path
stringlengths 13
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sequencelengths 1
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stringlengths 0
40.4k
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stringclasses 1
value |
|---|---|---|---|
33096987/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
train_df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
33096987/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
train_df.head() | code |
33096987/cell_39 | [
"text_html_output_1.png"
] | from collections import Counter
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 warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn-whitegrid')
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
def barplot(variable):
"""
input : variable example: "Sex"
output : barplot & value count
"""
var = train_df[variable]
varValue = var.value_counts()
plt.xticks(varValue.index, varValue.index.values)
def plothist(variable):
pass
def detect_outliers(df, features):
outlier_indices = []
for c in features:
q1 = np.percentile(df[c], 25)
q3 = np.percentile(df[c], 75)
IQR = q3 - q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < q1 - outlier_step) | (df[c] > q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'Parch', 'SibSp', 'Fare'])]
train_df = train_df.drop(detect_outliers(train_df, ['Age', 'Parch', 'SibSp', 'Fare']), axis=0).reset_index(drop=True)
train_df_len = len(train_df)
train_df = pd.concat([train_df, test_df], axis=0).reset_index(drop=True)
train_df.columns[train_df.isnull().any()]
train_df.isnull().sum()
train_df[train_df['Fare'].isnull()] | code |
33096987/cell_26 | [
"text_html_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
def detect_outliers(df, features):
outlier_indices = []
for c in features:
q1 = np.percentile(df[c], 25)
q3 = np.percentile(df[c], 75)
IQR = q3 - q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < q1 - outlier_step) | (df[c] > q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'Parch', 'SibSp', 'Fare'])] | code |
33096987/cell_48 | [
"text_html_output_1.png"
] | from collections import Counter
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 seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn-whitegrid')
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
def barplot(variable):
"""
input : variable example: "Sex"
output : barplot & value count
"""
var = train_df[variable]
varValue = var.value_counts()
plt.xticks(varValue.index, varValue.index.values)
def plothist(variable):
pass
def detect_outliers(df, features):
outlier_indices = []
for c in features:
q1 = np.percentile(df[c], 25)
q3 = np.percentile(df[c], 75)
IQR = q3 - q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < q1 - outlier_step) | (df[c] > q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'Parch', 'SibSp', 'Fare'])]
train_df = train_df.drop(detect_outliers(train_df, ['Age', 'Parch', 'SibSp', 'Fare']), axis=0).reset_index(drop=True)
train_df_len = len(train_df)
train_df = pd.concat([train_df, test_df], axis=0).reset_index(drop=True)
train_df.columns[train_df.isnull().any()]
train_df.isnull().sum()
list1 = ['SibSp', 'Age', 'Fare', 'Parch', 'Survived']
g = sns.factorplot(x="SibSp",y="Survived",data=train_df,kind="bar",size=6)
g.set_ylabels("Survived Probability")
g = sns.factorplot(x='Parch', y='Survived', data=train_df, kind='bar', size=6)
g.set_ylabels('Survived Probability') | code |
33096987/cell_2 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn-whitegrid')
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
33096987/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
train_df.describe() | code |
33096987/cell_45 | [
"text_html_output_1.png"
] | from collections import Counter
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 seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn-whitegrid')
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
def barplot(variable):
"""
input : variable example: "Sex"
output : barplot & value count
"""
var = train_df[variable]
varValue = var.value_counts()
plt.xticks(varValue.index, varValue.index.values)
def plothist(variable):
pass
def detect_outliers(df, features):
outlier_indices = []
for c in features:
q1 = np.percentile(df[c], 25)
q3 = np.percentile(df[c], 75)
IQR = q3 - q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < q1 - outlier_step) | (df[c] > q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'Parch', 'SibSp', 'Fare'])]
train_df = train_df.drop(detect_outliers(train_df, ['Age', 'Parch', 'SibSp', 'Fare']), axis=0).reset_index(drop=True)
train_df_len = len(train_df)
train_df = pd.concat([train_df, test_df], axis=0).reset_index(drop=True)
train_df.columns[train_df.isnull().any()]
train_df.isnull().sum()
list1 = ['SibSp', 'Age', 'Fare', 'Parch', 'Survived']
g = sns.factorplot(x='SibSp', y='Survived', data=train_df, kind='bar', size=6)
g.set_ylabels('Survived Probability') | code |
33096987/cell_18 | [
"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 warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn-whitegrid')
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
def barplot(variable):
"""
input : variable example: "Sex"
output : barplot & value count
"""
var = train_df[variable]
varValue = var.value_counts()
plt.xticks(varValue.index, varValue.index.values)
def plothist(variable):
pass
numericVar = ['Fare', 'Age', 'PassengerId']
for i in numericVar:
plothist(i) | code |
33096987/cell_32 | [
"text_html_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
def detect_outliers(df, features):
outlier_indices = []
for c in features:
q1 = np.percentile(df[c], 25)
q3 = np.percentile(df[c], 75)
IQR = q3 - q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < q1 - outlier_step) | (df[c] > q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'Parch', 'SibSp', 'Fare'])]
train_df = train_df.drop(detect_outliers(train_df, ['Age', 'Parch', 'SibSp', 'Fare']), axis=0).reset_index(drop=True)
train_df_len = len(train_df)
train_df = pd.concat([train_df, test_df], axis=0).reset_index(drop=True)
train_df.columns[train_df.isnull().any()]
train_df.isnull().sum() | code |
33096987/cell_51 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from collections import Counter
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 seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn-whitegrid')
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
def barplot(variable):
"""
input : variable example: "Sex"
output : barplot & value count
"""
var = train_df[variable]
varValue = var.value_counts()
plt.xticks(varValue.index, varValue.index.values)
def plothist(variable):
pass
def detect_outliers(df, features):
outlier_indices = []
for c in features:
q1 = np.percentile(df[c], 25)
q3 = np.percentile(df[c], 75)
IQR = q3 - q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < q1 - outlier_step) | (df[c] > q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'Parch', 'SibSp', 'Fare'])]
train_df = train_df.drop(detect_outliers(train_df, ['Age', 'Parch', 'SibSp', 'Fare']), axis=0).reset_index(drop=True)
train_df_len = len(train_df)
train_df = pd.concat([train_df, test_df], axis=0).reset_index(drop=True)
train_df.columns[train_df.isnull().any()]
train_df.isnull().sum()
list1 = ['SibSp', 'Age', 'Fare', 'Parch', 'Survived']
g = sns.factorplot(x="SibSp",y="Survived",data=train_df,kind="bar",size=6)
g.set_ylabels("Survived Probability")
g = sns.factorplot(x="Parch",y="Survived",data=train_df,kind="bar",size=6)
g.set_ylabels("Survived Probability")
g = sns.factorplot(x='Pclass', y='Survived', data=train_df, kind='bar', size=6)
g.set_ylabels('Survived Probability') | code |
33096987/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
category2 = ['Cabin', 'Name', 'Ticket']
for i in category2:
print(f'{train_df[i].value_counts()} \n') | code |
33096987/cell_35 | [
"text_html_output_1.png"
] | from collections import Counter
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 warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn-whitegrid')
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
def barplot(variable):
"""
input : variable example: "Sex"
output : barplot & value count
"""
var = train_df[variable]
varValue = var.value_counts()
plt.xticks(varValue.index, varValue.index.values)
def plothist(variable):
