path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
74067279/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
import re
import pandas as pd
import numpy as np
import re
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
from sklearn.model_selection import cross_va... | code |
74067279/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
import re
import pandas as pd
import numpy as np
import re
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
from sklearn.model_selection import cross_va... | code |
74067279/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import re
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
from sklearn.model_selection import cross_val_score
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_c... | code |
74067279/cell_7 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
import re
import pandas as pd
import numpy as np
import re
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestC... | code |
74067279/cell_8 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
import re
import pandas as pd
import numpy as np
import re
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestC... | code |
74067279/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import re
import pandas as pd
import numpy as np
import re
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
from sklearn.model_selection import cross_val_score
train = pd.r... | code |
74067279/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
import re
import pandas as pd
import numpy as np
import re
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
from sklearn.model_selection import cross_va... | code |
89128640/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import cudf as pd
import cupy as np
from sklearn.model_selection import cross_val_score
import numpy | code |
73071065/cell_9 | [
"text_html_output_1.png"
] | from scipy.stats.mstats import winsorize
from sklearn.linear_model import TweedieRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler, KBinsDiscretizer
import n... | code |
73071065/cell_11 | [
"text_plain_output_1.png"
] | from scipy.stats.mstats import winsorize
from sklearn.linear_model import TweedieRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler, KBinsDiscretizer
import n... | code |
128011630/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
pd.options.display.min_rows = 500
df_train.isnull().sum()
pd.options.display.min_rows = 500
df_train.isna().sum()
df_train.drop(['LotFrontage', 'Alley', 'PoolQC', 'Fence'... | code |
128011630/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
pd.options.display.min_rows = 500
df_train.isnull().sum()
pd.options.display.min_rows = 500
df_train.isna().sum()
df_train.drop(['LotFrontage', 'Alley', 'PoolQC', 'Fence'... | code |
128011630/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
pd.options.display.min_rows = 500
df_train.isnull().sum()
pd.options.display.min_rows = 500
df_train.isna().sum()
df_train.drop(['LotFrontage', 'Alley', 'PoolQC', 'Fence'... | code |
128011630/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
pd.options.display.min_rows = 500
df_train.isnull().sum()
pd.options.display.min_rows = 500
df_train.isna().sum()
df_train.drop(['LotFrontage', 'Alley', 'PoolQC', 'Fence'... | code |
128011630/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
pd.options.display.min_rows = 500
df_train.isnull().sum()
pd.options.display.min_rows = 500
df_train.isna().sum()
df_train.drop(['LotFrontage', 'Alley', 'PoolQC', 'Fence'... | code |
128011630/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
df_train.describe(include='all') | code |
128011630/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
pd.options.display.min_rows = 500
df_train.isnull().sum()
pd.options.display.min_rows = 500
df_train.isna().sum() | code |
128011630/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
pd.options.display.min_rows = 500
df_train.isnull().sum()
pd.options.display.min_rows = 500
df_train.isna().sum()
df_train.drop(['LotFrontage', 'Alley', 'PoolQC', 'Fence'... | code |
128011630/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.decomposition import PCA | code |
128011630/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
pd.options.display.min_rows = 500
df_train.isnull().sum()
pd.options.display.min_rows = 500
df_train.isna().sum()
df_train.drop(['LotFrontage', 'Alley', 'PoolQC', 'Fence'... | code |
128011630/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
for i in df_train.columns:
print(i) | code |
128011630/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
pd.options.display.min_rows = 500
df_train.isnull().sum()
pd.options.display.min_rows = 500
df_train.isna().sum()
df_train.drop(['LotFrontage', 'Alley', 'PoolQC', 'Fence'... | code |
128011630/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
pd.options.display.min_rows = 500
df_train.isnull().sum()
pd.options.display.min_rows = 500
df_train.isna().sum()
df_train.drop(['LotFrontage', 'Alley', 'PoolQC', 'Fence'... | code |
128011630/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
pd.options.display.min_rows = 500
df_train.isnull().sum() | code |
128011630/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/d/salauddintapu/house-prices-advanced-regression-techniques/train.csv')
df_train.shape | code |
1003108/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from collections import Counter
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import numpy as np
import operator
import pandas as pd
import seaborn as sns
