path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
89129165/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
pd.set_option('display.max_colwidth', None)
import warnings
warnings.filterwarnings('ignore')
custom_c... | code |
89129165/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
pd.set_option('display.max_colwidth', None)
import warnings
warnings.filterwarnings('ignore')
custom_c... | code |
89129165/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
pd.set_option('display.max_colwidth', None)
import warnings
warnings.filterwarnings('ignore')
custom_c... | code |
89129165/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
pd.set_option('display.max_colwidth', None)
import warnings
warnings.filterwarnings('ignore')
custom_c... | code |
89129165/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
pd.set_option('display.max_colwidth', None)
import warnings
warnings.filterwarnings('ignore')
custom_c... | code |
89129165/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
pd.set_option('display.max_colwidth', None)
import warnings
warnings.filterwarnings('ignore')
custom_c... | code |
89129165/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
pd.set_option('display.max_colwidth', None)
import warnings
warnings.filterwarnings('ignore')
custom_c... | code |
89129165/cell_31 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud,ImageColorGenerator,STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
pd.set... | code |
89129165/cell_14 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
pd.set_option('display.max_colwidth', None)
import warnings
warnings.filterwarnings('ignore')
custom_c... | code |
89129165/cell_10 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
pd.set_option('display.max_colwidth', None)
import warnings
warnings.filterwarnings('ignore')
custom_c... | code |
89129165/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
pd.set_option('display.max_colwidth', None)
import warnings
warnings.filterwarnings('ignore')
custom_c... | code |
89129165/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
pd.set_option('display.max_colwidth', None)
import warnings
warnings.filterwarnings('ignore')
custom_c... | code |
89129165/cell_36 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud,ImageColorGenerator,STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS
pd.set... | code |
17134171/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import plotly
import os
import numpy as np
import pandas as pd
import re
from datetime import datetime
import seaborn as sns
import matplotlib.pyplot as plt
import plotly
import plotly.plotly as py
import plotly.graph_objs as go
import colorlover as cl
plotly.offline.init_notebook_mode()
from sklearn.model_selection i... | code |
17134171/cell_8 | [
"text_html_output_1.png"
] | from datetime import datetime
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
import numpy as np
import os
import pandas as pd
import plotly
import plotly.graph_objs as go
import re
import os
import numpy as np
import pandas as pd
import re
from datetime im... | code |
17134171/cell_14 | [
"text_plain_output_1.png"
] | from datetime import datetime
from keras.layers import Input, Embedding, Dense, Bidirectional, CuDNNGRU, GlobalMaxPooling1D
from keras.models import Model
from keras.optimizers import Adamax
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from sklearn.decompos... | code |
17134171/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datetime import datetime
from keras.layers import Input, Embedding, Dense, Bidirectional, CuDNNGRU, GlobalMaxPooling1D
from keras.models import Model
from keras.optimizers import Adamax
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from sklearn.metrics ... | code |
17134171/cell_12 | [
"text_html_output_1.png"
] | from datetime import datetime
from keras.layers import Input, Embedding, Dense, Bidirectional, CuDNNGRU, GlobalMaxPooling1D
from keras.models import Model
from keras.optimizers import Adamax
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from sklearn.metrics ... | code |
88102651/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/income/train.csv')
data.shape
all_columns = list(data.columns)
all_columns
data.isna().sum()
cat_columns = ['workclass', 'education', 'marital-status', 'occupatio... | code |
88102651/cell_13 | [
"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/income/train.csv')
data.shape
all_columns = list(data.columns)
all_columns
data.isna().sum()
def event_rate_analysis(column, target):
temp = (data.groupby(column)[target].sum() / data[target].sum()).to_frame... | code |
88102651/cell_9 | [
"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/income/train.csv')
data.shape
all_columns = list(data.columns)
all_columns
data.isna().sum()
cat_columns = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'gender', 'native-cou... | code |
88102651/cell_4 | [
"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/income/train.csv')
data.shape
all_columns = list(data.columns)
all_columns
data.info() | code |
88102651/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/income/train.csv')
data.shape
all_columns = list(data.columns)
all_columns
data.isna().sum()
cat_columns = ['workclass', 'education', 'marital-status', 'occupatio... | code |
88102651/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/income/train.csv')
data.shape
all_columns = list(data.columns)
all_columns
data.isna().sum()
cat_columns = ['workclass', 'education', 'marital-status', 'occupatio... | code |
88102651/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix,recall_score, accuracy_score , classification_report, balanced_accuracy_score