pass
def detect_outliers(df, features):
outlier_indices = []
for c in features:
q1 = np.percentile(df[c], 25)
q3 = np.percentile(df[c], 75)
IQR = q3 - q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < q1 - outlier_step) | (df[c] > q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'Parch', 'SibSp', 'Fare'])]
train_df = train_df.drop(detect_outliers(train_df, ['Age', 'Parch', 'SibSp', 'Fare']), axis=0).reset_index(drop=True)
train_df_len = len(train_df)
train_df = pd.concat([train_df, test_df], axis=0).reset_index(drop=True)
train_df.columns[train_df.isnull().any()]
train_df.isnull().sum()
train_df.boxplot(column='Fare', by='Embarked')
plt.show() | code |
33096987/cell_31 | [
"text_html_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
def detect_outliers(df, features):
outlier_indices = []
for c in features:
q1 = np.percentile(df[c], 25)
q3 = np.percentile(df[c], 75)
IQR = q3 - q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < q1 - outlier_step) | (df[c] > q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'Parch', 'SibSp', 'Fare'])]
train_df = train_df.drop(detect_outliers(train_df, ['Age', 'Parch', 'SibSp', 'Fare']), axis=0).reset_index(drop=True)
train_df_len = len(train_df)
train_df = pd.concat([train_df, test_df], axis=0).reset_index(drop=True)
train_df.columns[train_df.isnull().any()] | code |
33096987/cell_14 | [
"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 warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn-whitegrid')
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
def barplot(variable):
"""
input : variable example: "Sex"
output : barplot & value count
"""
var = train_df[variable]
varValue = var.value_counts()
plt.xticks(varValue.index, varValue.index.values)
category1 = ['Survived', 'Sex', 'Pclass', 'Embarked', 'SibSp', 'Parch']
for i in category1:
barplot(i) | code |
33096987/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
train_df[['SibSp', 'Survived']].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
33096987/cell_37 | [
"text_plain_output_1.png"
] | from collections import Counter
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 warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn-whitegrid')
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
def barplot(variable):
"""
input : variable example: "Sex"
output : barplot & value count
"""
var = train_df[variable]
varValue = var.value_counts()
plt.xticks(varValue.index, varValue.index.values)
def plothist(variable):
pass
def detect_outliers(df, features):
outlier_indices = []
for c in features:
q1 = np.percentile(df[c], 25)
q3 = np.percentile(df[c], 75)
IQR = q3 - q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < q1 - outlier_step) | (df[c] > q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'Parch', 'SibSp', 'Fare'])]
train_df = train_df.drop(detect_outliers(train_df, ['Age', 'Parch', 'SibSp', 'Fare']), axis=0).reset_index(drop=True)
train_df_len = len(train_df)
train_df = pd.concat([train_df, test_df], axis=0).reset_index(drop=True)
train_df.columns[train_df.isnull().any()]
train_df.isnull().sum()
train_df[train_df['Fare'].isnull()] | code |
33096987/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns | code |
33096987/cell_36 | [
"text_plain_output_1.png"
] | from collections import Counter
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 warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn-whitegrid')
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
def barplot(variable):
"""
input : variable example: "Sex"
output : barplot & value count
"""
var = train_df[variable]
varValue = var.value_counts()
plt.xticks(varValue.index, varValue.index.values)
def plothist(variable):
pass
def detect_outliers(df, features):
outlier_indices = []
for c in features:
q1 = np.percentile(df[c], 25)
q3 = np.percentile(df[c], 75)
IQR = q3 - q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < q1 - outlier_step) | (df[c] > q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'Parch', 'SibSp', 'Fare'])]
train_df = train_df.drop(detect_outliers(train_df, ['Age', 'Parch', 'SibSp', 'Fare']), axis=0).reset_index(drop=True)
train_df_len = len(train_df)
train_df = pd.concat([train_df, test_df], axis=0).reset_index(drop=True)
train_df.columns[train_df.isnull().any()]
train_df.isnull().sum()
train_df['Embarked'] = train_df['Embarked'].fillna('C')
train_df[train_df['Embarked'].isnull()] | code |
90148984/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
len(y) | code |
90148984/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
len(df) | code |
90148984/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_
model.coef_
X_test = df.drop(columns=['price'])[:10]
X_test
y_hat = model.predict(X_test)
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
model.score(X, y) | code |
90148984/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_
model.coef_
X_test = df.drop(columns=['price'])[:10]
X_test
y_hat = model.predict(X_test)
dc = pd.concat([df[:10].reset_index(), pd.Series(y_hat, name='predicted')], axis='columns')
dc | code |
90148984/cell_30 | [
"text_html_output_1.png"
] | import numpy as np
import seaborn as sns
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
with sns.plotting_context("notebook",font_scale=2.5):
g = sns.pairplot(dataset[['sqft_lot','sqft_above','price','sqft_living','bedrooms']],
hue='bedrooms', palette='tab20',height=6)
g.set(xticklabels=[]);
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
X_test = df.drop(columns=['price'])[:10]
X_test
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
X_b = np.c_[np.ones((10, 1)), X]
X = X.to_numpy()
y = y.to_numpy()
sns.lmplot(x='sqft_living', y='price', data=df, ci=None) | code |
90148984/cell_20 | [
"text_plain_output_1.png"
] | dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
X_test = df.drop(columns=['price'])[:10]
X_test | code |
90148984/cell_6 | [
"text_plain_output_1.png"
] | import seaborn as sns
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
sns.lineplot(x='yr_built', y='sqft_living', data=df, ci=None) | code |
90148984/cell_29 | [
"text_html_output_1.png"
] | import numpy as np
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
X_test = df.drop(columns=['price'])[:10]
X_test
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
X_b = np.c_[np.ones((10, 1)), X]
X = X.to_numpy()
y = y.to_numpy()
eta = 0.1
n_iterations = 10
m = 100
theta = np.random.randn(19, 1)
for iteration in range(n_iterations):
gradients = 2 / m * X_b.T.dot(X_b.dot(theta) - y)
theta = theta - eta * gradients
theta | code |
90148984/cell_2 | [
"text_plain_output_1.png"
] | dataset.columns | code |
90148984/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_
model.coef_ | code |
90148984/cell_1 | [
"text_html_output_1.png"
] | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sklearn.linear_model import LinearRegression
dataset = pd.read_csv('../input/kc-house-data/kc_house_data.csv')
dataset.head() | code |
90148984/cell_7 | [
"text_plain_output_1.png"
] | import seaborn as sns
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
sns.lmplot(x='bedrooms', y='price', data=df, ci=None) | code |
90148984/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_ | code |
90148984/cell_28 | [
"text_plain_output_1.png"
] | import numpy as np
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
X_test = df.drop(columns=['price'])[:10]
X_test
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
X_b = np.c_[np.ones((10, 1)), X]
X = X.to_numpy()
y = y.to_numpy()
len(y) | code |
90148984/cell_8 | [
"text_plain_output_1.png"
] | import seaborn as sns
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
with sns.plotting_context('notebook', font_scale=2.5):
g = sns.pairplot(dataset[['sqft_lot', 'sqft_above', 'price', 'sqft_living', 'bedrooms']], hue='bedrooms', palette='tab20', height=6)
g.set(xticklabels=[]) | code |
90148984/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
X.head() | code |
90148984/cell_16 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
model = LinearRegression()
model.fit(X, y) | code |
90148984/cell_3 | [
"text_plain_output_1.png"
] | dataset.columns
print(dataset.dtypes) | code |
90148984/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
model = LinearRegression()
model.fit(X, y)
