path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
34118365/cell_3 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from glob import glob
from itertools import chain
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
all_xray_df = pd.read_csv('/kaggle/input/data/Data_Entry_2017.csv')
all_image_paths = {os.path.basename(x): x for x in glob(os.path.join('/kaggle/in... | code |
34118365/cell_17 | [
"text_plain_output_1.png"
] | from glob import glob
from itertools import chain
from keras.applications.resnet_v2 import ResNet50V2
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping, TensorBoard, ReduceLROnPlateau
from keras.layers import Conv2D, SeparableConv2D, MaxPool2D, LeakyReLU, Activation
from keras.layer... | code |
34118365/cell_14 | [
"text_plain_output_1.png"
] | from glob import glob
from itertools import chain
from keras.applications.resnet_v2 import ResNet50V2
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping, TensorBoard, ReduceLROnPlateau
from keras.layers import Conv2D, SeparableConv2D, MaxPool2D, LeakyReLU, Activation
from keras.layer... | code |
34118365/cell_12 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import multiprocessing as mp
import multiprocessing as mp
cpu_count = mp.cpu_count()
cpu_count | code |
34118365/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from glob import glob
from itertools import chain
from keras.preprocessing.image import ImageDataGenerator
from random import sample
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sklearn.model_selection as skl
all_xray_df = pd.read_cs... | code |
18131743/cell_21 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score,confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
import matplotlib.cm as cm
import matplotlib.pyplot as plt
... | code |
18131743/cell_9 | [
"application_vnd.jupyter.stderr_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/data.csv')
import seaborn as sns
from pandas.plotting import scatter_matrix
import matplotlib.cm as cm
import matplotlib.pyplot as plt
data['diagnosis'] = data['d... | code |
18131743/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
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/data.c... | code |
18131743/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import ExtraTreesRegressor
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
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/data.... | code |
18131743/cell_20 | [
"image_output_1.png"
] | from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
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... | code |
18131743/cell_29 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from mlxtend.regressor import StackingCVRegressor
from sklearn import ensemble
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection impo... | code |
18131743/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
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/data.cs... | code |
18131743/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18131743/cell_11 | [
"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
data = pd.read_csv('../input/data.csv')
import seaborn as sns
from pandas.plotting import scatter_matrix
import matplotlib.cm as cm
import matplotlib.pyplot as plt
data['diagnosis'] = data['d... | code |
18131743/cell_7 | [
"application_vnd.jupyter.stderr_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/data.csv')
import seaborn as sns
from pandas.plotting import scatter_matrix
import matplotlib.cm as cm
import matplotlib.pyplot as plt
data['diagnosis'] = data['d... | code |
18131743/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
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/data.csv')
import seaborn as sns
from pandas.plotting ... | code |
18131743/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score,confusion_matrix
from sklearn.model_selection import train_test_split
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (... | code |
18131743/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/data.csv')
data.info() | code |
18131743/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
data = pd.read_csv('../input/data.csv')
import seaborn as sns
from pandas.plotting import scatter_matrix
import matplotlib.cm as cm
import matplotlib.pyplot as plt
data['diagnosis'] = data['d... | code |
18131743/cell_31 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from mlxtend.regressor import StackingCVRegressor
from sklearn import ensemble
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection impo... | code |
18131743/cell_24 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
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/dat... | code |
18131743/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
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/data... | code |
18131743/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import ensemble
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
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/data.csv')
import seabor... | code |
18131743/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/data.csv')