from sklearn.tree import DecisionTreeClassifier
y_train.value_counts(normalize=True)
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier(max_depth=20, criterion='entropy... | code |
88102651/cell_33 | [
"text_html_output_1.png"
] | data1 = X_train[X_train['Husband'] == 1]
data2 = X_train[X_train['Married-civ-spouse'] == 1]
data3 = X_train.loc[(X_train['Sales'] == 1) | (X_train['Prof-specialty'] == 1) | (X_train['Exec-managerial'] == 1)]
data4 = X_train.loc[~((X_train['Husband'] == 1) | (X_train['Married-civ-spouse'] == 1) | ((X_train['Sales'] == ... | code |
88102651/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/income/train.csv')
data.shape
all_columns = list(data.columns)
all_columns
data.isna().sum()
cat_columns = ['workclass', 'education', 'marital-status', 'occupatio... | code |
88102651/cell_26 | [
"text_plain_output_1.png"
] | y_train.value_counts(normalize=True) | code |
88102651/cell_2 | [
"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/income/train.csv')
data.shape | code |
88102651/cell_11 | [
"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/income/train.csv')
data.shape
all_columns = list(data.columns)
all_columns
data.isna().sum()
def event_rate_analysis(column, target):
temp = (data.groupby(column)[target].sum() / data[target].sum()).to_frame... | code |
88102651/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/income/train.csv')
data.shape
all_columns = list(data.columns)
all_columns
data.isna().sum()
def event_rate_analysis(column, target):
temp = (data.groupby(col... | code |
88102651/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 |
88102651/cell_18 | [
"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/income/train.csv')
data.shape
all_columns = list(data.columns)
all_columns
data.isna().sum()
def event_rate_analysis(column, target):
temp = (data.groupby(column)[target].sum() / data[target].sum()).to_frame... | code |
88102651/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
y_train.value_counts(normalize=True)
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier(max_depth=20, criterion='entropy', random_state=42)
clf.fit(X_train, y_train) | code |
88102651/cell_8 | [
"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/income/train.csv')
data.shape
all_columns = list(data.columns)
all_columns
data.isna().sum() | code |
88102651/cell_15 | [
"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/income/train.csv')
data.shape
all_columns = list(data.columns)
all_columns
data.isna().sum()
def event_rate_analysis(column, target):
temp = (data.groupby(column)[target].sum() / data[target].sum()).to_frame... | code |
88102651/cell_16 | [
"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/income/train.csv')
data.shape
all_columns = list(data.columns)
all_columns
data.isna().sum()
def event_rate_analysis(column, target):
temp = (data.groupby(column)[target].sum() / data[target].sum()).to_frame... | code |
88102651/cell_3 | [
"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/income/train.csv')
data.shape
all_columns = list(data.columns)
all_columns | code |
88102651/cell_17 | [
"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/income/train.csv')
data.shape
all_columns = list(data.columns)
all_columns
data.isna().sum()
def event_rate_analysis(column, target):
temp = (data.groupby(column)[target].sum() / data[target].sum()).to_frame... | code |
88102651/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import DecisionTreeClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/income/train.csv')
data.shape
all_columns = list(data.columns)
all_columns
data.isna().sum()
cat_columns = ['work... | code |
88102651/cell_14 | [
"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/income/train.csv')
data.shape
all_columns = list(data.columns)
all_columns
data.isna().sum()
def event_rate_analysis(column, target):
temp = (data.groupby(column)[target].sum() / data[target].sum()).to_frame... | code |
88102651/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/income/train.csv')
data.shape
all_columns = list(data.columns)
all_columns
data.isna().sum()
cat_columns = ['workclass', 'education', 'marital-status', 'occupatio... | code |
88102651/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix,recall_score, accuracy_score , classification_report, balanced_accuracy_score
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import DecisionTreeClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggl... | code |
88102651/cell_12 | [
"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/income/train.csv')
data.shape
all_columns = list(data.columns)
all_columns
data.isna().sum()
def event_rate_analysis(column, target):
temp = (data.groupby(column)[target].sum() / data[target].sum()).to_frame... | code |
88102651/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/income/train.csv')
data.shape
all_columns = list(data.columns)
all_columns
print('Unique Occupation :', data['occupation'].nunique())
print(data['occupation'].unique())
print('Unique workclass :', data['workclass... | code |
33116081/cell_42 | [
"text_html_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from collections import Counter
import seaborn as sns
plt.style.use('seaborn-colorblind')
plt.style.use('sea... | code |
33116081/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_passengerId = test_df['PassengerId']
train_df[['Parch', 'Survived']].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
33116081/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_passengerId = test_df['PassengerId']
category2 = ['Cabin', 'Name', 'Ticket']
for c in category2:
print('{} \n'.format(train_df[c].value_counts())) | code |
33116081/cell_34 | [
"text_plain_output_1.png"
] | from collections import Counter
import numpy as np
import pandas as pd
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_passengerId = test_df['PassengerId']
def detectOutliers(df, features):
outlier_indices = []
for i in features:
... | code |
33116081/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_passengerId = test_df['PassengerId']
train_df[['Fare', 'Survived']].groupby(['Fare'], as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
33116081/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_passengerId = test_df['PassengerId']
train_df[['SibSp', 'Survived']].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
33116081/cell_29 | [
"text_html_output_1.png"
] | from collections import Counter
import numpy as np
import pandas as pd
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_passengerId = test_df['PassengerId']
def detectOutliers(df, features):
outlier_indices = []
for i in features:
... | code |
33116081/cell_39 | [
"text_html_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from collections import Counter
import seaborn as sns
plt.style.use('seaborn-colorblind')
plt.style.use('seaborn-whitegrid')
import... | code |
33116081/cell_48 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from collections import Counter
import seaborn as sns
plt.style.use('seaborn-colorblind')
plt.style.use('sea... | code |
33116081/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from collections import Counter
import seaborn as sns
plt.style.use('seaborn-colorblind')
plt.style.use('seaborn-whitegrid')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filenam... | code |
33116081/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_passengerId = test_df['PassengerId']
train_df[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
33116081/cell_7 | [
"image_output_1.png"
] | import pandas as pd
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_passengerId = test_df['PassengerId']
train_df.info() | code |
33116081/cell_45 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from collections import Counter
import seaborn as sns
plt.style.use('seaborn-colorblind')
plt.style.use('sea... | code |
33116081/cell_18 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"image_output_4.png",
"image_output_6.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_passengerId = test_df['PassengerId']
train_df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
33116081/cell_32 | [
"text_plain_output_1.png"
] | from collections import Counter
import numpy as np
import pandas as pd
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_passengerId = test_df['PassengerId']
def detectOutliers(df, features):
outlier_indices = []
for i in features:
... | code |
33116081/cell_51 | [
"image_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from collections import Counter
import seaborn as sns
plt.style.use('seaborn-colorblind')
plt.style.use('sea... | code |
33116081/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from collections import Counter
import seaborn as sns
plt.style.use('seaborn-colorblind')
plt.style.use('seaborn-whitegrid')
import os
plt.style.available
test_df = pd.read_csv('/kag... | code |
33116081/cell_3 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from collections import Counter
import seaborn as sns
plt.style.use('seaborn-colorblind')
plt.style.use('seaborn-whitegrid')
import os
plt.style.available | code |
33116081/cell_35 | [
"text_html_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from collections import Counter
import seaborn as sns
plt.style.use('seaborn-colorblind')
plt.style.use('seaborn-whitegrid')
import... | code |
33116081/cell_31 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from collections import Counter
import numpy as np
import pandas as pd
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_passengerId = test_df['PassengerId']
def detectOutliers(df, features):
outlier_indices = []
for i in features:
... | code |
33116081/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_passengerId = test_df['PassengerId']
train_df | code |
33116081/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_passengerId = test_df['PassengerId']
train_df[['Parch', 'SibSp', 'Survived']].groupby(['Parch', 'SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
33116081/cell_27 | [
"text_html_output_1.png"
] | from collections import Counter
import numpy as np
import pandas as pd
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_passengerId = test_df['PassengerId']
def detectOutliers(df, features):
outlier_indices = []
for i in features:
... | code |
33116081/cell_37 | [
"image_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from collections import Counter
import seaborn as sns
plt.style.use('seaborn-colorblind')
plt.style.use('seaborn-whitegrid')
import... | code |
33116081/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from collections import Counter
import seaborn as sns
plt.style.use('seaborn-colorblind')
plt.style.use('seaborn-whitegrid')
import os
plt.style.available
test_df = pd.read_csv('/kag... | code |
88096014/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/dataset-for-practicing-classification/exercises-logistic-regression-exercise-1/nba_logreg.csv')
data.describe().T
data.columns.to_list()
cols_pred = []
col_target = 'TARGET_5Yrs'
for col in data.columns.to_list():
if col not in ['Name', 'TARGET_5Yrs']:
... | code |
88096014/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/dataset-for-practicing-classification/exercises-logistic-regression-exercise-1/nba_logreg.csv')