model.score(X, y) | code |
90148984/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
y.head() | code |
90148984/cell_22 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_
model.coef_
X_test = df.drop(columns=['price'])[:10]
X_test
y_hat = model.predict(X_test)
y_hat | code |
90148984/cell_10 | [
"text_plain_output_1.png"
] | dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns | code |
90148984/cell_27 | [
"text_plain_output_1.png"
] | import numpy as np
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
X_test = df.drop(columns=['price'])[:10]
X_test
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
X_b = np.c_[np.ones((10, 1)), X]
X = X.to_numpy()
y = y.to_numpy()
len(X) | code |
90148984/cell_12 | [
"text_plain_output_1.png"
] | dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
df.columns
X = df.drop(columns=['price'])[:10]
y = df['price'][:10]
len(X) | code |
90148984/cell_5 | [
"text_plain_output_1.png"
] | import seaborn as sns
dataset.columns
df = dataset.drop(columns=['date', 'id'])
df1 = df.dropna()
sns.lmplot(x='price', y='sqft_living', data=df, ci=None) | code |
50212838/cell_13 | [
"text_html_output_1.png"
] | from sklearn.cluster import MiniBatchKMeans
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel
import networkx as nx
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
data['date_added'] = pd.to_datetime(data['date_added'])
data['year'] = data['date_added'].dt.year
data['month'] = data['date_added'].dt.month
data['day'] = data['date_added'].dt.day
data['directors'] = data['director'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
data['actors'] = data['cast'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
data['categories'] = data['listed_in'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
data['countries'] = data['country'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel
from sklearn.cluster import MiniBatchKMeans
text_content = data['description']
vector = TfidfVectorizer(max_df=0.3, min_df=1, stop_words='english', lowercase=True, use_idf=True, norm=u'l2', smooth_idf=True)
tfidf = vector.fit_transform(text_content)
kmeans = MiniBatchKMeans(n_clusters=200)
kmeans.fit(tfidf)
centers = kmeans.cluster_centers_.argsort()[:, ::-1]
terms = vector.get_feature_names()
request_transform = vector.transform(data['description'])
data['cluster'] = kmeans.predict(request_transform)
def find_similar(tfidf_matrix, index, top_n=5):
cosine_similarities = linear_kernel(tfidf_matrix[index:index + 1], tfidf_matrix).flatten()
related_docs_indices = [i for i in cosine_similarities.argsort()[::-1] if i != index]
return [index for index in related_docs_indices][0:top_n]
G = nx.Graph(label='NETFLIX')
for i, row in data.iterrows():
G.add_node(row['title'], key=row['show_id'], label='MOVIE', mtype=row['type'], rating=row['rating'])
for j in row['actors']:
G.add_node(j, label='PERSON')
G.add_edge(row['title'], j, label='ACTED_IN')
for j in row['directors']:
G.add_node(j, label='PERSON')
G.add_edge(row['title'], j, label='DIRECTED')
for j in row['categories']:
G.add_node(j, label='CAT')
G.add_edge(row['title'], j, label='CAT_IN')
for j in row['countries']:
G.add_node(j, label='COUNTRY')
G.add_edge(row['title'], j, label='COUNTRY_IN')
for i, row in data.iterrows():
similar = find_similar(tfidf, i, top_n=5)
for e in similar:
G.add_edge(row['title'], data['title'].loc[e], label='SIMILAR_TO')
G.number_of_nodes() | code |
50212838/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.cluster import MiniBatchKMeans
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
data['date_added'] = pd.to_datetime(data['date_added'])
data['year'] = data['date_added'].dt.year
data['month'] = data['date_added'].dt.month
data['day'] = data['date_added'].dt.day
data['directors'] = data['director'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
data['actors'] = data['cast'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
data['categories'] = data['listed_in'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
data['countries'] = data['country'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel
from sklearn.cluster import MiniBatchKMeans
text_content = data['description']
vector = TfidfVectorizer(max_df=0.3, min_df=1, stop_words='english', lowercase=True, use_idf=True, norm=u'l2', smooth_idf=True)
tfidf = vector.fit_transform(text_content)
kmeans = MiniBatchKMeans(n_clusters=200)
kmeans.fit(tfidf)
centers = kmeans.cluster_centers_.argsort()[:, ::-1]
terms = vector.get_feature_names()
request_transform = vector.transform(data['description'])
data['cluster'] = kmeans.predict(request_transform)
print(request_transform) | code |
50212838/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)) | code |
50212838/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
data['date_added'] = pd.to_datetime(data['date_added'])
data['year'] = data['date_added'].dt.year
data['month'] = data['date_added'].dt.month
data['day'] = data['date_added'].dt.day
data['directors'] = data['director'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
data['actors'] = data['cast'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
data['categories'] = data['listed_in'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
data['countries'] = data['country'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
data.head() | code |
50212838/cell_8 | [
"image_output_1.png"
] | from sklearn.cluster import MiniBatchKMeans
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
data['date_added'] = pd.to_datetime(data['date_added'])
data['year'] = data['date_added'].dt.year
data['month'] = data['date_added'].dt.month
data['day'] = data['date_added'].dt.day
data['directors'] = data['director'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
data['actors'] = data['cast'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
data['categories'] = data['listed_in'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
data['countries'] = data['country'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel
from sklearn.cluster import MiniBatchKMeans
text_content = data['description']
vector = TfidfVectorizer(max_df=0.3, min_df=1, stop_words='english', lowercase=True, use_idf=True, norm=u'l2', smooth_idf=True)
tfidf = vector.fit_transform(text_content)
kmeans = MiniBatchKMeans(n_clusters=200)
kmeans.fit(tfidf)
centers = kmeans.cluster_centers_.argsort()[:, ::-1]
terms = vector.get_feature_names()
request_transform = vector.transform(data['description'])
data['cluster'] = kmeans.predict(request_transform)
data['cluster'].value_counts().head() | code |
50212838/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.cluster import MiniBatchKMeans
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel
import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
data['date_added'] = pd.to_datetime(data['date_added'])
data['year'] = data['date_added'].dt.year
data['month'] = data['date_added'].dt.month
data['day'] = data['date_added'].dt.day
data['directors'] = data['director'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
data['actors'] = data['cast'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
data['categories'] = data['listed_in'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
data['countries'] = data['country'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel
from sklearn.cluster import MiniBatchKMeans
text_content = data['description']
vector = TfidfVectorizer(max_df=0.3, min_df=1, stop_words='english', lowercase=True, use_idf=True, norm=u'l2', smooth_idf=True)
tfidf = vector.fit_transform(text_content)
kmeans = MiniBatchKMeans(n_clusters=200)
kmeans.fit(tfidf)
centers = kmeans.cluster_centers_.argsort()[:, ::-1]
terms = vector.get_feature_names()
request_transform = vector.transform(data['description'])
data['cluster'] = kmeans.predict(request_transform)
def find_similar(tfidf_matrix, index, top_n=5):
cosine_similarities = linear_kernel(tfidf_matrix[index:index + 1], tfidf_matrix).flatten()
related_docs_indices = [i for i in cosine_similarities.argsort()[::-1] if i != index]
return [index for index in related_docs_indices][0:top_n]
G = nx.Graph(label='NETFLIX')
for i, row in data.iterrows():
G.add_node(row['title'], key=row['show_id'], label='MOVIE', mtype=row['type'], rating=row['rating'])
for j in row['actors']:
G.add_node(j, label='PERSON')
G.add_edge(row['title'], j, label='ACTED_IN')
for j in row['directors']:
G.add_node(j, label='PERSON')
G.add_edge(row['title'], j, label='DIRECTED')
for j in row['categories']:
G.add_node(j, label='CAT')
G.add_edge(row['title'], j, label='CAT_IN')
for j in row['countries']:
G.add_node(j, label='COUNTRY')
G.add_edge(row['title'], j, label='COUNTRY_IN')
for i, row in data.iterrows():
similar = find_similar(tfidf, i, top_n=5)
for e in similar:
G.add_edge(row['title'], data['title'].loc[e], label='SIMILAR_TO')
G.number_of_nodes()
G.number_of_edges()
def get_all_adj_nodes(list_in):
sub_graph = set()
for m in list_in:
sub_graph.add(m)
for e in G.neighbors(m):
sub_graph.add(e)
return list(sub_graph)
def draw_sub_graph(sub_graph):
subgraph = G.subgraph(sub_graph)
colors = []
for e in subgraph.nodes():
if G.nodes[e]['label'] == 'MOVIE':
colors.append('blue')
elif G.nodes[e]['label'] == 'PERSON':
colors.append('red')
elif G.nodes[e]['label'] == 'CAT':
colors.append('green')
elif G.nodes[e]['label'] == 'COUNTRY':
colors.append('yellow')
elif G.nodes[e]['label'] == 'SIMILAR_TO':
colors.append('orange')
list_in = ["Ocean's Twelve", "Ocean's Thirteen"]
plt.style.use('seaborn')
plt.rcParams['figure.figsize'] = [14, 14]
sub_graph = get_all_adj_nodes(list_in)
draw_sub_graph(sub_graph) | code |
50212838/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
data.head() | code |
50212838/cell_14 | [
"text_html_output_1.png"
] | from sklearn.cluster import MiniBatchKMeans
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel
import networkx as nx
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
data['date_added'] = pd.to_datetime(data['date_added'])
data['year'] = data['date_added'].dt.year
data['month'] = data['date_added'].dt.month
data['day'] = data['date_added'].dt.day
data['directors'] = data['director'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
data['actors'] = data['cast'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
data['categories'] = data['listed_in'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
data['countries'] = data['country'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel
from sklearn.cluster import MiniBatchKMeans
text_content = data['description']
vector = TfidfVectorizer(max_df=0.3, min_df=1, stop_words='english', lowercase=True, use_idf=True, norm=u'l2', smooth_idf=True)
tfidf = vector.fit_transform(text_content)
kmeans = MiniBatchKMeans(n_clusters=200)
kmeans.fit(tfidf)
centers = kmeans.cluster_centers_.argsort()[:, ::-1]
terms = vector.get_feature_names()
request_transform = vector.transform(data['description'])
data['cluster'] = kmeans.predict(request_transform)
def find_similar(tfidf_matrix, index, top_n=5):
cosine_similarities = linear_kernel(tfidf_matrix[index:index + 1], tfidf_matrix).flatten()
related_docs_indices = [i for i in cosine_similarities.argsort()[::-1] if i != index]
return [index for index in related_docs_indices][0:top_n]
G = nx.Graph(label='NETFLIX')
for i, row in data.iterrows():
G.add_node(row['title'], key=row['show_id'], label='MOVIE', mtype=row['type'], rating=row['rating'])
for j in row['actors']:
G.add_node(j, label='PERSON')
G.add_edge(row['title'], j, label='ACTED_IN')
for j in row['directors']:
G.add_node(j, label='PERSON')
G.add_edge(row['title'], j, label='DIRECTED')
for j in row['categories']:
G.add_node(j, label='CAT')
G.add_edge(row['title'], j, label='CAT_IN')
for j in row['countries']:
G.add_node(j, label='COUNTRY')
G.add_edge(row['title'], j, label='COUNTRY_IN')
for i, row in data.iterrows():
similar = find_similar(tfidf, i, top_n=5)
for e in similar:
G.add_edge(row['title'], data['title'].loc[e], label='SIMILAR_TO')
G.number_of_nodes()
G.number_of_edges() | code |
50212838/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
data['date_added'] = pd.to_datetime(data['date_added'])
data['year'] = data['date_added'].dt.year
data['month'] = data['date_added'].dt.month
data['day'] = data['date_added'].dt.day
data['directors'] = data['director'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
data['actors'] = data['cast'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
data['categories'] = data['listed_in'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
data['countries'] = data['country'].apply(lambda x: [] if pd.isna(x) else [i.strip() for i in x.split(',')])
print(data['cluster']) | code |
50212838/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/netflix-shows/netflix_titles.csv')
data.describe() | code |
1008986/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
import itertools
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score
import itertools
def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
thresh = cm.max() / 2.0
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j], horizontalalignment='center', color='white' if cm[i, j] > thresh else 'black')
plt.tight_layout()
def show_data(cm, title='Confusion matrix', print_res=0):
tp = cm[1, 1]
fn = cm[1, 0]
fp = cm[0, 1]
tn = cm[0, 0]
return (tp / (tp + fp), tp / (tp + fn), fp / (fp + tn))
df = pd.read_csv('..input/creditcard.csv')
y = np.array(df.Class.tolist())
df = df.drop('Class', 1)
df = df.drop('Time', 1)
df['Amount'] = StandardScaler().fit_transform(df['Amount'].values.reshape(-1, 1))
X = np.array(df.as_matrix())
lrn = LogisticRegression()
skf = StratifiedKFold(n_splits=5, shuffle=True)
for train_index, test_index in skf.split(X, y):
X_train, y_train = (X[train_index], y[train_index])
X_test, y_test = (X[test_index], y[test_index])
break
lrn.fit(X_train, y_train)
y_pred = lrn.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
if lrn.classes_[0] == 1:
cm = np.array([[cm[1, 1], cm[1, 0]], [cm[0, 1], cm[0, 0]]])
plot_confusion_matrix(cm, ['0', '1'])
pr, tpr, fpr = show_data(cm, print_res=1) | code |
1008986/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import itertools
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score
import itertools
def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
thresh = cm.max() / 2.0
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j], horizontalalignment='center', color='white' if cm[i, j] > thresh else 'black')
plt.tight_layout()
def show_data(cm, title='Confusion matrix', print_res=0):
tp = cm[1, 1]
fn = cm[1, 0]
fp = cm[0, 1]
tn = cm[0, 0]
return (tp / (tp + fp), tp / (tp + fn), fp / (fp + tn))
df = pd.read_csv('..input/creditcard.csv')
print(df.head(3))
y = np.array(df.Class.tolist())
df = df.drop('Class', 1)
df = df.drop('Time', 1)
df['Amount'] = StandardScaler().fit_transform(df['Amount'].values.reshape(-1, 1))
X = np.array(df.as_matrix()) | code |
1008986/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
import itertools
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score
import itertools
def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
thresh = cm.max() / 2.0
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j], horizontalalignment='center', color='white' if cm[i, j] > thresh else 'black')
plt.tight_layout()
def show_data(cm, title='Confusion matrix', print_res=0):
tp = cm[1, 1]
fn = cm[1, 0]
fp = cm[0, 1]
tn = cm[0, 0]
return (tp / (tp + fp), tp / (tp + fn), fp / (fp + tn))
df = pd.read_csv('..input/creditcard.csv')
y = np.array(df.Class.tolist())
df = df.drop('Class', 1)
df = df.drop('Time', 1)
df['Amount'] = StandardScaler().fit_transform(df['Amount'].values.reshape(-1, 1))
X = np.array(df.as_matrix())
lrn = LogisticRegression()
skf = StratifiedKFold(n_splits = 5, shuffle = True)
for train_index, test_index in skf.split(X, y):
X_train, y_train = X[train_index], y[train_index]
X_test, y_test = X[test_index], y[test_index]
break
lrn.fit(X_train, y_train)
y_pred = lrn.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
if lrn.classes_[0] == 1:
cm = np.array([[cm[1,1], cm[1,0]], [cm[0,1], cm[0,0]]])
plot_confusion_matrix(cm, ['0', '1'], )
pr, tpr, fpr = show_data(cm, print_res = 1);
def ROC(X, y, c, r):
dic_weight = {1: len(y) / (r * np.sum(y)), 0: len(y) / (len(y) - r * np.sum(y))}
lrn = LogisticRegression(penalty='l2', C=c, class_weight=dic_weight)
N = 5
N_iter = 7
mean_tpr = 0.0
mean_thresh = 0.0
mean_fpr = np.linspace(0, 1, 50000)
for it in range(N_iter):
skf = StratifiedKFold(n_splits=N, shuffle=True)
for train_index, test_index in skf.split(X, y):
