df = titanic_df.dropna()
titanic_df.count() | code |
328596/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.head() | code |
328596/cell_17 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
test_df.count()
test_df = test_df.drop(['Cabin'], axis=1)
test_df = test_df.dropna()
test_df.count() | code |
328596/cell_10 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()
group_by_age = pd.cut(titanic_df['Age'], np.arange(0, 90, 10))
age_grouping = titanic_df.groupby(group_by_age).mean()
titanic_df.count() | code |
328596/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df['Survived'].mean() | code |
122252822/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
os.chdir('C:\\\\Users\\\\melanie.vercaempt\\\\Documents\\\\Code\\\\train-keyrus-academy-python\\\\data-viz') | code |
122252822/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datetime import date
import os
import geopandas as gpd
import folium
import mapclassify
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
import pandas as pd
import plotly.express as px
import re
import seaborn as sns
from shapely.geometry import Point, Polygon
from shapely.geometry import MultiPolygon | code |
17122803/cell_13 | [
"text_plain_output_1.png"
] | from nltk.tokenize import word_tokenize, sent_tokenize
text = 'Mary had a little lamb. Her fleece was white as snow'
sents = sent_tokenize(text)
print(sents) | code |
17122803/cell_9 | [
"text_plain_output_1.png"
] | text1.concordance('monstrous')
text1.dispersion_plot(['happy', 'sad']) | code |
17122803/cell_25 | [
"image_output_1.png"
] | from nltk.tokenize import word_tokenize, sent_tokenize
import nltk
text2.concordance('monstrous')
text2.similar('monstrous')
text2.common_contexts(['monstrous', 'very'])
text2 = 'Mary closed on closing night when she was in the mood to close.'
nltk.pos_tag(word_tokenize(text2)) | code |
17122803/cell_4 | [
"image_output_1.png"
] | text2.concordance('monstrous') | code |
17122803/cell_6 | [
"text_plain_output_1.png"
] | text2.concordance('monstrous')
text2.similar('monstrous')
text2.common_contexts(['monstrous', 'very']) | code |
17122803/cell_2 | [
"text_plain_output_1.png"
] | from nltk.book import * | code |
17122803/cell_19 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize, sent_tokenize
from string import punctuation
text = 'Mary had a little lamb. Her fleece was white as snow'
customStopWords = set(stopwords.words('english') + list(punctuation))
wordsWOStopwords = [word for word in word_tokenize(text) if word not in customStopWords]
print(wordsWOStopwords) | code |
17122803/cell_1 | [
"text_plain_output_1.png"
] | import os
import nltk
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17122803/cell_7 | [
"text_plain_output_1.png"
] | text4.dispersion_plot(['citizens', 'democracy', 'freedom', 'duties', 'America']) | code |
17122803/cell_8 | [
"text_plain_output_1.png"
] | text2.concordance('monstrous')
text2.similar('monstrous')
text2.common_contexts(['monstrous', 'very'])
text2.dispersion_plot(['happy', 'sad']) | code |
17122803/cell_3 | [
"image_output_1.png"
] | text1.concordance('monstrous') | code |
17122803/cell_24 | [
"text_plain_output_1.png"
] | from nltk.stem.lancaster import LancasterStemmer
from nltk.tokenize import word_tokenize, sent_tokenize
text2.concordance('monstrous')
text2.similar('monstrous')
text2.common_contexts(['monstrous', 'very'])
text2 = 'Mary closed on closing night when she was in the mood to close.'
st = LancasterStemmer()
stemmedWords = [st.stem(word) for word in word_tokenize(text2)]
print(stemmedWords) | code |
17122803/cell_14 | [
"text_plain_output_1.png"
] | from nltk.tokenize import word_tokenize, sent_tokenize
text = 'Mary had a little lamb. Her fleece was white as snow'
sents = sent_tokenize(text)
words = [word_tokenize(sent) for sent in sents]
print(words) | code |
17122803/cell_5 | [
"text_plain_output_1.png"
] | text2.concordance('monstrous')
text2.similar('monstrous') | code |
90143099/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
earning = pd.read_csv('/kaggle/input/cusersmarildownloadsearningcsv/earning.csv', delimiter=';')
data = earning[['year', 'femalesmanagers', 'malemanagers', 'personmanagers', 'femalemachineryoperatorsanddrivers', 'malemachineryoperatorsanddrivers', 'personmachineryoperatorsanddrivers', 'femalesalesworkers', 'malesalesworkers', 'personsalesworkers']]
data.columns = ['year', 'manager_f', 'manager_m', 'manager_tot', 'operator_f', 'operator_m', 'operator_tot', 'sales_f', 'sales_m', 'sales_tot']
data
plt.plot(data['year'], data['manager_m'] - data['manager_f'], label='ManagerInnen')
plt.plot(data['year'], data['operator_m'] - data['operator_f'], label='OperatorInnen')
plt.plot(data['year'], data['sales_m'] - data['sales_f'], label='Sales')
plt.plot(data['year'], np.zeros(len(data['year'])), color='red', linestyle='--')
plt.title('Lohndifferenz Entwicklung')
plt.ylabel('Differenz in US $')
plt.xlabel('Jahre')
plt.legend()
plt.xlim(2004, 2017)
plt.show() | code |
90143099/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
earning = pd.read_csv('/kaggle/input/cusersmarildownloadsearningcsv/earning.csv', delimiter=';')
data = earning[['year', 'femalesmanagers', 'malemanagers', 'personmanagers', 'femalemachineryoperatorsanddrivers', 'malemachineryoperatorsanddrivers', 'personmachineryoperatorsanddrivers', 'femalesalesworkers', 'malesalesworkers', 'personsalesworkers']]
data.columns = ['year', 'manager_f', 'manager_m', 'manager_tot', 'operator_f', 'operator_m', 'operator_tot', 'sales_f', 'sales_m', 'sales_tot']
data
new_data = pd.DataFrame({'year': data['year'], 'manager': data['manager_m'] - data['manager_f'], 'operator': data['operator_m'] - data['operator_f'], 'sales': data['sales_m'] - data['sales_f']})
new_data.index = new_data['year']
new_data = new_data.drop('year', axis=1)
new_data | code |
90143099/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
earning = pd.read_csv('/kaggle/input/cusersmarildownloadsearningcsv/earning.csv', delimiter=';')
data = earning[['year', 'femalesmanagers', 'malemanagers', 'personmanagers', 'femalemachineryoperatorsanddrivers', 'malemachineryoperatorsanddrivers', 'personmachineryoperatorsanddrivers', 'femalesalesworkers', 'malesalesworkers', 'personsalesworkers']]
data.columns = ['year', 'manager_f', 'manager_m', 'manager_tot', 'operator_f', 'operator_m', 'operator_tot', 'sales_f', 'sales_m', 'sales_tot']
data | code |
90143099/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
90143099/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
earning = pd.read_csv('/kaggle/input/cusersmarildownloadsearningcsv/earning.csv', delimiter=';')