def clean_ts(df):
return df[(df['author_timestamp'] > 1104600000) & (df['author_timestamp'] < 1487807212)]
df = clean_ts(pd.read_csv('..... | code |
1003108/cell_13 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
def clean_ts(df):
return df[(df['author_timestamp'] > 1104600000) & (df['author_timestamp'] < 1487807212)]
df = clean_ts(pd.read_csv('../input/linux_kernel_git_revlog.csv'))
df['author_dt'] = pd.to_datetime(df['author_timestamp'], unit='s')
time_df = ... | code |
1003108/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
def clean_ts(df):
return df[(df['author_timestamp'] > 1104600000) & (df['author_timestamp'] < 1487807212)]
df = clean_ts(pd.read_csv('../input/linux_kernel_git_revlog.csv'))
df['author_dt'] = pd.to_datetime(df['author_timestamp'], unit='s')
time_df = df.groupby(['author_tim... | code |
1003108/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
def clean_ts(df):
return df[(df['author_timestamp'] > 1104600000) & (df['author_timestamp'] < 1487807212)]
df = clean_ts(pd.read_csv('../input/linux_kernel_git_revlog.csv'))
df['author_dt'] = pd.to_datetime(df['author_timestamp'], unit='s')
time_df = df.groupby(['author_tim... | code |
1003108/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1003108/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
def clean_ts(df):
return df[(df['author_timestamp'] > 1104600000) & (df['author_timestamp'] < 1487807212)]
df = clean_ts(pd.read_csv('../input/linux_kernel_git_revlog.csv'))
df['author_dt'] = pd.to_datetime(df['author_timestamp'], unit='s')
time_df = df.groupby(['author_tim... | code |
1003108/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from collections import Counter
import numpy as np
import operator
import pandas as pd
import seaborn as sns
def clean_ts(df):
return df[(df['author_timestamp'] > 1104600000) & (df['author_timestamp'] < 1487807212)]
df = clean_ts(pd.read_csv('../input/linux_kernel_git_revlog.csv'))
df['author_dt'] = pd.to_date... | code |
1003108/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
def clean_ts(df):
return df[(df['author_timestamp'] > 1104600000) & (df['author_timestamp'] < 1487807212)]
df = clean_ts(pd.read_csv('../input/linux_kernel_git_revlog.csv'))
df['author_dt'] = pd.to_datetime(df['author_timestamp'], unit='s')
time_df = df.groupby(['author_tim... | code |
1003108/cell_15 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
def clean_ts(df):
return df[(df['author_timestamp'] > 1104600000) & (df['author_timestamp'] < 1487807212)]
df = clean_ts(pd.read_csv('../input/linux_kernel_git_revlog.csv'))
df['author_dt'] = pd.to_datetime(df['author_timestamp'], unit='s')
time_df = ... | code |
1003108/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
def clean_ts(df):
return df[(df['author_timestamp'] > 1104600000) & (df['author_timestamp'] < 1487807212)]
df = clean_ts(pd.read_csv('../input/linux_kernel_git_revlog.csv'))
df['author_dt'] = pd.to_datetime(df['author_timestamp'], unit='s')
df.head() | code |
1003108/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
def clean_ts(df):
return df[(df['author_timestamp'] > 1104600000) & (df['author_timestamp'] < 1487807212)]
df = clean_ts(pd.read_csv('../input/linux_kernel_git_revlog.csv'))
df['author_dt'] = pd.to_datetime(df['author_timestamp'], unit='s')
time_df = df.groupby(['author_tim... | code |
128031687/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape | code |
128031687/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.head() | code |
128031687/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes | code |
128031687/cell_34 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().sum()
data.describe() | code |
128031687/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.info() | code |
128031687/cell_44 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().... | code |
128031687/cell_40 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().... | code |
128031687/cell_29 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().sum() | code |
128031687/cell_48 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().... | code |
128031687/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().sum()
data['department... | code |
128031687/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
print('lenght of data is', len(data)) | code |
128031687/cell_52 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().... | code |
128031687/cell_45 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().... | code |
128031687/cell_49 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().... | code |
128031687/cell_32 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().sum()
print('Count of rows in the data is: ', len(data... | code |
128031687/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.tail() | code |
128031687/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns | code |
128031687/cell_31 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().sum()
print('Count of columns in the data is: ', len(d... | code |
128031687/cell_53 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().... | code |
128031687/cell_27 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1)) | code |
128031687/cell_37 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/employees-evaluation-for-promotion/employee_promotion.csv', encoding='ISO-8859-1', engine='python')
data.columns
data.shape
data.dtypes
np.sum(data.isnull().any(axis=1))
data.isnull().sum()
data.hist(figsiz... | code |
105186524/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.s... | code |