X_train, y_train = (X[train_index], y[train_index])
X_test, y_test = (X[test_index], y[test_index])
lrn.fit(X_train, y_train)
y_prob = lrn.predict_proba(X_test)[:, lrn.classes_[1]]
fpr, tpr, thresholds = roc_curve(y_test, y_prob)
mean_tpr += np.interp(mean_fpr, fpr, tpr)
mean_thresh += np.interp(mean_fpr, fpr, thresholds)
mean_tpr[0] = 0.0
mean_tpr /= N * N_iter
mean_thresh /= N * N_iter
mean_tpr[-1] = 1.0
return (mean_fpr, mean_tpr, roc_auc_score(y_test, y_prob), mean_thresh)
N = np.arange(10, 80, 2)
cm = {}
for n in N:
cm[n] = 0.0
lrn = LogisticRegression(penalty='l2', C=1, class_weight='balanced')
N_Kfold = 5
N_iter = 5
for it in range(N_iter):
skf = StratifiedKFold(n_splits=N_Kfold, shuffle=True)
for train_index, test_index in skf.split(X, y):
X_train, y_train = (X[train_index], y[train_index])
X_test, y_test = (X[test_index], y[test_index])
lrn.fit(X_train, y_train)
y_prob = lrn.predict_proba(X_test)[:, lrn.classes_[1]]
for n in N:
thresh = 1 - np.power(10.0, -(n / 10))
y_pred = np.zeros(len(y_prob))
for j in range(len(y_prob)):
if y_prob[j] > thresh:
y_pred[j] = 1
B = confusion_matrix(y_test, y_pred)
if lrn.classes_[0] == 1:
B = np.array([[B[1, 1], B[1, 0]], [B[0, 1], B[0, 0]]])
cm[n] += B
for n in N:
cm[n] = cm[n] // (N_Kfold * N_iter) | code |
1008986/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import itertools
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score
import itertools
def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
thresh = cm.max() / 2.0
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j], horizontalalignment='center', color='white' if cm[i, j] > thresh else 'black')
plt.tight_layout()
def show_data(cm, title='Confusion matrix', print_res=0):
tp = cm[1, 1]
fn = cm[1, 0]
fp = cm[0, 1]
tn = cm[0, 0]
return (tp / (tp + fp), tp / (tp + fn), fp / (fp + tn))
df = pd.read_csv('..input/creditcard.csv')
y = np.array(df.Class.tolist())
df = df.drop('Class', 1)
df = df.drop('Time', 1)
df['Amount'] = StandardScaler().fit_transform(df['Amount'].values.reshape(-1, 1))
X = np.array(df.as_matrix())
print('Fraction of frauds: {:.5f}'.format(np.sum(y) / len(y))) | code |
1008986/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
import itertools
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score
import itertools
def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
thresh = cm.max() / 2.0
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j], horizontalalignment='center', color='white' if cm[i, j] > thresh else 'black')
plt.tight_layout()
def show_data(cm, title='Confusion matrix', print_res=0):
tp = cm[1, 1]
fn = cm[1, 0]
fp = cm[0, 1]
tn = cm[0, 0]
return (tp / (tp + fp), tp / (tp + fn), fp / (fp + tn))
df = pd.read_csv('..input/creditcard.csv')
y = np.array(df.Class.tolist())
df = df.drop('Class', 1)
df = df.drop('Time', 1)
df['Amount'] = StandardScaler().fit_transform(df['Amount'].values.reshape(-1, 1))
X = np.array(df.as_matrix())
lrn = LogisticRegression()
skf = StratifiedKFold(n_splits = 5, shuffle = True)
for train_index, test_index in skf.split(X, y):
X_train, y_train = X[train_index], y[train_index]
X_test, y_test = X[test_index], y[test_index]
break
lrn.fit(X_train, y_train)
y_pred = lrn.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
if lrn.classes_[0] == 1:
cm = np.array([[cm[1,1], cm[1,0]], [cm[0,1], cm[0,0]]])
plot_confusion_matrix(cm, ['0', '1'], )
pr, tpr, fpr = show_data(cm, print_res = 1);
def ROC(X, y, c, r):
dic_weight = {1: len(y) / (r * np.sum(y)), 0: len(y) / (len(y) - r * np.sum(y))}
lrn = LogisticRegression(penalty='l2', C=c, class_weight=dic_weight)
N = 5
N_iter = 7
mean_tpr = 0.0
mean_thresh = 0.0
mean_fpr = np.linspace(0, 1, 50000)
for it in range(N_iter):
skf = StratifiedKFold(n_splits=N, shuffle=True)
for train_index, test_index in skf.split(X, y):
X_train, y_train = (X[train_index], y[train_index])
X_test, y_test = (X[test_index], y[test_index])
lrn.fit(X_train, y_train)
y_prob = lrn.predict_proba(X_test)[:, lrn.classes_[1]]
fpr, tpr, thresholds = roc_curve(y_test, y_prob)
mean_tpr += np.interp(mean_fpr, fpr, tpr)
mean_thresh += np.interp(mean_fpr, fpr, thresholds)
mean_tpr[0] = 0.0
mean_tpr /= N * N_iter
mean_thresh /= N * N_iter
mean_tpr[-1] = 1.0
return (mean_fpr, mean_tpr, roc_auc_score(y_test, y_prob), mean_thresh)
def plot_roc(X,y, list_par_1, par_1 = 'C', par_2 = 1):
f = plt.figure(figsize = (12,8));
for p in list_par_1:
if par_1 == 'C':
c = p
r = par_2
else:
r = p
c = par_2
list_FP, list_TP, AUC, mean_thresh = ROC(X, y, c, r)
plt.plot(list_FP, list_TP, label = 'C = {}, r = {}, TPR(3e-4) = {:.4f}'.format(c,r,list_TP[10]));
plt.legend(title = 'values', loc='lower right')
plt.xlim(0, 0.001) #we are only interested in small values of FPR
plt.ylim(0.5, 0.9)
plt.xlabel('FPR')
plt.ylabel('TPR')
plt.title('ROC detail')
plt.axvline(3e-4, color='b', linestyle='dashed', linewidth=2)
plt.show()
plt.close()
plot_roc(X, y, [0.001, 0.01, 0.1, 1, 10, 100], 'C', 1) | code |
1008986/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
import itertools
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score
import itertools
def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
thresh = cm.max() / 2.0
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j], horizontalalignment='center', color='white' if cm[i, j] > thresh else 'black')
plt.tight_layout()
def show_data(cm, title='Confusion matrix', print_res=0):
tp = cm[1, 1]
fn = cm[1, 0]
fp = cm[0, 1]
tn = cm[0, 0]
return (tp / (tp + fp), tp / (tp + fn), fp / (fp + tn))
df = pd.read_csv('..input/creditcard.csv')
y = np.array(df.Class.tolist())
df = df.drop('Class', 1)
df = df.drop('Time', 1)
df['Amount'] = StandardScaler().fit_transform(df['Amount'].values.reshape(-1, 1))
X = np.array(df.as_matrix())
lrn = LogisticRegression()
skf = StratifiedKFold(n_splits = 5, shuffle = True)
for train_index, test_index in skf.split(X, y):
X_train, y_train = X[train_index], y[train_index]
X_test, y_test = X[test_index], y[test_index]
break
lrn.fit(X_train, y_train)
y_pred = lrn.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
if lrn.classes_[0] == 1:
cm = np.array([[cm[1,1], cm[1,0]], [cm[0,1], cm[0,0]]])
plot_confusion_matrix(cm, ['0', '1'], )
pr, tpr, fpr = show_data(cm, print_res = 1);
def ROC(X, y, c, r):
dic_weight = {1: len(y) / (r * np.sum(y)), 0: len(y) / (len(y) - r * np.sum(y))}
lrn = LogisticRegression(penalty='l2', C=c, class_weight=dic_weight)
N = 5
N_iter = 7
mean_tpr = 0.0
mean_thresh = 0.0
mean_fpr = np.linspace(0, 1, 50000)
for it in range(N_iter):
skf = StratifiedKFold(n_splits=N, shuffle=True)
for train_index, test_index in skf.split(X, y):
X_train, y_train = (X[train_index], y[train_index])
X_test, y_test = (X[test_index], y[test_index])
lrn.fit(X_train, y_train)
y_prob = lrn.predict_proba(X_test)[:, lrn.classes_[1]]
fpr, tpr, thresholds = roc_curve(y_test, y_prob)
mean_tpr += np.interp(mean_fpr, fpr, tpr)
mean_thresh += np.interp(mean_fpr, fpr, thresholds)
mean_tpr[0] = 0.0
mean_tpr /= N * N_iter
mean_thresh /= N * N_iter
mean_tpr[-1] = 1.0
return (mean_fpr, mean_tpr, roc_auc_score(y_test, y_prob), mean_thresh)
def plot_roc(X,y, list_par_1, par_1 = 'C', par_2 = 1):
f = plt.figure(figsize = (12,8));
for p in list_par_1:
if par_1 == 'C':
c = p
r = par_2
else:
r = p
c = par_2
list_FP, list_TP, AUC, mean_thresh = ROC(X, y, c, r)
plt.plot(list_FP, list_TP, label = 'C = {}, r = {}, TPR(3e-4) = {:.4f}'.format(c,r,list_TP[10]));
plt.legend(title = 'values', loc='lower right')
plt.xlim(0, 0.001) #we are only interested in small values of FPR
plt.ylim(0.5, 0.9)
plt.xlabel('FPR')
plt.ylabel('TPR')
plt.title('ROC detail')
plt.axvline(3e-4, color='b', linestyle='dashed', linewidth=2)
plt.show()
plt.close()
plot_roc(X, y, [1, 3, 10, 30, 100], 'r', 1) | code |
1008986/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
import itertools
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score
import itertools
def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
thresh = cm.max() / 2.0
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j], horizontalalignment='center', color='white' if cm[i, j] > thresh else 'black')
plt.tight_layout()
def show_data(cm, title='Confusion matrix', print_res=0):
tp = cm[1, 1]
fn = cm[1, 0]
fp = cm[0, 1]
tn = cm[0, 0]