data = earning[['year', 'femalesmanagers', 'malemanagers', 'personmanagers', 'femalemachineryoperatorsanddrivers', 'malemachineryoperatorsanddrivers', 'personmachineryoperatorsanddrivers', 'femalesalesworkers', 'malesalesworkers', 'personsalesworkers']]
data.columns = ['year', 'manager_f', 'manager_m', 'manager_tot', 'operator_f', 'operator_m', 'operator_tot', 'sales_f', 'sales_m', 'sales_tot']
data
new_data = pd.DataFrame({'year': data['year'], 'manager': data['manager_m'] - data['manager_f'], 'operator': data['operator_m'] - data['operator_f'], 'sales': data['sales_m'] - data['sales_f']})
new_data.index = new_data['year']
new_data = new_data.drop('year', axis=1)
new_data
for beruf in new_data.columns:
print(beruf) | code |
90143099/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
earning = pd.read_csv('/kaggle/input/cusersmarildownloadsearningcsv/earning.csv', delimiter=';')
data = earning[['year', 'femalesmanagers', 'malemanagers', 'personmanagers', 'femalemachineryoperatorsanddrivers', 'malemachineryoperatorsanddrivers', 'personmachineryoperatorsanddrivers', 'femalesalesworkers', 'malesalesworkers', 'personsalesworkers']]
data.columns = ['year', 'manager_f', 'manager_m', 'manager_tot', 'operator_f', 'operator_m', 'operator_tot', 'sales_f', 'sales_m', 'sales_tot']
data
plt.xlim(2004, 2017)
new_data = pd.DataFrame({'year': data['year'], 'manager': data['manager_m'] - data['manager_f'], 'operator': data['operator_m'] - data['operator_f'], 'sales': data['sales_m'] - data['sales_f']})
new_data.index = new_data['year']
new_data = new_data.drop('year', axis=1)
new_data
for beruf in new_data.columns:
plt.bar(beruf, new_data.loc[2012, beruf]) | code |
1003686/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_train.head() | code |
1003686/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_train['SalePrice'].describe() | code |
1003686/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
sns.distplot(df_train['SalePrice']) | code |
1003686/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
print('How skewed is the data?, Skewness: {}'.format(df_train['SalePrice'].skew()))
print('How sharp is the peak the data?, Kurtosis: {}'.format(df_train['SalePrice'].kurt())) | code |
1003686/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
print(df_train.columns) | code |
32071698/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
plt.style.use('fivethirtyeight')
full_table = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv', parse_dates=['ObservationDate'])
italy = pd.DataFrame(full_table[full_table['Country/Region'] == 'Italy'])
france = pd.DataFrame(full_table[full_table['Country/Region'] == 'France'])
germany = pd.DataFrame(full_table[full_table['Country/Region'] == 'Germany'])
uk = pd.DataFrame(full_table[full_table['Country/Region'] == 'UK'])
spain = pd.DataFrame(full_table[full_table['Country/Region'] == 'Spain'])
turkey = pd.DataFrame(full_table[full_table['Country/Region'] == 'Turkey'])
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
italy[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Italy")
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
germany[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Germany")
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
spain[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Spain")
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
italy[['ObservationDate','Confirmed','Deaths']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus cases, deaths in Italy, Germany and Spain")
germany[['ObservationDate','Confirmed','Deaths']].plot(x='ObservationDate',kind='line',ax=ax)
spain[['ObservationDate','Confirmed','Deaths']].plot(x='ObservationDate',kind='line',ax=ax)
ax.legend(['Confirmed Cases in Italy','Confirmed Deaths in Italy',
'Confirmed Cases in Germany','Confirmed Deaths in Germany',
'Confirmed Cases in Spain','Confirmed Deaths in Spain'])
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
turkey[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Turkey")
uk_agg=pd.pivot_table(uk, index=['ObservationDate'],values=['Confirmed','Deaths','Recovered'],aggfunc=np.sum)
#uk_agg
fig, ax = plt.subplots(figsize=(15,7))
plt.plot(uk_agg.index,uk_agg.values)
plt.legend(['Confirmed','Deaths','Recovered'])
plt.title("Tracking Corona Virus in United Kingdom")
plt.ylabel('Count of Confirmed Positive Cases, Deaths, and Recoveries')
plt.xticks(rotation=90)
fig, ax = plt.subplots(figsize=(15, 7))
ax.set_xlabel('Observation Date')
ax.set_ylabel('Death Rate(%)')
italy[['ObservationDate', 'Death Rate in Italy']].plot(x='ObservationDate', kind='line', ax=ax)
germany[['ObservationDate', 'Death Rate in Germany']].plot(x='ObservationDate', kind='line', ax=ax)
spain[['ObservationDate', 'Death Rate in Spain']].plot(x='ObservationDate', kind='line', ax=ax, title='Comparing Corona Virus in Italy, Germany and Spain')
turkey[['ObservationDate', 'Death Rate in Turkey']].plot(x='ObservationDate', kind='line', ax=ax, title='Comparing Corona Virus in Italy, Germany, Turkey and Spain') | code |
32071698/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
plt.style.use('fivethirtyeight')
full_table = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv', parse_dates=['ObservationDate'])
italy = pd.DataFrame(full_table[full_table['Country/Region'] == 'Italy'])
france = pd.DataFrame(full_table[full_table['Country/Region'] == 'France'])
germany = pd.DataFrame(full_table[full_table['Country/Region'] == 'Germany'])
uk = pd.DataFrame(full_table[full_table['Country/Region'] == 'UK'])
spain = pd.DataFrame(full_table[full_table['Country/Region'] == 'Spain'])
turkey = pd.DataFrame(full_table[full_table['Country/Region'] == 'Turkey'])
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
italy[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Italy")
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
germany[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Germany")
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
spain[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Spain")
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
italy[['ObservationDate','Confirmed','Deaths']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus cases, deaths in Italy, Germany and Spain")
germany[['ObservationDate','Confirmed','Deaths']].plot(x='ObservationDate',kind='line',ax=ax)
spain[['ObservationDate','Confirmed','Deaths']].plot(x='ObservationDate',kind='line',ax=ax)
ax.legend(['Confirmed Cases in Italy','Confirmed Deaths in Italy',
'Confirmed Cases in Germany','Confirmed Deaths in Germany',