105186524/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
youtube.info() | code |
105186524/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count | code |
105186524/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.s... | code |
105186524/cell_19 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.s... | code |
105186524/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 |
105186524/cell_7 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.s... | code |
105186524/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.s... | code |
105186524/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.s... | code |
105186524/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.s... | code |
105186524/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
youtube.head() | code |
105186524/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.s... | code |
105186524/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.s... | code |
105186524/cell_10 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.s... | code |
122244852/cell_2 | [
"text_html_output_1.png"
] | !pip install mlflow dagshub | code |
122244852/cell_7 | [
"text_plain_output_1.png"
] | import mlflow
mlflow.set_tracking_uri('https://dagshub.com/ChiragChauhan4579/MLflow-integration.mlflow')
mlflow.set_experiment(experiment_name='wine-quality') | code |
122244852/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.head() | code |
122244852/cell_16 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import ElasticNet
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn.model_selection import train_test_split, GridSearchCV
import mlflow
import numpy as np
mlflow.set_tracking_uri('https://dagshub.... | code |
122244852/cell_10 | [
"text_plain_output_1.png"
] | y_train.value_counts() | code |
122244852/cell_12 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_html_output_3.png"
] | from sklearn.linear_model import ElasticNet
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import mlflow
import numpy as np
mlflow.set_tracking_uri('https://dagshub.com/ChiragChauhan4579/MLflow-integration.mlflow')
mlflow.set_experiment(experiment_name='wine-quality')
y_train.value_c... | code |
122244852/cell_5 | [
"text_plain_output_1.png"
] | import dagshub
import dagshub
dagshub.init('MLflow-integration', 'ChiragChauhan4579', mlflow=True) | code |
73070733/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
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)
test.head() | code |
73070733/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
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)
y = train['target']
X = train.drop(['target'], axis=1)
X.head() | code |
73070733/cell_18 | [
"text_html_output_1.png"
] | from lightgbm import LGBMRegressor
from sklearn.compose import ColumnTransformer
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_validate, cross_val_predict
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.prepro... | code |
330183/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.info() | code |
330183/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
print('Number of observations in the training set: %d (%d%%)' % (n_train, ratio * 100))
print('Number of obse... | code |
330183/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ticket', 'Cabin', 'Name'], axis=1)
df_titanic_na = df_tit... | code |
330183/cell_83 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
forest = RandomForestClassifier(n_estimators=1000, n_jobs=-1)
forest.fit(X_train, y_train) | code |
330183/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ti... | code |
330183/cell_90 | [
"text_plain_output_1.png"
] | from sklearn import cross_validation, metrics
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test... | code |
330183/cell_87 | [
"text_plain_output_1.png"
] | from sklearn import cross_validation, metrics
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test... | code |
330183/cell_44 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ti... | code |
330183/cell_73 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ticket', 'Cabin', 'Name'], axis=1)
df... | code |
330183/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ti... | code |
330183/cell_50 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ti... | code |
330183/cell_64 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ticket', 'Cabin', 'Name'], axis=1)
df_titanic_na = df_tit... | code |
330183/cell_89 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import cross_validation, metrics
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test... | code |
330183/cell_68 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ticket', 'Cabin', 'Name'], axis=1)
df... | code |
330183/cell_62 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ticket', 'Cabin', 'Name'], axis=1)
df_titanic_na = df_tit... | code |
330183/cell_80 | [
"text_html_output_1.png"
] | from sklearn import cross_validation, metrics
from sklearn.ensemble import RandomForestClassifier | code |
330183/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10) | code |
330183/cell_47 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ti... | code |
330183/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
n_train = df_train.shape[0]
n_test = df_test.shape[0]
ratio = round(n_train / (n_train + n_test), 1)
df_train.sample(10)
df_titanic = df_train.drop(['Ticket', 'Cabin', 'Name'], axis=1)
... | code |
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