path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
89139379/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape | code |
89139379/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.isnull().sum() | code |
74042979/cell_21 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-re... | code |
74042979/cell_9 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-regression-techniques/sa... | code |
74042979/cell_23 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-re... | code |
74042979/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-re... | code |
74042979/cell_6 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-regression-techniques/sa... | code |
74042979/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 |
74042979/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv(... | code |
74042979/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-regression-techniques/sa... | code |
74042979/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-regression-techniques/sa... | code |
74042979/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv(... | code |
74042979/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-regression-techniques/sa... | code |
74042979/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv(... | code |
74042979/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-r... | code |
74042979/cell_22 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-re... | code |
74042979/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-regression-techniques/sa... | code |
74042979/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-regression-techniques/sa... | code |
74042979/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-regression-techniques/sa... | code |
72065751/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt # Visualization
import pandas as pd #Data manipulation
df = pd.read_csv('../input/insurance/insurance.csv')
df
from sklearn import linear_model
lin = linear_model.LinearRegression()
lin.fit(df[['age']], df.charges)
lin.predict([[40]])
lin.fit(df[['... | code |
72065751/cell_13 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt # Visualization
import pandas as pd #Data manipulation
df = pd.read_csv('../input/insurance/insurance.csv')
df
from sklearn import linear_model
lin = linear_model.LinearRegression()
lin.fit(df[['age']], df.charges)
lin.predict([[40]])
lin.fit(df[['... | code |
72065751/cell_9 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt # Visualization
import pandas as pd #Data manipulation
df = pd.read_csv('../input/insurance/insurance.csv')
df
from sklearn import linear_model
lin = linear_model.LinearRegression()
lin.fit(df[['age']], df.charges)
lin.predict([[40]])
plt.scatter(d... | code |
72065751/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd #Data manipulation
df = pd.read_csv('../input/insurance/insurance.csv')
df
df.describe() | code |
72065751/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt # Visualization
import pandas as pd #Data manipulation
df = pd.read_csv('../input/insurance/insurance.csv')
df
from sklearn import linear_model
lin = linear_model.LinearRegression()
lin.fit(df[['age']], df.charges)
lin.predict([[40]])
lin.fit(df[['... | code |
72065751/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd #Data manipulation
df = pd.read_csv('../input/insurance/insurance.csv')
df | code |
72065751/cell_8 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt # Visualization
import pandas as pd #Data manipulation
df = pd.read_csv('../input/insurance/insurance.csv')
df
from sklearn import linear_model
lin = linear_model.LinearRegression()
plt.scatter(df.age, df.charges)
plt.xlabel('Age')
plt.ylabel('Charg... | code |
72065751/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt # Visualization
import pandas as pd #Data manipulation
import seaborn as sns #Visualization
df = pd.read_csv('../input/insurance/insurance.csv')
df
from sklearn import linear_model
lin = linear_model.LinearRegression()
lin.fit(df[['age']], df.charg... | code |
72065751/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt # Visualization
import pandas as pd #Data manipulation
import seaborn as sns #Visualization
df = pd.read_csv('../input/insurance/insurance.csv')
df
from sklearn import linear_model
lin = linear_model.LinearRegression()
lin.fit(df[['age']], df.charg... | code |
72065751/cell_17 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt # Visualization
import pandas as pd #Data manipulation
import seaborn as sns #Visualization
df = pd.read_csv('../input/insurance/insurance.csv')
df
from sklearn import linear_model
lin = linear_model.LinearRegression()
lin.fit(df[['age']], df.charg... | code |
72065751/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt # Visualization
import pandas as pd #Data manipulation
import seaborn as sns #Visualization
df = pd.read_csv('../input/insurance/insurance.csv')
df
from sklearn import linear_model
lin = linear_model.LinearRegression()
lin.fit(df[['age']], df.charg... | code |