'Confirmed Cases in Spain','Confirmed Deaths in Spain'])
fig, ax = plt.subplots(figsize=(15, 7))
ax.set_xlabel('Observation Date')
ax.set_ylabel('Count of Confirmed Positive Cases, Deaths, and Recoveries')
turkey[['ObservationDate', 'Confirmed', 'Deaths', 'Recovered']].plot(x='ObservationDate', kind='line', ax=ax, title='Tracking Corona Virus in Turkey') | code |
32071698/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
full_table = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv', parse_dates=['ObservationDate'])
italy = pd.DataFrame(full_table[full_table['Country/Region'] == 'Italy'])
france = pd.DataFrame(full_table[full_table['Country/Region'] == 'France'])
germany = pd.DataFrame(full_table[full_table['Country/Region'] == 'Germany'])
uk = pd.DataFrame(full_table[full_table['Country/Region'] == 'UK'])
spain = pd.DataFrame(full_table[full_table['Country/Region'] == 'Spain'])
turkey = pd.DataFrame(full_table[full_table['Country/Region'] == 'Turkey'])
france.sample(10) | code |
32071698/cell_2 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
plt.style.use('fivethirtyeight') | code |
32071698/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
plt.style.use('fivethirtyeight')
full_table = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv', parse_dates=['ObservationDate'])
italy = pd.DataFrame(full_table[full_table['Country/Region'] == 'Italy'])
france = pd.DataFrame(full_table[full_table['Country/Region'] == 'France'])
germany = pd.DataFrame(full_table[full_table['Country/Region'] == 'Germany'])
uk = pd.DataFrame(full_table[full_table['Country/Region'] == 'UK'])
spain = pd.DataFrame(full_table[full_table['Country/Region'] == 'Spain'])
turkey = pd.DataFrame(full_table[full_table['Country/Region'] == 'Turkey'])
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
italy[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Italy")
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
germany[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Germany")
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
spain[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Spain")
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
italy[['ObservationDate','Confirmed','Deaths']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus cases, deaths in Italy, Germany and Spain")
germany[['ObservationDate','Confirmed','Deaths']].plot(x='ObservationDate',kind='line',ax=ax)
spain[['ObservationDate','Confirmed','Deaths']].plot(x='ObservationDate',kind='line',ax=ax)
ax.legend(['Confirmed Cases in Italy','Confirmed Deaths in Italy',
'Confirmed Cases in Germany','Confirmed Deaths in Germany',
'Confirmed Cases in Spain','Confirmed Deaths in Spain'])
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
turkey[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Turkey")
uk_agg=pd.pivot_table(uk, index=['ObservationDate'],values=['Confirmed','Deaths','Recovered'],aggfunc=np.sum)
#uk_agg
fig, ax = plt.subplots(figsize=(15,7))
plt.plot(uk_agg.index,uk_agg.values)
plt.legend(['Confirmed','Deaths','Recovered'])
plt.title("Tracking Corona Virus in United Kingdom")
plt.ylabel('Count of Confirmed Positive Cases, Deaths, and Recoveries')
plt.xticks(rotation=90)
uk_agg['Death Rate in United Kingdom'] = uk_agg['Deaths'] / uk_agg['Confirmed'] * 100
uk_agg['Recovery Rate in United Kingdom'] = uk_agg['Recovered'] / uk_agg['Confirmed'] * 100
uk_agg.sample(10)
uk2 = uk_agg.unstack()
uk2 | code |
32071698/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
full_table = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv', parse_dates=['ObservationDate'])
italy = pd.DataFrame(full_table[full_table['Country/Region'] == 'Italy'])
france = pd.DataFrame(full_table[full_table['Country/Region'] == 'France'])
germany = pd.DataFrame(full_table[full_table['Country/Region'] == 'Germany'])
uk = pd.DataFrame(full_table[full_table['Country/Region'] == 'UK'])
spain = pd.DataFrame(full_table[full_table['Country/Region'] == 'Spain'])
turkey = pd.DataFrame(full_table[full_table['Country/Region'] == 'Turkey'])
italy.head() | code |
32071698/cell_15 | [
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
plt.style.use('fivethirtyeight')
full_table = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv', parse_dates=['ObservationDate'])
italy = pd.DataFrame(full_table[full_table['Country/Region'] == 'Italy'])
france = pd.DataFrame(full_table[full_table['Country/Region'] == 'France'])
germany = pd.DataFrame(full_table[full_table['Country/Region'] == 'Germany'])
uk = pd.DataFrame(full_table[full_table['Country/Region'] == 'UK'])
spain = pd.DataFrame(full_table[full_table['Country/Region'] == 'Spain'])
turkey = pd.DataFrame(full_table[full_table['Country/Region'] == 'Turkey'])
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
italy[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Italy")
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
germany[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Germany")
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
spain[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Spain")
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
italy[['ObservationDate','Confirmed','Deaths']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus cases, deaths in Italy, Germany and Spain")
germany[['ObservationDate','Confirmed','Deaths']].plot(x='ObservationDate',kind='line',ax=ax)
spain[['ObservationDate','Confirmed','Deaths']].plot(x='ObservationDate',kind='line',ax=ax)
ax.legend(['Confirmed Cases in Italy','Confirmed Deaths in Italy',
'Confirmed Cases in Germany','Confirmed Deaths in Germany',
'Confirmed Cases in Spain','Confirmed Deaths in Spain'])
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
turkey[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Turkey")
uk_agg = pd.pivot_table(uk, index=['ObservationDate'], values=['Confirmed', 'Deaths', 'Recovered'], aggfunc=np.sum)
fig, ax = plt.subplots(figsize=(15, 7))
plt.plot(uk_agg.index, uk_agg.values)
plt.legend(['Confirmed', 'Deaths', 'Recovered'])
plt.title('Tracking Corona Virus in United Kingdom')
plt.ylabel('Count of Confirmed Positive Cases, Deaths, and Recoveries')
plt.xticks(rotation=90) | code |
32071698/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
plt.style.use('fivethirtyeight')
full_table = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv', parse_dates=['ObservationDate'])
italy = pd.DataFrame(full_table[full_table['Country/Region'] == 'Italy'])
france = pd.DataFrame(full_table[full_table['Country/Region'] == 'France'])
germany = pd.DataFrame(full_table[full_table['Country/Region'] == 'Germany'])