return (tp / (tp + fp), tp / (tp + fn), fp / (fp + tn))
df = pd.read_csv('..input/creditcard.csv')
y = np.array(df.Class.tolist())
df = df.drop('Class', 1)
df = df.drop('Time', 1)
df['Amount'] = StandardScaler().fit_transform(df['Amount'].values.reshape(-1, 1))
X = np.array(df.as_matrix())
lrn = LogisticRegression()
skf = StratifiedKFold(n_splits = 5, shuffle = True)
for train_index, test_index in skf.split(X, y):
X_train, y_train = X[train_index], y[train_index]
X_test, y_test = X[test_index], y[test_index]
break
lrn.fit(X_train, y_train)
y_pred = lrn.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
if lrn.classes_[0] == 1:
cm = np.array([[cm[1,1], cm[1,0]], [cm[0,1], cm[0,0]]])
plot_confusion_matrix(cm, ['0', '1'], )
pr, tpr, fpr = show_data(cm, print_res = 1);
def ROC(X, y, c, r):
dic_weight = {1: len(y) / (r * np.sum(y)), 0: len(y) / (len(y) - r * np.sum(y))}
lrn = LogisticRegression(penalty='l2', C=c, class_weight=dic_weight)
N = 5
N_iter = 7
mean_tpr = 0.0
mean_thresh = 0.0
mean_fpr = np.linspace(0, 1, 50000)
for it in range(N_iter):
skf = StratifiedKFold(n_splits=N, shuffle=True)
for train_index, test_index in skf.split(X, y):
X_train, y_train = (X[train_index], y[train_index])
X_test, y_test = (X[test_index], y[test_index])
lrn.fit(X_train, y_train)
y_prob = lrn.predict_proba(X_test)[:, lrn.classes_[1]]
fpr, tpr, thresholds = roc_curve(y_test, y_prob)
mean_tpr += np.interp(mean_fpr, fpr, tpr)
mean_thresh += np.interp(mean_fpr, fpr, thresholds)
mean_tpr[0] = 0.0
mean_tpr /= N * N_iter
mean_thresh /= N * N_iter
mean_tpr[-1] = 1.0
return (mean_fpr, mean_tpr, roc_auc_score(y_test, y_prob), mean_thresh)
def plot_roc(X,y, list_par_1, par_1 = 'C', par_2 = 1):
f = plt.figure(figsize = (12,8));
for p in list_par_1:
if par_1 == 'C':
c = p
r = par_2
else:
r = p
c = par_2
list_FP, list_TP, AUC, mean_thresh = ROC(X, y, c, r)
plt.plot(list_FP, list_TP, label = 'C = {}, r = {}, TPR(3e-4) = {:.4f}'.format(c,r,list_TP[10]));
plt.legend(title = 'values', loc='lower right')
plt.xlim(0, 0.001) #we are only interested in small values of FPR
plt.ylim(0.5, 0.9)
plt.xlabel('FPR')
plt.ylabel('TPR')
plt.title('ROC detail')
plt.axvline(3e-4, color='b', linestyle='dashed', linewidth=2)
plt.show()
plt.close()
N = np.arange(10, 80, 2)
cm = {}
for n in N:
cm[n] = 0.0
lrn = LogisticRegression(penalty='l2', C=1, class_weight='balanced')
N_Kfold = 5
N_iter = 5
for it in range(N_iter):
skf = StratifiedKFold(n_splits=N_Kfold, shuffle=True)
for train_index, test_index in skf.split(X, y):
X_train, y_train = (X[train_index], y[train_index])
X_test, y_test = (X[test_index], y[test_index])
lrn.fit(X_train, y_train)
y_prob = lrn.predict_proba(X_test)[:, lrn.classes_[1]]
for n in N:
thresh = 1 - np.power(10.0, -(n / 10))
y_pred = np.zeros(len(y_prob))
for j in range(len(y_prob)):
if y_prob[j] > thresh:
y_pred[j] = 1
B = confusion_matrix(y_test, y_pred)
if lrn.classes_[0] == 1:
B = np.array([[B[1, 1], B[1, 0]], [B[0, 1], B[0, 0]]])
cm[n] += B
for n in N:
cm[n] = cm[n] // (N_Kfold * N_iter)
PR = []
TPR = []
FPR = []
THRESH = N
for n in N:
pr, tpr, fpr = show_data(cm[n], title='Results for threshold = 1-10^-{:.1f}'.format(n / 10))
PR.append(pr)
TPR.append(tpr)
FPR.append(-np.log(fpr) / 10)
g = plt.figure(figsize=(12, 8))
plt.plot(THRESH, PR, label='Precision')
plt.plot(THRESH, TPR, label='Recall (TPR)')
plt.plot(THRESH, FPR, label='-log(FPR)/10')
plt.axhline(-np.log(0.0003) / 10, color='b', linestyle='dashed', linewidth=2)
plt.title('Evaluation of the classifier')
plt.legend(loc='lower right')
plt.xlabel('-log(1-thresh)/log(10)')
plt.ylim(0.55, 0.9)
plt.show() | code |
90112109/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sb
data = pd.read_csv('../input/insurance/insurance.csv')
data.nunique()
data.isnull().sum()
data.corr()
cor = data.corr()
data2 = data.drop(['children', 'region'], axis=1)
sb.relplot(x='age', y='charges', hue='smoker', data=data2) | code |
90112109/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sb
data = pd.read_csv('../input/insurance/insurance.csv')
data.nunique()
data.isnull().sum()
data.corr()
cor = data.corr()
sb.heatmap(cor, xticklabels=cor.columns, yticklabels=cor.columns, annot=True) | code |
90112109/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/insurance/insurance.csv')
data.info() | code |
90112109/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/insurance/insurance.csv')
data.nunique() | code |
90112109/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/insurance/insurance.csv')
data.nunique()
data.isnull().sum() | code |
90112109/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/insurance/insurance.csv')
data.nunique()
data.isnull().sum()
data.corr() | code |
90112109/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/insurance/insurance.csv')
data.head() | code |
90112109/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sb
data = pd.read_csv('../input/insurance/insurance.csv')
data.nunique()
data.isnull().sum()
data.corr()
cor = data.corr()
data2 = data.drop(['children', 'region'], axis=1)
sb.displot(data2['charges']) | code |
90112109/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sb
data = pd.read_csv('../input/insurance/insurance.csv')
data.nunique()
data.isnull().sum()
data.corr()
cor = data.corr()
sb.pairplot(data) | code |
90112109/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sb
data = pd.read_csv('../input/insurance/insurance.csv')
data.nunique()
data.isnull().sum()
data.corr()
cor = data.corr()
data2 = data.drop(['children', 'region'], axis=1)
data2.head() | code |
90112109/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/insurance/insurance.csv')
data.describe() | code |
73074228/cell_13 | [
"text_html_output_1.png"
] | from pathlib import Path
import pandas as pd
INPUT = Path('../input/tabular-playground-series-aug-2021')
train = pd.read_csv(INPUT / 'train.csv')
test = pd.read_csv(INPUT / 'test.csv')
train.shape
train.info() | code |
73074228/cell_20 | [
"text_plain_output_1.png"
] | from pathlib import Path
import pandas as pd
INPUT = Path('../input/tabular-playground-series-aug-2021')
train = pd.read_csv(INPUT / 'train.csv')
test = pd.read_csv(INPUT / 'test.csv')
train.shape
train.isnull().any().sum()
y = train['loss']
y.head() | code |
73074228/cell_55 | [
"text_html_output_1.png"
] | from pathlib import Path
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV, KFold
from sklearn.preprocessing import StandardScaler
from xgboost import XGBRegressor
import numpy as np
import pandas as pd
INPUT = Path('../input/tabular-playground-series-aug-2021')
train = pd.read_csv(INPUT / 'train.csv')
test = pd.read_csv(INPUT / 'test.csv')
train.shape
train.isnull().any().sum()
test.isnull().any().sum()
ss_features = [col for col in test.columns if 'f' in col]
ss = StandardScaler()
train[ss_features] = ss.fit_transform(train[ss_features])
test[ss_features] = ss.transform(test[ss_features])
X = train
kf = KFold(n_splits=10, shuffle=True, random_state=42)
for fold, (train_indices, valid_indices) in enumerate(kf.split(X=X)):
X.loc[valid_indices, 'kfold'] = fold
useful_features = [col for col in X.columns if col not in ('loss', 'kfold')]
xgb_params = {'learning_rate': 0.013222817649672616, 'n_estimators': 12462, 'max_depth': 5, 'tree_method': 'gpu_hist', 'predictor': 'gpu_predictor'}
xgb = XGBRegressor(**xgb_params)
xgb_predictions = []
for fold in range(10):
xtrain = X[X.kfold != fold].reset_index(drop=True)
xvalid = X[X.kfold == fold].reset_index(drop=True)
ytrain = xtrain.loss
yvalid = xvalid.loss
xtrain = xtrain[useful_features]
xvalid = xvalid[useful_features]
model = xgb
model.fit(xtrain, ytrain, early_stopping_rounds=10, eval_set=[(xvalid, yvalid)], verbose=1000)
preds_valid = model.predict(xvalid)
test_preds = model.predict(test)
xgb_predictions.append(test_preds)
final_predictions = xgb_predictions
predictions = np.mean(np.column_stack(final_predictions), axis=1)
submission = pd.read_csv(INPUT / 'sample_submission.csv')
submission['loss'] = predictions
submission.to_csv('submission.csv', index=False)
submission | code |
73074228/cell_54 | [
"text_plain_output_1.png"
] | from pathlib import Path
import pandas as pd
INPUT = Path('../input/tabular-playground-series-aug-2021')
train = pd.read_csv(INPUT / 'train.csv')
test = pd.read_csv(INPUT / 'test.csv')