data.describe().T | code |
88096014/cell_2 | [
"text_plain_output_1.png"
] | !pip install skorecard optbinning | code |
88096014/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/dataset-for-practicing-classification/exercises-logistic-regression-exercise-1/nba_logreg.csv')
data.describe().T
data.columns.to_list()
cols_pred = []
col_target = 'TARGET_5Yrs'
for col in data.columns.to_... | code |
88096014/cell_7 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/dataset-for-practicing-classification/exercises-logistic-regression-exercise-1/nba_logreg.csv')
data.describe().T
data.columns.to_list() | code |
88096014/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/dataset-for-practicing-classification/exercises-logistic-regression-exercise-1/nba_logreg.csv')
data.describe().T
data.columns.to_list()
cols_pred = []
col_target = 'TARGET_5Yrs'
for col in data.columns.to_list():
if col not in ['Name', 'TARGET_5Yrs']:
... | code |
88096014/cell_16 | [
"text_html_output_1.png"
] | from sklearn.pipeline import make_pipeline
from skorecard.bucketers import DecisionTreeBucketer, OptimalBucketer
from skorecard.pipeline import BucketingProcess
from skorecard.preprocessing import WoeEncoder
import pandas as pd
data = pd.read_csv('/kaggle/input/dataset-for-practicing-classification/exercises-logis... | code |
88096014/cell_17 | [
"text_plain_output_1.png"
] | data_woe | code |
88096014/cell_10 | [
"text_plain_output_1.png"
] | import missingno as msno
msno.matrix(data[cols_pred + [col_target]]) | code |
88096014/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/dataset-for-practicing-classification/exercises-logistic-regression-exercise-1/nba_logreg.csv')
data.head() | code |
128026152/cell_21 | [
"text_plain_output_1.png"
] | name | code |
128026152/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import ipywidgets as widgets
widgets.FloatSlider(value=7.5, min=0, max=10.0, step=0.1, description='Test:', disabled=False, continuous_update=False, orientation='vertical', readout=True, readout_format='.1f')
widgets.IntRangeSlider(value=[5, 7], min=0, max=10, step=1, description='Test:', disabled=False, continuous_u... | code |
128026152/cell_17 | [
"text_plain_output_1.png"
] | import ipywidgets as widgets
widgets.FloatSlider(value=7.5, min=0, max=10.0, step=0.1, description='Test:', disabled=False, continuous_update=False, orientation='vertical', readout=True, readout_format='.1f')
widgets.IntRangeSlider(value=[5, 7], min=0, max=10, step=1, description='Test:', disabled=False, continuous_u... | code |
128026152/cell_10 | [
"text_plain_output_1.png"
] | import ipywidgets as widgets
widgets.FloatSlider(value=7.5, min=0, max=10.0, step=0.1, description='Test:', disabled=False, continuous_update=False, orientation='vertical', readout=True, readout_format='.1f')
widgets.IntRangeSlider(value=[5, 7], min=0, max=10, step=1, description='Test:', disabled=False, continuous_u... | code |
17130389/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
quartet = pd.read_csv('../input/quartet.csv', index_col='id')
print(quartet) | code |
17130389/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
quartet = pd.read_csv('../input/quartet.csv', index_col='id')
quartet.describe() | code |
17130389/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
quartet = pd.read_csv('../input/quartet.csv', index_col='id')
quartet.groupby('dataset').agg(['mean', 'std']) | code |
325211/cell_4 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
act_df = pd.read_csv('../input/act_train.csv', sep=',')
sns.countplot(x='activity_category', data=act_df, hue='outcome')
sns.plt.show() | code |
325211/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
act_df = pd.read_csv('../input/act_train.csv', sep=',')
fig, ax = plt.subplots()
fig.set_size_inches(30, 20)
h = sns.countplot(x='char_1',data=act_df,hue='outcome',ax=ax)
h.set_xticklabels(h.get_xticklabels(),rotation=50)
sns.plt.show()
fig,... | code |
325211/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
act_df = pd.read_csv('../input/act_train.csv', sep=',')
fig, ax = plt.subplots()
fig.set_size_inches(30, 20)
h = sns.countplot(x='char_1',data=act_df,hue='outcome',ax=ax)
h.set_xticklabels(h.get_xticklabels(),rotation=50)
... | code |
325211/cell_3 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
act_df = pd.read_csv('../input/act_train.csv', sep=',')
sns.countplot(x='outcome', data=act_df)
sns.plt.show() | code |
325211/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
act_df = pd.read_csv('../input/act_train.csv', sep=',')
fig, ax = plt.subplots()
fig.set_size_inches(30, 20)
h = sns.countplot(x='char_1', data=act_df, hue='outcome', ax=ax)
h.set_xticklabels(h.get_xticklabels(), rotation=50)
sns.plt.show() | code |
128020060/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
players.isnull().sum... | code |
128020060/cell_6 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
players.isnull().sum... | code |
128020060/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
seasons_stats[season... | code |
128020060/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 |
128020060/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)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
players.isnull().sum... | code |
128020060/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
players.isnull().sum... | code |
128020060/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
len(players) | code |
128020060/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
seasons_stats[season... | code |
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