uk = pd.DataFrame(full_table[full_table['Country/Region'] == 'UK'])
spain = pd.DataFrame(full_table[full_table['Country/Region'] == 'Spain'])
turkey = pd.DataFrame(full_table[full_table['Country/Region'] == 'Turkey'])
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
italy[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Italy")
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
germany[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Germany")
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
spain[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Spain")
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
italy[['ObservationDate','Confirmed','Deaths']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus cases, deaths in Italy, Germany and Spain")
germany[['ObservationDate','Confirmed','Deaths']].plot(x='ObservationDate',kind='line',ax=ax)
spain[['ObservationDate','Confirmed','Deaths']].plot(x='ObservationDate',kind='line',ax=ax)
ax.legend(['Confirmed Cases in Italy','Confirmed Deaths in Italy',
'Confirmed Cases in Germany','Confirmed Deaths in Germany',
'Confirmed Cases in Spain','Confirmed Deaths in Spain'])
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
turkey[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Turkey")
uk_agg=pd.pivot_table(uk, index=['ObservationDate'],values=['Confirmed','Deaths','Recovered'],aggfunc=np.sum)
#uk_agg
fig, ax = plt.subplots(figsize=(15,7))
plt.plot(uk_agg.index,uk_agg.values)
plt.legend(['Confirmed','Deaths','Recovered'])
plt.title("Tracking Corona Virus in United Kingdom")
plt.ylabel('Count of Confirmed Positive Cases, Deaths, and Recoveries')
plt.xticks(rotation=90)
uk_agg['Death Rate in United Kingdom'] = uk_agg['Deaths'] / uk_agg['Confirmed'] * 100
uk_agg['Recovery Rate in United Kingdom'] = uk_agg['Recovered'] / uk_agg['Confirmed'] * 100
uk_agg.tail() | code |
32071698/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
plt.style.use('fivethirtyeight')
full_table = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv', parse_dates=['ObservationDate'])
italy = pd.DataFrame(full_table[full_table['Country/Region'] == 'Italy'])
france = pd.DataFrame(full_table[full_table['Country/Region'] == 'France'])
germany = pd.DataFrame(full_table[full_table['Country/Region'] == 'Germany'])
uk = pd.DataFrame(full_table[full_table['Country/Region'] == 'UK'])
spain = pd.DataFrame(full_table[full_table['Country/Region'] == 'Spain'])
turkey = pd.DataFrame(full_table[full_table['Country/Region'] == 'Turkey'])
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
italy[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Italy")
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
germany[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Germany")
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
spain[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Spain")
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
italy[['ObservationDate','Confirmed','Deaths']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus cases, deaths in Italy, Germany and Spain")
germany[['ObservationDate','Confirmed','Deaths']].plot(x='ObservationDate',kind='line',ax=ax)
spain[['ObservationDate','Confirmed','Deaths']].plot(x='ObservationDate',kind='line',ax=ax)
ax.legend(['Confirmed Cases in Italy','Confirmed Deaths in Italy',
'Confirmed Cases in Germany','Confirmed Deaths in Germany',
'Confirmed Cases in Spain','Confirmed Deaths in Spain'])
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
turkey[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Turkey")
uk_agg=pd.pivot_table(uk, index=['ObservationDate'],values=['Confirmed','Deaths','Recovered'],aggfunc=np.sum)
#uk_agg
fig, ax = plt.subplots(figsize=(15,7))
plt.plot(uk_agg.index,uk_agg.values)
plt.legend(['Confirmed','Deaths','Recovered'])
plt.title("Tracking Corona Virus in United Kingdom")
plt.ylabel('Count of Confirmed Positive Cases, Deaths, and Recoveries')
plt.xticks(rotation=90)
uk_agg['Death Rate in United Kingdom'] = uk_agg['Deaths'] / uk_agg['Confirmed'] * 100
uk_agg['Recovery Rate in United Kingdom'] = uk_agg['Recovered'] / uk_agg['Confirmed'] * 100
uk_agg.sample(10) | code |
32071698/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
plt.style.use('fivethirtyeight')
full_table = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv', parse_dates=['ObservationDate'])
italy = pd.DataFrame(full_table[full_table['Country/Region'] == 'Italy'])
france = pd.DataFrame(full_table[full_table['Country/Region'] == 'France'])
germany = pd.DataFrame(full_table[full_table['Country/Region'] == 'Germany'])
uk = pd.DataFrame(full_table[full_table['Country/Region'] == 'UK'])
spain = pd.DataFrame(full_table[full_table['Country/Region'] == 'Spain'])
turkey = pd.DataFrame(full_table[full_table['Country/Region'] == 'Turkey'])
fig, ax = plt.subplots(figsize=(15, 7))
ax.set_xlabel('Observation Date')
ax.set_ylabel('Count of Confirmed Positive Cases, Deaths, and Recoveries')
italy[['ObservationDate', 'Confirmed', 'Deaths', 'Recovered']].plot(x='ObservationDate', kind='line', ax=ax, title='Tracking Corona Virus in Italy')
fig, ax = plt.subplots(figsize=(15, 7))
ax.set_xlabel('Observation Date')
ax.set_ylabel('Count of Confirmed Positive Cases, Deaths, and Recoveries')
germany[['ObservationDate', 'Confirmed', 'Deaths', 'Recovered']].plot(x='ObservationDate', kind='line', ax=ax, title='Tracking Corona Virus in Germany')
fig, ax = plt.subplots(figsize=(15, 7))
ax.set_xlabel('Observation Date')
ax.set_ylabel('Count of Confirmed Positive Cases, Deaths, and Recoveries')
spain[['ObservationDate', 'Confirmed', 'Deaths', 'Recovered']].plot(x='ObservationDate', kind='line', ax=ax, title='Tracking Corona Virus in Spain') | code |
32071698/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
plt.style.use('fivethirtyeight')
full_table = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv', parse_dates=['ObservationDate'])
italy = pd.DataFrame(full_table[full_table['Country/Region'] == 'Italy'])
france = pd.DataFrame(full_table[full_table['Country/Region'] == 'France'])
germany = pd.DataFrame(full_table[full_table['Country/Region'] == 'Germany'])
uk = pd.DataFrame(full_table[full_table['Country/Region'] == 'UK'])
spain = pd.DataFrame(full_table[full_table['Country/Region'] == 'Spain'])
turkey = pd.DataFrame(full_table[full_table['Country/Region'] == 'Turkey'])
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
italy[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Italy")
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
germany[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Germany")