submission = pd.read_csv(INPUT / 'sample_submission.csv')
submission.head() | code |
73074228/cell_11 | [
"text_plain_output_1.png"
] | from pathlib import Path
import pandas as pd
INPUT = Path('../input/tabular-playground-series-aug-2021')
train = pd.read_csv(INPUT / 'train.csv')
test = pd.read_csv(INPUT / 'test.csv')
train.shape
train.head() | code |
73074228/cell_32 | [
"text_plain_output_1.png"
] | from pathlib import Path
from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV, KFold
import pandas as pd
INPUT = Path('../input/tabular-playground-series-aug-2021')
train = pd.read_csv(INPUT / 'train.csv')
test = pd.read_csv(INPUT / 'test.csv')
train.shape
train.isnull().any().sum()
X = train
kf = KFold(n_splits=10, shuffle=True, random_state=42)
for fold, (train_indices, valid_indices) in enumerate(kf.split(X=X)):
X.loc[valid_indices, 'kfold'] = fold
X.head() | code |
73074228/cell_8 | [
"text_html_output_1.png"
] | from xgboost import XGBRegressor
from catboost import CatBoostRegressor
from lightgbm import LGBMRegressor | code |
73074228/cell_15 | [
"text_plain_output_1.png"
] | from pathlib import Path
import pandas as pd
INPUT = Path('../input/tabular-playground-series-aug-2021')
train = pd.read_csv(INPUT / 'train.csv')
test = pd.read_csv(INPUT / 'test.csv')
test.isnull().any().sum() | code |
73074228/cell_46 | [
"text_html_output_1.png"
] | from pathlib import Path
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV, KFold
from sklearn.preprocessing import StandardScaler
from xgboost import XGBRegressor
import pandas as pd
INPUT = Path('../input/tabular-playground-series-aug-2021')
train = pd.read_csv(INPUT / 'train.csv')
test = pd.read_csv(INPUT / 'test.csv')
train.shape
train.isnull().any().sum()
test.isnull().any().sum()
ss_features = [col for col in test.columns if 'f' in col]
ss = StandardScaler()
train[ss_features] = ss.fit_transform(train[ss_features])
test[ss_features] = ss.transform(test[ss_features])
X = train
kf = KFold(n_splits=10, shuffle=True, random_state=42)
for fold, (train_indices, valid_indices) in enumerate(kf.split(X=X)):
X.loc[valid_indices, 'kfold'] = fold
useful_features = [col for col in X.columns if col not in ('loss', 'kfold')]
xgb_params = {'learning_rate': 0.013222817649672616, 'n_estimators': 12462, 'max_depth': 5, 'tree_method': 'gpu_hist', 'predictor': 'gpu_predictor'}
xgb = XGBRegressor(**xgb_params)
xgb_predictions = []
for fold in range(10):
xtrain = X[X.kfold != fold].reset_index(drop=True)
xvalid = X[X.kfold == fold].reset_index(drop=True)
ytrain = xtrain.loss
yvalid = xvalid.loss
xtrain = xtrain[useful_features]
xvalid = xvalid[useful_features]
model = xgb
model.fit(xtrain, ytrain, early_stopping_rounds=10, eval_set=[(xvalid, yvalid)], verbose=1000)
preds_valid = model.predict(xvalid)
test_preds = model.predict(test)
xgb_predictions.append(test_preds)
print(f'fold: {fold}, rmse: {mean_squared_error(yvalid, preds_valid, squared=False)}') | code |
73074228/cell_14 | [
"text_plain_output_1.png"
] | from pathlib import Path
import pandas as pd
INPUT = Path('../input/tabular-playground-series-aug-2021')
train = pd.read_csv(INPUT / 'train.csv')
test = pd.read_csv(INPUT / 'test.csv')
train.shape
train.isnull().any().sum() | code |
73074228/cell_12 | [
"text_html_output_1.png"
] | from pathlib import Path
import pandas as pd
INPUT = Path('../input/tabular-playground-series-aug-2021')
train = pd.read_csv(INPUT / 'train.csv')
test = pd.read_csv(INPUT / 'test.csv')
test.head() | code |
17137542/cell_21 | [
"image_output_1.png"
] | from sklearn import manifold
from sklearn.cluster import KMeans
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/whisky.csv')
dist = []
for i in range(2, 20):
km = KMeans(n_clusters=i, n_init=10, max_iter=500, random_state=0)
km.fit(df.iloc[:, 2:-3])
dist.append(km.inertia_)
km = KMeans(n_clusters=5, n_init=10, max_iter=300, random_state=0)
df['class'] = km.fit_predict(df.iloc[:, 2:-3])
df['class'].values
mds = manifold.MDS(n_components=2, dissimilarity='euclidean', random_state=0)
pos = mds.fit_transform(df.iloc[:, 2:-4])
col = ['orange', 'green', 'blue', 'purple', 'red']
chars = '^<>vo+d'
c_flag = 0
labels = df['Distillery']
plt.rcParams['font.size'] = 15
for label, x, y, c in zip(labels, pos[:, 0], pos[:, 1], df['class']):
if c == c_flag:
c_flag = c_flag + 1
plt.annotate(label, xy=(x, y))
df.query('Distillery == "GlenSpey" or Distillery == "Miltonduff"')
df.query('Distillery == "GlenSpey" or Distillery == "Glendronach"')
tree = DecisionTreeClassifier(criterion='gini', max_depth=5, random_state=1, min_samples_leaf=5)
X_train = df.iloc[:, 2:-4]
y_train = df['class']
tree.fit(X_train, y_train) | code |
17137542/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/whisky.csv')
df.head() | code |
17137542/cell_23 | [
"text_html_output_1.png"
] | from IPython.display import Image, display_png
from pydotplus import graph_from_dot_data
from sklearn import manifold
from sklearn.cluster import KMeans
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_graphviz
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/whisky.csv')
dist = []
for i in range(2, 20):
km = KMeans(n_clusters=i, n_init=10, max_iter=500, random_state=0)
km.fit(df.iloc[:, 2:-3])
dist.append(km.inertia_)
km = KMeans(n_clusters=5, n_init=10, max_iter=300, random_state=0)
df['class'] = km.fit_predict(df.iloc[:, 2:-3])
df['class'].values
mds = manifold.MDS(n_components=2, dissimilarity='euclidean', random_state=0)
pos = mds.fit_transform(df.iloc[:, 2:-4])
col = ['orange', 'green', 'blue', 'purple', 'red']
chars = '^<>vo+d'
c_flag = 0
labels = df['Distillery']
plt.rcParams['font.size'] = 15
for label, x, y, c in zip(labels, pos[:, 0], pos[:, 1], df['class']):
if c == c_flag:
c_flag = c_flag + 1
plt.annotate(label, xy=(x, y))
df.query('Distillery == "GlenSpey" or Distillery == "Miltonduff"')
df.query('Distillery == "GlenSpey" or Distillery == "Glendronach"')
tree = DecisionTreeClassifier(criterion='gini', max_depth=5, random_state=1, min_samples_leaf=5)
X_train = df.iloc[:, 2:-4]
y_train = df['class']
tree.fit(X_train, y_train)
dot_data = export_graphviz(tree, filled=True, rounded=True, class_names=['Class 1', 'Class 2', 'Class 3', 'Class 4', 'Class 5'], feature_names=df.columns[2:-4].values, out_file=None)
graph = graph_from_dot_data(dot_data)
graph.write_png('tree.png')
display_png(Image('tree.png')) | code |
17137542/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/whisky.csv')
df.info() | code |
17137542/cell_26 | [
"text_html_output_1.png"
] | from IPython.display import Image, display_png
from pydotplus import graph_from_dot_data
from pyproj import Proj, transform
from sklearn import manifold
from sklearn.cluster import KMeans
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_graphviz
import folium
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/whisky.csv')
dist = []
for i in range(2, 20):
km = KMeans(n_clusters=i, n_init=10, max_iter=500, random_state=0)
km.fit(df.iloc[:, 2:-3])
dist.append(km.inertia_)
km = KMeans(n_clusters=5, n_init=10, max_iter=300, random_state=0)
df['class'] = km.fit_predict(df.iloc[:, 2:-3])
df['class'].values
mds = manifold.MDS(n_components=2, dissimilarity='euclidean', random_state=0)
pos = mds.fit_transform(df.iloc[:, 2:-4])
col = ['orange', 'green', 'blue', 'purple', 'red']
chars = '^<>vo+d'
c_flag = 0
labels = df['Distillery']
plt.rcParams['font.size'] = 15
for label, x, y, c in zip(labels, pos[:, 0], pos[:, 1], df['class']):
if c == c_flag:
c_flag = c_flag + 1
plt.annotate(label, xy=(x, y))
df.query('Distillery == "GlenSpey" or Distillery == "Miltonduff"')
df.query('Distillery == "GlenSpey" or Distillery == "Glendronach"')
tree = DecisionTreeClassifier(criterion='gini', max_depth=5, random_state=1, min_samples_leaf=5)
X_train = df.iloc[:, 2:-4]
y_train = df['class']
tree.fit(X_train, y_train)
dot_data = export_graphviz(tree, filled=True, rounded=True, class_names=['Class 1', 'Class 2', 'Class 3', 'Class 4', 'Class 5'], feature_names=df.columns[2:-4].values, out_file=None)
graph = graph_from_dot_data(dot_data)
graph.write_png('tree.png')
map_whisky = folium.Map(location=[57.49952, -2.77639], zoom_start=9)
inProj = Proj(init='epsg:27700')
outProj = Proj(init='epsg:4326')
for label, lon, lat, c in zip(labels, df['Latitude'], df['Longitude'], df['class']):