import seaborn as sns
from pandas.plotting import scatter_matrix
import matplotlib.cm as cm
import matplotlib.pyplot as plt
data['diagnosis'] = data['diagnosis'].map({'M': 1, 'B': 0})
y = pd.DataFrame(data=d... | code |
106194784/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
athlete_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
region_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/noc_regions.csv')
data = pd.merge(athlete_df, region_df, how='left', on='NOC')
data | code |
106194784/cell_30 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
athlete_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
region_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/noc_regions.csv')
data = pd.merg... | code |
106194784/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
athlete_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
region_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/noc_regions.csv')
region_df | code |
106194784/cell_29 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
athlete_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
region_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/noc_regions.csv')
data = pd.merg... | code |
106194784/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
athlete_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
region_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/noc_regions.csv')
data = pd.merge(athlete_df, region_df, how='left', on='NOC')
data.describe() | code |
106194784/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
athlete_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
region_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/noc_regions.csv')
data = pd.merge(athlete_df, region_df, how='left', on='NOC')
data.rename(columns={'region... | code |
106194784/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
athlete_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
region_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/noc_regions.csv')
data = pd.merge(athlete_df, region_df, how='left', on='NOC')
data.rename(columns={'region... | code |
106194784/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
athlete_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
region_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/noc_regions.csv')
data = pd.merge(athlete_df, region_df, how='left', on='NOC')
data.rename(columns={'region... | code |
106194784/cell_24 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
athlete_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
region_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/noc_regions.csv')
data = pd.merge(athlete_df, region_df, how='left', on='NOC')
data.ren... | code |
106194784/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
athlete_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
region_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/noc_regions.csv')
data = pd.merge(athlete_df, region_df, how='left', on='NOC')
data.rename(columns={'region... | code |
106194784/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
athlete_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
region_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/noc_regions.csv')
data = pd.merg... | code |
106194784/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
athlete_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
region_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/noc_regions.csv')
data = pd.merge(athlete_df, region_df, how='left', on='NOC')
data.info() | code |
106194784/cell_27 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
athlete_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
region_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/noc_regions.csv')
data = pd.merg... | code |
106194784/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
athlete_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
region_df = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/noc_regions.csv')
athlete_df | code |
106202299/cell_13 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import holidays
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.mo... | code |
106202299/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"
] | _, ax = plt.subplots(12, 4, figsize=(14, 50))
test_df['num_sold'] = 0
oh_cols = ['day', 'day_of_week', 'week', 'quarter', 'important_dates']
encoder = OneHotEncoder(sparse=False)
for country, i in zip(train_df['country'].unique(), range(6)):
for store, k in zip(train_df['store'].unique(), range(2)):
for pro... | code |
106202299/cell_7 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.model_selection import train_test_split... | code |
106202299/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.model_selection import train_test_split... | code |
49119038/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from dask.diagnostics import ProgressBar
import dask.dataframe as dd
import datetime
import math
dask_data = dd.read_csv('./train.csv')
dask_data.columns
dask_data.compute().shape
len(dask_data.columns)
dask_data.isnull().sum().compute()
dask_data.fare_amount.mean().compute()