fig, ax = plt.subplots(figsize=(15,7))
ax.set_xlabel("Observation Date")
ax.set_ylabel("Count of Confirmed Positive Cases, Deaths, and Recoveries")
spain[['ObservationDate','Confirmed','Deaths','Recovered']].plot(x='ObservationDate',kind='line',ax=ax, title="Tracking Corona Virus in Spain")
fig, ax = plt.subplots(figsize=(15, 7))
ax.set_xlabel('Observation Date')
ax.set_ylabel('Count of Confirmed Positive Cases, Deaths, and Recoveries')
italy[['ObservationDate', 'Confirmed', 'Deaths']].plot(x='ObservationDate', kind='line', ax=ax, title='Tracking Corona Virus cases, deaths in Italy, Germany and Spain')
germany[['ObservationDate', 'Confirmed', 'Deaths']].plot(x='ObservationDate', kind='line', ax=ax)
spain[['ObservationDate', 'Confirmed', 'Deaths']].plot(x='ObservationDate', kind='line', ax=ax)
ax.legend(['Confirmed Cases in Italy', 'Confirmed Deaths in Italy', 'Confirmed Cases in Germany', 'Confirmed Deaths in Germany', 'Confirmed Cases in Spain', 'Confirmed Deaths in Spain']) | code |
73074342/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/30days-folds/train_folds.csv')
train = df
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
cat_features = ['cat' + str(i) for i in range(10)]
num_features = ['cont' + str(i) for i in range(14)]
for col in cat_features:
print(set(train[col].value_counts().index) == set(df_test[col].value_counts().index)) | code |
73074342/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/30days-folds/train_folds.csv')
train = df
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
df.describe(percentiles=[0.1, 0.25, 0.5, 0.75, 0.9]).T | code |
73074342/cell_11 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('../input/30days-folds/train_folds.csv')
train = df
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
# Comparing the datasets length
fig, ax = plt.subplots(figsize=(5, 5))
pie = ax.pie([len(df), len(df_test)],
labels=["Train dataset", "Test dataset"],
colors=["salmon", "teal"],
textprops={"fontsize": 15},
autopct='%1.1f%%')
ax.axis("equal")
ax.set_title("Dataset length comparison", fontsize=18)
fig.set_facecolor('white')
plt.show();
df.describe(percentiles=[0.1, 0.25, 0.5, 0.75, 0.9]).T
fig, ax = plt.subplots(figsize=(16, 8))
bars = ax.hist(df["target"],
bins=100,
color="palevioletred",
edgecolor="black")
ax.set_title("Target distribution", fontsize=20, pad=15)
ax.set_ylabel("Amount of values", fontsize=14, labelpad=15)
ax.set_xlabel("Target value", fontsize=14, labelpad=10)
ax.margins(0.025, 0.12)
ax.grid(axis="y")
plt.show();
cat_features = ['cat' + str(i) for i in range(10)]
num_features = ['cont' + str(i) for i in range(14)]
# Combined dataframe containing numerical features only
df = pd.concat([df[num_features], df_test[num_features]], axis=0)
columns = df.columns.values
# Calculating required amount of rows to display all feature plots
cols = 3
rows = len(columns) // cols + 1
fig, axs = plt.subplots(ncols=cols, nrows=rows, figsize=(16,20), sharex=False)
# Adding some distance between plots
plt.subplots_adjust(hspace = 0.3)
# Plots counter
i=0
for r in np.arange(0, rows, 1):
for c in np.arange(0, cols, 1):
if i >= len(columns): # If there is no more data columns to make plots from
axs[r, c].set_visible(False) # Hiding axes so there will be clean background
else:
# Train data histogram
hist1 = axs[r, c].hist(df[columns[i]].values,
range=(df[columns[i]].min(),
df[columns[i]].max()),
bins=40,
color="deepskyblue",
edgecolor="black",
alpha=0.7,
label="Train Dataset")
# Test data histogram
hist2 = axs[r, c].hist(df_test[columns[i]].values,
range=(df[columns[i]].min(),
df[columns[i]].max()),
bins=40,
color="palevioletred",
edgecolor="black",
alpha=0.7,
label="Test Dataset")
axs[r, c].set_title(columns[i], fontsize=14, pad=5)
axs[r, c].tick_params(axis="y", labelsize=13)
axs[r, c].tick_params(axis="x", labelsize=13)
axs[r, c].grid(axis="y")
axs[r, c].legend(fontsize=13)
i+=1
# plt.suptitle("Numerical feature values distribution in both datasets", y=0.99)
plt.show();
df.head() | code |
73074342/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/30days-folds/train_folds.csv')
train = df
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
df.describe(percentiles=[0.1, 0.25, 0.5, 0.75, 0.9]).T
print(f"{(df['target'] < 5).sum() / len(df) * 100:.3f}% of the target values are less than 5") | code |
73074342/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/30days-folds/train_folds.csv')
train = df
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
# Comparing the datasets length
fig, ax = plt.subplots(figsize=(5, 5))
pie = ax.pie([len(df), len(df_test)],
labels=["Train dataset", "Test dataset"],
colors=["salmon", "teal"],
textprops={"fontsize": 15},
autopct='%1.1f%%')
ax.axis("equal")
ax.set_title("Dataset length comparison", fontsize=18)
fig.set_facecolor('white')
plt.show();
df.describe(percentiles=[0.1, 0.25, 0.5, 0.75, 0.9]).T
fig, ax = plt.subplots(figsize=(16, 8))
bars = ax.hist(df['target'], bins=100, color='palevioletred', edgecolor='black')
ax.set_title('Target distribution', fontsize=20, pad=15)
ax.set_ylabel('Amount of values', fontsize=14, labelpad=15)
ax.set_xlabel('Target value', fontsize=14, labelpad=10)
ax.margins(0.025, 0.12)
ax.grid(axis='y')
plt.show() | code |
73074342/cell_15 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import random
import seaborn as sns
df = pd.read_csv('../input/30days-folds/train_folds.csv')
train = df
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
colors = ['lightcoral', 'sandybrown', 'darkorange', 'mediumseagreen', 'lightseagreen', 'cornflowerblue', 'mediumpurple', 'palevioletred', 'lightskyblue', 'sandybrown', 'yellowgreen', 'indianred', 'lightsteelblue', 'mediumorchid', 'deepskyblue']
# Comparing the datasets length
fig, ax = plt.subplots(figsize=(5, 5))
pie = ax.pie([len(df), len(df_test)],
labels=["Train dataset", "Test dataset"],
colors=["salmon", "teal"],
textprops={"fontsize": 15},
autopct='%1.1f%%')
ax.axis("equal")
ax.set_title("Dataset length comparison", fontsize=18)
fig.set_facecolor('white')
plt.show();
df.describe(percentiles=[0.1, 0.25, 0.5, 0.75, 0.9]).T
fig, ax = plt.subplots(figsize=(16, 8))
bars = ax.hist(df["target"],
bins=100,
color="palevioletred",
edgecolor="black")
ax.set_title("Target distribution", fontsize=20, pad=15)
ax.set_ylabel("Amount of values", fontsize=14, labelpad=15)
ax.set_xlabel("Target value", fontsize=14, labelpad=10)
ax.margins(0.025, 0.12)
ax.grid(axis="y")
plt.show();
cat_features = ['cat' + str(i) for i in range(10)]
num_features = ['cont' + str(i) for i in range(14)]