lat2, lon2 = transform(inProj, outProj, lon, lat)
folium.Marker([lon2, lat2], popup=label, icon=folium.Icon(color=col[c])).add_to(map_whisky)
map_whisky | code |
17137542/cell_2 | [
"text_plain_output_1.png"
] | !pip install pydotplus
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier
from IPython.display import Image, display_png
from pydotplus import graph_from_dot_data
from sklearn.tree import export_graphviz
from sklearn import manifold
import folium
from pyproj import Proj, transform | code |
17137542/cell_18 | [
"image_output_1.png"
] | from sklearn import manifold
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/whisky.csv')
dist = []
for i in range(2, 20):
km = KMeans(n_clusters=i, n_init=10, max_iter=500, random_state=0)
km.fit(df.iloc[:, 2:-3])
dist.append(km.inertia_)
km = KMeans(n_clusters=5, n_init=10, max_iter=300, random_state=0)
df['class'] = km.fit_predict(df.iloc[:, 2:-3])
df['class'].values
mds = manifold.MDS(n_components=2, dissimilarity='euclidean', random_state=0)
pos = mds.fit_transform(df.iloc[:, 2:-4])
col = ['orange', 'green', 'blue', 'purple', 'red']
chars = '^<>vo+d'
c_flag = 0
labels = df['Distillery']
plt.rcParams['font.size'] = 15
for label, x, y, c in zip(labels, pos[:, 0], pos[:, 1], df['class']):
if c == c_flag:
c_flag = c_flag + 1
plt.annotate(label, xy=(x, y))
df.query('Distillery == "GlenSpey" or Distillery == "Miltonduff"')
df.query('Distillery == "GlenSpey" or Distillery == "Glendronach"') | code |
17137542/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/whisky.csv')
df.describe() | code |
17137542/cell_16 | [
"text_html_output_1.png"
] | from sklearn import manifold
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/whisky.csv')
dist = []
for i in range(2, 20):
km = KMeans(n_clusters=i, n_init=10, max_iter=500, random_state=0)
km.fit(df.iloc[:, 2:-3])
dist.append(km.inertia_)
km = KMeans(n_clusters=5, n_init=10, max_iter=300, random_state=0)
df['class'] = km.fit_predict(df.iloc[:, 2:-3])
df['class'].values
mds = manifold.MDS(n_components=2, dissimilarity='euclidean', random_state=0)
pos = mds.fit_transform(df.iloc[:, 2:-4])
col = ['orange', 'green', 'blue', 'purple', 'red']
chars = '^<>vo+d'
c_flag = 0
labels = df['Distillery']
plt.rcParams['font.size'] = 15
for label, x, y, c in zip(labels, pos[:, 0], pos[:, 1], df['class']):
if c == c_flag:
c_flag = c_flag + 1
plt.annotate(label, xy=(x, y))
df.query('Distillery == "GlenSpey" or Distillery == "Miltonduff"') | code |
17137542/cell_14 | [
"text_plain_output_1.png"
] | from sklearn import manifold
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/whisky.csv')
dist = []
for i in range(2, 20):
km = KMeans(n_clusters=i, n_init=10, max_iter=500, random_state=0)
km.fit(df.iloc[:, 2:-3])
dist.append(km.inertia_)
km = KMeans(n_clusters=5, n_init=10, max_iter=300, random_state=0)
df['class'] = km.fit_predict(df.iloc[:, 2:-3])
df['class'].values
mds = manifold.MDS(n_components=2, dissimilarity='euclidean', random_state=0)
pos = mds.fit_transform(df.iloc[:, 2:-4])
col = ['orange', 'green', 'blue', 'purple', 'red']
chars = '^<>vo+d'
c_flag = 0
labels = df['Distillery']
plt.figure(figsize=(20, 20), dpi=50)
plt.rcParams['font.size'] = 15
for label, x, y, c in zip(labels, pos[:, 0], pos[:, 1], df['class']):
if c == c_flag:
c_flag = c_flag + 1
plt.scatter(x, y, c=col[c], marker=chars[c], s=100, label='Class ' + str(c + 1))
else:
plt.scatter(x, y, c=col[c], marker=chars[c], s=100)
plt.annotate(label, xy=(x, y))
plt.legend(loc='upper right')
plt.show() | code |
17137542/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/whisky.csv')
dist = []
for i in range(2, 20):
km = KMeans(n_clusters=i, n_init=10, max_iter=500, random_state=0)
km.fit(df.iloc[:, 2:-3])
dist.append(km.inertia_)
plt.plot(range(2, 20), dist)
plt.show() | code |
17137542/cell_12 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/whisky.csv')
dist = []
for i in range(2, 20):
km = KMeans(n_clusters=i, n_init=10, max_iter=500, random_state=0)
km.fit(df.iloc[:, 2:-3])
dist.append(km.inertia_)
km = KMeans(n_clusters=5, n_init=10, max_iter=300, random_state=0)
df['class'] = km.fit_predict(df.iloc[:, 2:-3])
df['class'].values | code |
17144010/cell_13 | [
"text_plain_output_1.png"
] | import keras as K
import numpy as np
import tensorflow as tf
np.random.seed(1)
tf.set_random_seed(1)
tf.logging.set_verbosity(tf.logging.ERROR)
discriminator = K.Sequential()
depth = 64
dropout = 0.4
input_shape = (28, 28, 1)
discriminator.add(K.layers.Conv2D(depth * 1, 5, strides=2, input_shape=input_shape, padding='same'))
discriminator.add(K.layers.LeakyReLU(alpha=0.2))
discriminator.add(K.layers.Dropout(dropout))
discriminator.add(K.layers.Conv2D(depth * 2, 5, strides=2, padding='same'))
discriminator.add(K.layers.LeakyReLU(alpha=0.2))
discriminator.add(K.layers.Dropout(dropout))
discriminator.add(K.layers.Conv2D(depth * 4, 5, strides=2, padding='same'))
discriminator.add(K.layers.LeakyReLU(alpha=0.2))
discriminator.add(K.layers.Dropout(dropout))
discriminator.add(K.layers.Conv2D(depth * 8, 5, strides=1, padding='same'))
discriminator.add(K.layers.LeakyReLU(alpha=0.2))
discriminator.add(K.layers.Dropout(dropout))
discriminator.add(K.layers.Flatten())
discriminator.add(K.layers.Dense(1, activation='sigmoid'))
discriminator.summary()
tf.logging.set_verbosity(tf.logging.ERROR)
generator = K.Sequential()
depth = 64 + 64 + 64 + 64
dim = 7
dropout = 0.4
generator.add(K.layers.Dense(dim * dim * depth, input_shape=(100,)))
generator.add(K.layers.BatchNormalization(momentum=0.9))
generator.add(K.layers.ReLU())
generator.add(K.layers.Reshape((dim, dim, depth)))
generator.add(K.layers.Dropout(dropout))
generator.add(K.layers.UpSampling2D())
generator.add(K.layers.Conv2DTranspose(int(depth / 2), 5, padding='same'))
generator.add(K.layers.BatchNormalization(momentum=0.9))
generator.add(K.layers.ReLU())
generator.add(K.layers.UpSampling2D())
generator.add(K.layers.Conv2DTranspose(int(depth / 4), 5, padding='same'))
generator.add(K.layers.BatchNormalization(momentum=0.9))
generator.add(K.layers.ReLU())
generator.add(K.layers.Conv2DTranspose(int(depth / 8), 5, padding='same'))
generator.add(K.layers.BatchNormalization(momentum=0.9))
generator.add(K.layers.ReLU())
generator.add(K.layers.Conv2DTranspose(1, 5, padding='same', activation='sigmoid'))
generator.summary() | code |
17144010/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import keras as K
import numpy as np
import tensorflow as tf
np.random.seed(1)
tf.set_random_seed(1)
tf.logging.set_verbosity(tf.logging.ERROR)
discriminator = K.Sequential()
depth = 64
dropout = 0.4
input_shape = (28, 28, 1)
discriminator.add(K.layers.Conv2D(depth * 1, 5, strides=2, input_shape=input_shape, padding='same'))
discriminator.add(K.layers.LeakyReLU(alpha=0.2))
discriminator.add(K.layers.Dropout(dropout))
discriminator.add(K.layers.Conv2D(depth * 2, 5, strides=2, padding='same'))
discriminator.add(K.layers.LeakyReLU(alpha=0.2))
discriminator.add(K.layers.Dropout(dropout))
discriminator.add(K.layers.Conv2D(depth * 4, 5, strides=2, padding='same'))
discriminator.add(K.layers.LeakyReLU(alpha=0.2))
discriminator.add(K.layers.Dropout(dropout))
discriminator.add(K.layers.Conv2D(depth * 8, 5, strides=1, padding='same'))
discriminator.add(K.layers.LeakyReLU(alpha=0.2))
discriminator.add(K.layers.Dropout(dropout))
discriminator.add(K.layers.Flatten())
discriminator.add(K.layers.Dense(1, activation='sigmoid'))
discriminator.summary() | code |
17144010/cell_8 | [
"text_plain_output_1.png"
] | from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import tensorflow as tf
np.random.seed(1)
tf.set_random_seed(1)
from tensorflow.examples.tutorials.mnist import input_data
x_train = input_data.read_data_sets('mnist', one_hot=True).train.images
x_train = x_train.reshape(-1, 28, 28, 1).astype(np.float32) | code |
17144010/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import keras as K
import tensorflow as tf
import pandas as pd
import os
from matplotlib import pyplot as plt
import seaborn as sns
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' | code |
73067082/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from xgboost import XGBRegressor
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
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