72065751/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt # Visualization
import pandas as pd #Data manipulation
df = pd.read_csv('../input/insurance/insurance.csv')
df
from sklearn import linear_model
lin = linear_model.LinearRegression()
lin.fit(df[['age']], df.charges)
lin.predict([[40]])
lin.fit(df[['... | code |
72065751/cell_10 | [
"text_html_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt # Visualization
import pandas as pd #Data manipulation
df = pd.read_csv('../input/insurance/insurance.csv')
df
from sklearn import linear_model
lin = linear_model.LinearRegression()
lin.fit(df[['age']], df.charges)
lin.predict([[40]])
lin.fit(df[['... | code |
72065751/cell_12 | [
"text_html_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt # Visualization
import pandas as pd #Data manipulation
df = pd.read_csv('../input/insurance/insurance.csv')
df
from sklearn import linear_model
lin = linear_model.LinearRegression()
lin.fit(df[['age']], df.charges)
lin.predict([[40]])
lin.fit(df[['... | code |
72065751/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd #Data manipulation
df = pd.read_csv('../input/insurance/insurance.csv')
df
df.info() | code |
73100919/cell_16 | [
"text_html_output_1.png"
] | from lightgbm import LGBMRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder
from tqdm import tqdm
import numpy as np
import pandas as pd
X = pd.read_csv('../input/30-days-of-ml/train.csv', encoding='utf-8', index_col=0... | code |
73100919/cell_3 | [
"text_html_output_1.png"
] | import warnings
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from lightgbm import LGBMRegressor
from sklearn.model_selection import KFold
from tqdm import tqdm
from sklearn.metrics import mean_squared_error
import warnings
warnings.filterwarnings('ignore') | code |
73100919/cell_5 | [
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_11.png",
"text_plain_output_20.png",
"text_plain_output_4.png",
"text_plain_output_14.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"text_plain_output_1... | import pandas as pd
X = pd.read_csv('../input/30-days-of-ml/train.csv', encoding='utf-8', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', encoding='utf-8', index_col=0)
y = X['target']
X = X.drop(['target'], axis=1)
X.head() | code |
2014551/cell_13 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
from datetime import date
pd.set_option('display.float_f... | code |
2014551/cell_4 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
from datetime import date
pd.set_option('display.float_format', lambda x: '%.5f' % x)
from subprocess im... | code |
2014551/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
from datetime import date
pd.set_option('display.float_format', lambda x: '%.5f' % x)
from subprocess im... | code |
2014551/cell_2 | [
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
from datetime import date
pd.set_option('display.float_format', lambda x: '%.5f' % x)
from subprocess im... | code |
2014551/cell_11 | [
"text_html_output_2.png",
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_html_output_3.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
from datetime import date
pd.set_option('display.float_format', lambda x: '%.5f' % x)
from subprocess im... | code |
2014551/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
from datetime import date
pd.set_option('display.float_format', lambda x: '%.5f' % x)
from subprocess im... | code |
2014551/cell_15 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
from datetime import date
pd.set_option('display.float_f... | code |
2014551/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import MinMaxScaler
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
f... | code |
2014551/cell_14 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
from datetime import date
pd.set_option('display.float_format', lambda x: '%.5f' % x)
from subprocess im... | code |
2014551/cell_22 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.metrics import make_scorer
from sklearn.grid_search import GridSearchCV
from sklearn.cross_validation import ShuffleSplit
from sklearn import tree
from sklearn.ensemble import AdaBoostRegressor
from sklearn.linear_model import BayesianRidge, LinearRegression
from time import time | code |
2014551/cell_10 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
from datetime import date
pd.set_option('display.float_format', lambda x: '%.5f' % x)
from subprocess im... | code |
2014551/cell_12 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_1.png",
"text_html_output_3.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
from datetime import date
pd.set_option('display.float_format', lambda x: '%.5f' % x)
from subprocess im... | code |
2014551/cell_5 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
from datetime import date
pd.set_option('display.float_format', lambda x: '%.5f' % x)
from subprocess im... | code |
16148624/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['figure.figsize'] = (16, 10)