# Combined dataframe containing numerical features only
df = pd.concat([df[num_features], df_test[num_features]], axis=0)
columns = df.columns.values
# Calculating required amount of rows to display all feature plots
cols = 3
rows = len(columns) // cols + 1
fig, axs = plt.subplots(ncols=cols, nrows=rows, figsize=(16,20), sharex=False)
# Adding some distance between plots
plt.subplots_adjust(hspace = 0.3)
# Plots counter
i=0
for r in np.arange(0, rows, 1):
for c in np.arange(0, cols, 1):
if i >= len(columns): # If there is no more data columns to make plots from
axs[r, c].set_visible(False) # Hiding axes so there will be clean background
else:
# Train data histogram
hist1 = axs[r, c].hist(df[columns[i]].values,
range=(df[columns[i]].min(),
df[columns[i]].max()),
bins=40,
color="deepskyblue",
edgecolor="black",
alpha=0.7,
label="Train Dataset")
# Test data histogram
hist2 = axs[r, c].hist(df_test[columns[i]].values,
range=(df[columns[i]].min(),
df[columns[i]].max()),
bins=40,
color="palevioletred",
edgecolor="black",
alpha=0.7,
label="Test Dataset")
axs[r, c].set_title(columns[i], fontsize=14, pad=5)
axs[r, c].tick_params(axis="y", labelsize=13)
axs[r, c].tick_params(axis="x", labelsize=13)
axs[r, c].grid(axis="y")
axs[r, c].legend(fontsize=13)
i+=1
# plt.suptitle("Numerical feature values distribution in both datasets", y=0.99)
plt.show();
# Bars position should be numerical because there will be arithmetical operations with them
bars_pos = np.arange(len(cat_features))
width=0.3
fig, ax = plt.subplots(figsize=(14, 6))
# Making two bar objects. One is on the left from bar position and the other one is on the right
bars1 = ax.bar(bars_pos-width/2,
train[cat_features].nunique().values,
width=width,
color="darkorange", edgecolor="black")
bars2 = ax.bar(bars_pos+width/2,
train[cat_features].nunique().values,
width=width,
color="steelblue", edgecolor="black")
ax.set_title("Amount of values in categorical features", fontsize=20, pad=15)
ax.set_xlabel("Categorical feature", fontsize=15, labelpad=15)
ax.set_ylabel("Amount of values", fontsize=15, labelpad=15)
ax.set_xticks(bars_pos)
ax.set_xticklabels(cat_features, fontsize=12)
ax.tick_params(axis="y", labelsize=12)
ax.grid(axis="y")
plt.margins(0.01, 0.05)
# Plot dataframe
#df = train.drop("id", axis=1)
# Encoding categorical features with OrdinalEncoder
'''for col in cat_features:
encoder = OrdinalEncoder()
df[col] = encoder.fit_transform(np.array(df[col]).reshape(-1, 1))
'''
# Calculatin correlation values
df = df.corr().round(2)
# Mask to hide upper-right part of plot as it is a duplicate
mask = np.zeros_like(df)
mask[np.triu_indices_from(mask)] = True
# Making a plot
plt.figure(figsize=(14,14))
ax = sns.heatmap(df, annot=True, mask=mask, cmap="RdBu", annot_kws={"weight": "normal", "fontsize":9})
ax.set_title("Feature correlation heatmap", fontsize=17)
plt.setp(ax.get_xticklabels(), rotation=90, ha="right",
rotation_mode="anchor", weight="normal")
plt.setp(ax.get_yticklabels(), weight="normal",
rotation_mode="anchor", rotation=0, ha="right")
plt.show();
columns = train.drop(['target'], axis=1).columns.values
cols = 4
rows = len(columns) // cols + 1
fig, axs = plt.subplots(ncols=cols, nrows=rows, figsize=(16, 20), sharex=False)
plt.subplots_adjust(hspace=0.3)
i = 0
for r in np.arange(0, rows, 1):
for c in np.arange(0, cols, 1):
if i >= len(columns):
axs[r, c].set_visible(False)
else:
scatter = axs[r, c].scatter(train[columns[i]].values, train['target'], color=random.choice(colors))
axs[r, c].set_title(columns[i], fontsize=14, pad=5)
axs[r, c].tick_params(axis='y', labelsize=11)
axs[r, c].tick_params(axis='x', labelsize=11)
i += 1
plt.show() | code |
73074342/cell_3 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/30days-folds/train_folds.csv')
train = df
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
df.head() | code |
73074342/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/30days-folds/train_folds.csv')
train = df
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
# Comparing the datasets length
fig, ax = plt.subplots(figsize=(5, 5))
pie = ax.pie([len(df), len(df_test)],
labels=["Train dataset", "Test dataset"],
colors=["salmon", "teal"],
textprops={"fontsize": 15},
autopct='%1.1f%%')
ax.axis("equal")
ax.set_title("Dataset length comparison", fontsize=18)
fig.set_facecolor('white')
plt.show();
df.describe(percentiles=[0.1, 0.25, 0.5, 0.75, 0.9]).T
fig, ax = plt.subplots(figsize=(16, 8))
bars = ax.hist(df["target"],
bins=100,
color="palevioletred",
edgecolor="black")
ax.set_title("Target distribution", fontsize=20, pad=15)
ax.set_ylabel("Amount of values", fontsize=14, labelpad=15)
ax.set_xlabel("Target value", fontsize=14, labelpad=10)
ax.margins(0.025, 0.12)
ax.grid(axis="y")
plt.show();
cat_features = ['cat' + str(i) for i in range(10)]
num_features = ['cont' + str(i) for i in range(14)]
# Combined dataframe containing numerical features only
df = pd.concat([df[num_features], df_test[num_features]], axis=0)
columns = df.columns.values
# Calculating required amount of rows to display all feature plots
cols = 3
rows = len(columns) // cols + 1
fig, axs = plt.subplots(ncols=cols, nrows=rows, figsize=(16,20), sharex=False)
# Adding some distance between plots
plt.subplots_adjust(hspace = 0.3)
# Plots counter
i=0
for r in np.arange(0, rows, 1):
for c in np.arange(0, cols, 1):
if i >= len(columns): # If there is no more data columns to make plots from
axs[r, c].set_visible(False) # Hiding axes so there will be clean background
else:
# Train data histogram
hist1 = axs[r, c].hist(df[columns[i]].values,
range=(df[columns[i]].min(),
df[columns[i]].max()),
bins=40,
color="deepskyblue",
edgecolor="black",
alpha=0.7,
label="Train Dataset")
# Test data histogram
hist2 = axs[r, c].hist(df_test[columns[i]].values,
range=(df[columns[i]].min(),
df[columns[i]].max()),
bins=40,
color="palevioletred",
edgecolor="black",
alpha=0.7,
label="Test Dataset")
axs[r, c].set_title(columns[i], fontsize=14, pad=5)
axs[r, c].tick_params(axis="y", labelsize=13)
axs[r, c].tick_params(axis="x", labelsize=13)
axs[r, c].grid(axis="y")
axs[r, c].legend(fontsize=13)
i+=1
# plt.suptitle("Numerical feature values distribution in both datasets", y=0.99)
plt.show();
# Bars position should be numerical because there will be arithmetical operations with them
bars_pos = np.arange(len(cat_features))
width=0.3
fig, ax = plt.subplots(figsize=(14, 6))
# Making two bar objects. One is on the left from bar position and the other one is on the right
bars1 = ax.bar(bars_pos-width/2,
train[cat_features].nunique().values,
width=width,
color="darkorange", edgecolor="black")
bars2 = ax.bar(bars_pos+width/2,
train[cat_features].nunique().values,
width=width,
color="steelblue", edgecolor="black")
ax.set_title("Amount of values in categorical features", fontsize=20, pad=15)
ax.set_xlabel("Categorical feature", fontsize=15, labelpad=15)
ax.set_ylabel("Amount of values", fontsize=15, labelpad=15)
ax.set_xticks(bars_pos)
ax.set_xticklabels(cat_features, fontsize=12)
ax.tick_params(axis="y", labelsize=12)
ax.grid(axis="y")
plt.margins(0.01, 0.05)
"""for col in cat_features:
encoder = OrdinalEncoder()
df[col] = encoder.fit_transform(np.array(df[col]).reshape(-1, 1))
"""
df = df.corr().round(2)
mask = np.zeros_like(df)
mask[np.triu_indices_from(mask)] = True
plt.figure(figsize=(14, 14))
ax = sns.heatmap(df, annot=True, mask=mask, cmap='RdBu', annot_kws={'weight': 'normal', 'fontsize': 9})
ax.set_title('Feature correlation heatmap', fontsize=17)
plt.setp(ax.get_xticklabels(), rotation=90, ha='right', rotation_mode='anchor', weight='normal')
plt.setp(ax.get_yticklabels(), weight='normal', rotation_mode='anchor', rotation=0, ha='right')
plt.show() | code |
73074342/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('../input/30days-folds/train_folds.csv')
train = df
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
# Comparing the datasets length
fig, ax = plt.subplots(figsize=(5, 5))
pie = ax.pie([len(df), len(df_test)],
labels=["Train dataset", "Test dataset"],
colors=["salmon", "teal"],
textprops={"fontsize": 15},
autopct='%1.1f%%')
ax.axis("equal")
ax.set_title("Dataset length comparison", fontsize=18)
fig.set_facecolor('white')
plt.show();
df.describe(percentiles=[0.1, 0.25, 0.5, 0.75, 0.9]).T
fig, ax = plt.subplots(figsize=(16, 8))
bars = ax.hist(df["target"],
bins=100,
color="palevioletred",
edgecolor="black")
ax.set_title("Target distribution", fontsize=20, pad=15)
ax.set_ylabel("Amount of values", fontsize=14, labelpad=15)
ax.set_xlabel("Target value", fontsize=14, labelpad=10)
ax.margins(0.025, 0.12)
ax.grid(axis="y")
plt.show();
cat_features = ['cat' + str(i) for i in range(10)]
num_features = ['cont' + str(i) for i in range(14)]
df = pd.concat([df[num_features], df_test[num_features]], axis=0)
columns = df.columns.values
cols = 3
rows = len(columns) // cols + 1
fig, axs = plt.subplots(ncols=cols, nrows=rows, figsize=(16, 20), sharex=False)
plt.subplots_adjust(hspace=0.3)
i = 0
for r in np.arange(0, rows, 1):
for c in np.arange(0, cols, 1):
if i >= len(columns):
axs[r, c].set_visible(False)
else:
hist1 = axs[r, c].hist(df[columns[i]].values, range=(df[columns[i]].min(), df[columns[i]].max()), bins=40, color='deepskyblue', edgecolor='black', alpha=0.7, label='Train Dataset')
hist2 = axs[r, c].hist(df_test[columns[i]].values, range=(df[columns[i]].min(), df[columns[i]].max()), bins=40, color='palevioletred', edgecolor='black', alpha=0.7, label='Test Dataset')
axs[r, c].set_title(columns[i], fontsize=14, pad=5)
axs[r, c].tick_params(axis='y', labelsize=13)
axs[r, c].tick_params(axis='x', labelsize=13)
axs[r, c].grid(axis='y')
axs[r, c].legend(fontsize=13)
i += 1
plt.show() | code |
73074342/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('../input/30days-folds/train_folds.csv')
train = df
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
# Comparing the datasets length
fig, ax = plt.subplots(figsize=(5, 5))
pie = ax.pie([len(df), len(df_test)],
labels=["Train dataset", "Test dataset"],
colors=["salmon", "teal"],
textprops={"fontsize": 15},
autopct='%1.1f%%')
ax.axis("equal")
ax.set_title("Dataset length comparison", fontsize=18)
fig.set_facecolor('white')
plt.show();
df.describe(percentiles=[0.1, 0.25, 0.5, 0.75, 0.9]).T
fig, ax = plt.subplots(figsize=(16, 8))
bars = ax.hist(df["target"],
bins=100,
color="palevioletred",
edgecolor="black")
ax.set_title("Target distribution", fontsize=20, pad=15)
ax.set_ylabel("Amount of values", fontsize=14, labelpad=15)
ax.set_xlabel("Target value", fontsize=14, labelpad=10)
ax.margins(0.025, 0.12)
ax.grid(axis="y")
plt.show();
cat_features = ['cat' + str(i) for i in range(10)]
num_features = ['cont' + str(i) for i in range(14)]
# Combined dataframe containing numerical features only
df = pd.concat([df[num_features], df_test[num_features]], axis=0)
columns = df.columns.values
# Calculating required amount of rows to display all feature plots
cols = 3
rows = len(columns) // cols + 1
fig, axs = plt.subplots(ncols=cols, nrows=rows, figsize=(16,20), sharex=False)
# Adding some distance between plots
plt.subplots_adjust(hspace = 0.3)
# Plots counter
i=0
for r in np.arange(0, rows, 1):
for c in np.arange(0, cols, 1):
if i >= len(columns): # If there is no more data columns to make plots from
axs[r, c].set_visible(False) # Hiding axes so there will be clean background
else:
# Train data histogram
hist1 = axs[r, c].hist(df[columns[i]].values,
range=(df[columns[i]].min(),
df[columns[i]].max()),
bins=40,
color="deepskyblue",
edgecolor="black",
alpha=0.7,
label="Train Dataset")
# Test data histogram
hist2 = axs[r, c].hist(df_test[columns[i]].values,
range=(df[columns[i]].min(),
df[columns[i]].max()),
bins=40,
color="palevioletred",
edgecolor="black",
alpha=0.7,
label="Test Dataset")
axs[r, c].set_title(columns[i], fontsize=14, pad=5)
axs[r, c].tick_params(axis="y", labelsize=13)
axs[r, c].tick_params(axis="x", labelsize=13)
axs[r, c].grid(axis="y")
axs[r, c].legend(fontsize=13)
i+=1
# plt.suptitle("Numerical feature values distribution in both datasets", y=0.99)
plt.show();
bars_pos = np.arange(len(cat_features))
width = 0.3
fig, ax = plt.subplots(figsize=(14, 6))
bars1 = ax.bar(bars_pos - width / 2, train[cat_features].nunique().values, width=width, color='darkorange', edgecolor='black')
bars2 = ax.bar(bars_pos + width / 2, train[cat_features].nunique().values, width=width, color='steelblue', edgecolor='black')
ax.set_title('Amount of values in categorical features', fontsize=20, pad=15)
ax.set_xlabel('Categorical feature', fontsize=15, labelpad=15)
ax.set_ylabel('Amount of values', fontsize=15, labelpad=15)
ax.set_xticks(bars_pos)
ax.set_xticklabels(cat_features, fontsize=12)
ax.tick_params(axis='y', labelsize=12)
ax.grid(axis='y')
plt.margins(0.01, 0.05) | code |
73074342/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/30days-folds/train_folds.csv')
train = df
df_test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
fig, ax = plt.subplots(figsize=(5, 5))
pie = ax.pie([len(df), len(df_test)], labels=['Train dataset', 'Test dataset'], colors=['salmon', 'teal'], textprops={'fontsize': 15}, autopct='%1.1f%%')
ax.axis('equal')
ax.set_title('Dataset length comparison', fontsize=18)
fig.set_facecolor('white')
plt.show() | code |