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv(... | code |
16148624/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True, copy=False)
df_train.nunique().min()
num_features = df_train.select_dtypes(['float64', 'int64']).columns... | code |
16148624/cell_25 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['figure.figsize'] = (16, 10)
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv(... | code |
16148624/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True, copy=False)
df_train.info() | code |
16148624/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True, copy=False)
df_train.nunique().min() | code |
16148624/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True, copy=False)
df_train.nunique().min()
num_features = df_train.select_dtypes(['float64', 'int64']).columns... | code |
16148624/cell_19 | [
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True, copy=False)
df_train.nunique().min()
num_features = df_train.select_dtypes(['float64', 'int64']).columns... | code |
16148624/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['figure.figsize'] = (16, 10)
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv(... | code |
16148624/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['figure.figsize'] = (16, 10)
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv(... | code |
16148624/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True, copy=False)
df_train.nunique().min()
num_features = df_train.select_dtypes(['float64', 'int64']).columns... | code |
16148624/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True, copy=False)
df_train.nunique().min()
num_features = df_train.select_dtypes(['float64', 'int64']).columns... | code |
16148624/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['figure.figsize'] = (16, 10)
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv(... | code |
16148624/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['figure.figsize'] = (16, 10)
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv(... | code |
16148624/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True, copy=False)
df_train.nunique().min()
num_features = df_train.select_dtypes(['float64', 'int64']).columns... | code |
34130785/cell_13 | [
"text_html_output_1.png"
] | from keras.preprocessing import sequence
from keras.preprocessing.text import Tokenizer
import pandas as pd
datasets_dir = ''
vnrows = None
datasets_dir = '../input/'
df = pd.read_csv(datasets_dir + 'fakenews_preprocessed_35k.csv', nrows=vnrows, encoding='utf-8')
df = df.loc[:, ~df.columns.str.contains('^Unnamed')]... | code |
34130785/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
datasets_dir = ''
vnrows = None
datasets_dir = '../input/'
df = pd.read_csv(datasets_dir + 'fakenews_preprocessed_35k.csv', nrows=vnrows, encoding='utf-8')
df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
print(df.groupby(['label'])['label'].count())
df = df.sample(frac=1).reset_index(drop=Tr... | code |
34130785/cell_20 | [
"text_plain_output_1.png"
] | from gensim.models import Word2Vec
from keras import optimizers
from keras import regularizers
from keras.callbacks import EarlyStopping
from keras.layers import Dense, Activation, Dropout, Flatten,Input
from keras.layers import Embedding, Conv1D, MaxPooling1D, Dense
from keras.models import Sequential
from kera... | code |
34130785/cell_11 | [
"text_plain_output_1.png"
] | from keras.preprocessing.text import Tokenizer
import pandas as pd
datasets_dir = ''
vnrows = None
datasets_dir = '../input/'
df = pd.read_csv(datasets_dir + 'fakenews_preprocessed_35k.csv', nrows=vnrows, encoding='utf-8')
df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
df = df.sample(frac=1).reset_index(drop=... | code |
34130785/cell_18 | [
"text_plain_output_1.png"
] | from gensim.models import Word2Vec
from keras.preprocessing.text import Tokenizer
import numpy as np
import pandas as pd
datasets_dir = ''
vnrows = None
datasets_dir = '../input/'
df = pd.read_csv(datasets_dir + 'fakenews_preprocessed_35k.csv', nrows=vnrows, encoding='utf-8')
df = df.loc[:, ~df.columns.str.contain... | code |
34130785/cell_8 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | print(len(X_train))
print(len(X_test))
print(len(y_train))
print(len(y_test)) | code |
34130785/cell_15 | [
"text_plain_output_1.png"
] | from gensim.models import Word2Vec
max_seq_len = 458
gensim_news_desc = []
chunk_data = X_train
for record in range(0, len(chunk_data)):
news_desc_list = []
for tok in chunk_data[record].split():
news_desc_list.append(str(tok))
gensim_news_desc.append(news_desc_list)
vsize = max_seq_len
vmin_coun... | code |
34130785/cell_17 | [
"text_plain_output_1.png"
] | from gensim.models import Word2Vec
max_seq_len = 458
gensim_news_desc = []
chunk_data = X_train
for record in range(0, len(chunk_data)):
news_desc_list = []
for tok in chunk_data[record].split():
news_desc_list.append(str(tok))
gensim_news_desc.append(news_desc_list)
vsize = max_seq_len