128010348/cell_13 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/iris/Iris.csv')
df = df.dropna()
df = df.replace([np.inf, -np.inf], np.nan)
df = df.dropna()
df = df.drop(['Id'], axis=1)
scaler = MinMaxScaler()
cols_to_scale = df.columns[:-1]
df_norm = pd.DataFrame(scaler.fit_transform(df[cols_to_scale]), columns=cols_to_scale)
df_norm = pd.concat([df_norm, df.iloc[:, -1]], axis=1)
df = df_norm
df.info() | code |
128010348/cell_25 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from catboost import CatBoostClassifier, Pool
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
models = {'Logistic Regression': LogisticRegression(random_state=42), 'Random Forest': RandomForestClassifier(random_state=42), 'Gradient Boosting': GradientBoostingClassifier(random_state=42), 'Support Vector Machines': SVC(random_state=42), 'K-Nearest': KNeighborsClassifier(), 'XGB': XGBClassifier(random_state=42), 'Cat': CatBoostClassifier(random_state=42), 'Decision Tree': DecisionTreeClassifier(random_state=42)}
params = {'Logistic Regression': {'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000], 'solver': ['newton-cg', 'lbfgs', 'liblinear']}, 'Random Forest': {'n_estimators': [10, 50, 100, 250, 500], 'max_depth': [5, 10, 20]}, 'Gradient Boosting': {'n_estimators': [10, 50, 100, 250, 500], 'learning_rate': [0.001, 0.005, 0.0001, 0.0005]}, 'Support Vector Machines': {'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000], 'kernel': ['linear', 'rbf']}, 'K-Nearest': {'n_neighbors': [3, 5, 7, 11, 21], 'weights': ['uniform', 'distance'], 'metric': ['euclidean', 'manhattan']}, 'XGB': {'max_depth': [5, 10, 20], 'n_estimators': [10, 50, 100, 250, 500], 'learning_rate': [0.001, 0.005, 0.0001, 0.0005]}, 'Cat': {'iterations': [50, 500, 5000], 'max_depth': [5, 10, 20], 'loss_function': ['Logloss', 'CrossEntropy', 'MultiClass'], 'learning_rate': [0.001, 0.005, 0.0001, 0.0005], 'eval_metric': ['MultiClass']}, 'Decision Tree': {'max_features': ['auto', 'sqrt', 'log2'], 'ccp_alpha': [0.1, 0.01, 0.001], 'max_depth': [5, 10, 20], 'criterion': ['gini', 'entropy']}}
results = []
for name, model in models.items():
clf = RandomizedSearchCV(model, params[name], cv=5, n_jobs=-1, scoring='accuracy')
clf.fit(X_train_ex, y_train_ex)
results.append({'model': name, 'best_score': clf.best_score_, 'best_params': clf.best_params_})
for result in results:
print(f"{result['model']}: Best score = {result['best_score']:.4f}, Best params = {result['best_params']}")
"\nLogistic Regression: Best score = 1.0000, Best params = {'solver': 'newton-cg', 'C': 1}\nRandom Forest: Best score = 1.0000, Best params = {'n_estimators': 50, 'max_depth': 10}\nGradient Boosting: Best score = 1.0000, Best params = {'n_estimators': 10, 'learning_rate': 0.005}\nSupport Vector Machines: Best score = 1.0000, Best params = {'kernel': 'rbf', 'C': 100}\nK-Nearest: Best score = 1.0000, Best params = {'weights': 'uniform', 'n_neighbors': 5, 'metric': 'manhattan'}\nXGB: Best score = 1.0000, Best params = {'n_estimators': 250, 'max_depth': 10, 'learning_rate': 0.0001}\nCat: Best score = 1.0000, Best params = {'max_depth': 10, 'loss_function': 'MultiClass', 'learning_rate': 0.005, 'iterations': 50, 'eval_metric': 'MultiClass'}\nDecision Tree: Best score = 1.0000, Best params = {'max_features': 'sqrt', 'max_depth': 5, 'criterion': 'gini', 'ccp_alpha': 0.001}\n" | code |
128010348/cell_30 | [
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"application_vnd.jupyter.stderr_output_16.png",
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"application_vnd.jupyter.stderr_output_30.png",
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"application_vnd.jupyter.stderr_output_28.png",
"application_vnd.jupyter.stderr_output_46.png",
"text_plain_output_41.png",
"text_plain_output_53.png",
"application_vnd.jupyter.stderr_output_20.png",
"text_plain_output_23.png",
"application_vnd.jupyter.stderr_output_36.png",
"application_vnd.jupyter.stderr_output_22.png",
"text_plain_output_51.png",
"application_vnd.jupyter.stderr_output_56.png",
"application_vnd.jupyter.stderr_output_50.png",
"text_plain_output_1.png",
"text_plain_output_33.png",
"text_plain_output_39.png",
"text_plain_output_55.png",
"text_plain_output_19.png",
"text_plain_output_17.png",
"text_plain_output_11.png",
"application_vnd.jupyter.stderr_output_14.png",
"application_vnd.jupyter.stderr_output_54.png",
"text_plain_output_61.png",
"application_vnd.jupyter.stderr_output_40.png"
] | from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from keras.models import Sequential, Model, load_model
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from tensorflow.keras import regularizers
from tensorflow.keras.layers import Conv2D, MaxPool2D, Activation, Dropout, BatchNormalization, LeakyReLU
from tensorflow.keras.layers import Input, Dense, Flatten, GlobalAveragePooling2D, concatenate
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.optimizers import Adam, RMSprop
from tensorflow.keras.wrappers.scikit_learn import KerasClassifier
y_train_ex = to_categorical(y_train_ex)
y_test_ex = to_categorical(y_test_ex)
def create_model(neurons, dropout_rate, kernel_regularizer, learning_rate):
input_shape = (X_train_ex.shape[1],)
model = Sequential()
model.add(Dense(neurons, activation='relu', input_shape=input_shape))
model.add(Dropout(dropout_rate))
model.add(Dense(neurons // 2, activation='relu'))
model.add(Dropout(dropout_rate))
model.add(Dense(neurons // 4, activation='relu'))
model.add(Dropout(dropout_rate))
model.add(Dense(neurons // 8, activation='relu', kernel_regularizer=regularizers.l2(kernel_regularizer)))
model.add(Dropout(dropout_rate))
model.add(Dense(3, activation='softmax'))
opt = Adam(learning_rate=learning_rate)
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
return model
model = KerasClassifier(build_fn=create_model, verbose=0)
neurons = [64, 128, 256, 512, 1024, 2048]
dropout_rate = [0, 0.25, 0.5, 0.75]
kernel_regularizer = [0.01, 0.001, 0.0001]
learning_rate = [0.01, 0.05, 0.001, 0.005, 0.0001, 0.0005]
batch_size = [16, 32, 64]
epochs = [50, 100, 150, 300, 500, 1000]
param_grid = dict(neurons=neurons, dropout_rate=dropout_rate, kernel_regularizer=kernel_regularizer, learning_rate=learning_rate, batch_size=batch_size, epochs=epochs)
n_iter_search = 50
random_search = RandomizedSearchCV(model, param_distributions=param_grid, n_iter=n_iter_search, cv=5, n_jobs=-1, scoring='accuracy')
random_search.fit(X_train_ex, y_train_ex)
print('Best parameters: ', random_search.best_params_)
print('Best score: ', random_search.best_score_) | code |
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