vmin_coun... | code |
34130785/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
datasets_dir = ''
vnrows = None
datasets_dir = '../input/'
df = pd.read_csv(datasets_dir + 'fakenews_preprocessed_35k.csv', nrows=vnrows, encoding='utf-8')
df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
df = df.sample(frac=1).reset_index(drop=True)
df[['label', 'target_text']].head(5) | code |
89135227/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import warnings
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
warnings.simplefilter(action='ignore')
df = pd.read_csv('../input/sepsis-dataset/Dataset.csv')
df.head() | code |
89122519/cell_9 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | def max_digit(n, d, r):
max_number = -1
n = abs(n)
while n > 0:
digit = n % 10
if digit % d == r:
if digit > max_number:
max_number = digit
n //= 10
return max_number
def main():
n = int(input())
d = int(input())
r = int(input())
main()
de... | code |
89122519/cell_11 | [
"text_plain_output_1.png"
] | def max_digit(n, d, r):
max_number = -1
n = abs(n)
while n > 0:
digit = n % 10
if digit % d == r:
if digit > max_number:
max_number = digit
n //= 10
return max_number
def main():
n = int(input())
d = int(input())
r = int(input())
main()
de... | code |
89122519/cell_1 | [
"text_plain_output_1.png"
] | def max_digit(n, d, r):
max_number = -1
n = abs(n)
while n > 0:
digit = n % 10
if digit % d == r:
if digit > max_number:
max_number = digit
n //= 10
return max_number
def main():
n = int(input())
d = int(input())
r = int(input())
print(... | code |
89122519/cell_7 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | def max_digit(n, d, r):
max_number = -1
n = abs(n)
while n > 0:
digit = n % 10
if digit % d == r:
if digit > max_number:
max_number = digit
n //= 10
return max_number
def main():
n = int(input())
d = int(input())
r = int(input())
main()
de... | code |
89122519/cell_3 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | def max_digit(n, d, r):
max_number = -1
n = abs(n)
while n > 0:
digit = n % 10
if digit % d == r:
if digit > max_number:
max_number = digit
n //= 10
return max_number
def main():
n = int(input())
d = int(input())
r = int(input())
main()
de... | code |
89122519/cell_5 | [
"text_plain_output_1.png"
] | def max_digit(n, d, r):
max_number = -1
n = abs(n)
while n > 0:
digit = n % 10
if digit % d == r:
if digit > max_number:
max_number = digit
n //= 10
return max_number
def main():
n = int(input())
d = int(input())
r = int(input())
main()
de... | code |
72092307/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import OrdinalEncoder
from xgboost import XGBRegressor
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/3... | code |
72092307/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
sub = pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv')
sub.head() | code |
72092307/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.shape
train.head() | code |
72092307/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_error
from xgboost import XGBRegressor
import numpy as np # linear algebra
from sklearn.metrics import mean_squared_error
from xgboost import XGBRegressor
model = XGBRegressor()
model.fit(X_train, y_train)
preds = model.predict(... | code |
72092307/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 |
72092307/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.shape
train.describe() | code |
72092307/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.shape
train.info | code |
72092307/cell_16 | [
"text_html_output_1.png"
] | print(X_train) | code |
72092307/cell_17 | [
"text_plain_output_1.png"
] | print(y_train) | code |
72092307/cell_14 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.shape
train.info
y = train['target']
features = train... | code |
72092307/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import OrdinalEncoder
from xgboost import XGBRegressor
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/3... | code |
72092307/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.shape
train.info
y = train['target']
features = train.drop(['target'], axis=1)
print(y) | code |
72092307/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.shape | code |
73069645/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews... | code |
73069645/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('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews_per_month.isnull().sum... | code |
73069645/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews... | code |
73069645/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month... | code |
73069645/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews_per_month.isnull().sum... | code |
73069645/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape | code |
73069645/cell_2 | [
"text_plain_output_1.png"
] | 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))
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import seaborn as sns | code |
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