missing_values = dask_data.isnull().... | code |
49119038/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from dask.diagnostics import ProgressBar
import dask.dataframe as dd
import datetime
dask_data = dd.read_csv('./train.csv')
dask_data.columns
with ProgressBar():
dask_data.head() | code |
49119038/cell_25 | [
"text_plain_output_1.png"
] | from dask.diagnostics import ProgressBar
import dask.dataframe as dd
import datetime
import math
dask_data = dd.read_csv('./train.csv')
dask_data.columns
dask_data.compute().shape
len(dask_data.columns)
dask_data.isnull().sum().compute()
dask_data.fare_amount.mean().compute()
missing_values = dask_data.isnull().... | code |
49119038/cell_23 | [
"text_plain_output_1.png"
] | from dask.diagnostics import ProgressBar
import dask.dataframe as dd
import datetime
import math
dask_data = dd.read_csv('./train.csv')
dask_data.columns
dask_data.compute().shape
len(dask_data.columns)
dask_data.isnull().sum().compute()
dask_data.fare_amount.mean().compute()
missing_values = dask_data.isnull().... | code |
49119038/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import dask.dataframe as dd
import datetime
print('Start of Dask Read:', datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
dask_data = dd.read_csv('./train.csv')
print('End of Dask Read:', datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')) | code |
49119038/cell_19 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_html_output_1.png",
"text_plain_output_1.png"
] | from dask.diagnostics import ProgressBar
import dask.dataframe as dd
import datetime
dask_data = dd.read_csv('./train.csv')
dask_data.columns
dask_data.compute().shape
len(dask_data.columns)
dask_data.isnull().sum().compute()
dask_data.fare_amount.mean().compute()
missing_values = dask_data.isnull().sum().compute... | code |
49119038/cell_8 | [
"text_html_output_1.png"
] | import dask.dataframe as dd
import datetime
dask_data = dd.read_csv('./train.csv')
dask_data.columns | code |
49119038/cell_15 | [
"text_plain_output_1.png"
] | from dask.diagnostics import ProgressBar
import dask.dataframe as dd
import datetime
dask_data = dd.read_csv('./train.csv')
dask_data.columns
dask_data.compute().shape
len(dask_data.columns)
dask_data.isnull().sum().compute()
dask_data.fare_amount.mean().compute()
missing_values = dask_data.isnull().sum().compute... | code |
49119038/cell_16 | [
"text_html_output_1.png"
] | from dask.diagnostics import ProgressBar
import dask.dataframe as dd
import datetime
dask_data = dd.read_csv('./train.csv')
dask_data.columns
dask_data.compute().shape
len(dask_data.columns)
dask_data.isnull().sum().compute()
dask_data.fare_amount.mean().compute()
missing_values = dask_data.isnull().sum().compute... | code |
49119038/cell_3 | [
"text_plain_output_1.png"
] | from dask.diagnostics import ProgressBar
from dask.distributed import progress
from distributed import Client
client = Client()
client
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = 'all'
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
impo... | code |
49119038/cell_10 | [
"text_plain_output_1.png"
] | from dask.diagnostics import ProgressBar
import dask.dataframe as dd
import datetime
dask_data = dd.read_csv('./train.csv')
dask_data.columns
display(dask_data.head(2))
print('Information:')
dask_data.compute().info()
print('Shape:')
dask_data.compute().shape
print('Describe:')
dask_data.describe().compute()
print... | code |
49119038/cell_12 | [
"text_plain_output_1.png"
] | pandas_data.shape
pandas_data.head(2)
pandas_data.describe
pandas_data['fare_amount'].unique()
pandas_data.isnull().sum()
pandas_data.isna().sum() | code |
121152199/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airbnb-user-pathways/airbnb.csv')
df.drop('id_visitor', axis=1, inplace=True)
df.drop('id_session', axis=1, inplace=True)
df.drop('next_id_session', axis=1, inplace=True)
df.drop('dim_user_agent', axis=1, inplace=Tru... | code |
121152199/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airbnb-user-pathways/airbnb.csv')
df.drop('id_visitor', axis=1, inplace=True)
df.drop('id_session', axis=1, inplace=True)
df.drop('next_id_session', axis=1, inplace=True)
df.drop('dim_user_agent', axis=1, inplace=Tru... | code |
121152199/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airbnb-user-pathways/airbnb.csv')
df.drop('id_visitor', axis=1, inplace=True)
df.drop('id_session', axis=1, inplace=True)
df.drop('next_id_session', axis=1, inplace=True)
df.drop('dim_user_agent', axis=1, inplace=Tru... | code |
121152199/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airbnb-user-pathways/airbnb.csv')
df.drop('id_visitor', axis=1, inplace=True)
df.drop('id_session', axis=1, inplace=True)
df.drop('next_id_session', axis=1, inplace=True)
df.drop('dim_user_agent', axis=1, inplace=Tru... | code |
121152199/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airbnb-user-pathways/airbnb.csv')
df.drop('id_visitor', axis=1, inplace=True)
df.drop('id_session', axis=1, inplace=True)
df.drop('next_id_session', axis=1, inplace=True)
df.drop('dim_user_agent', axis=1, inplace=Tru... | code |
121152199/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
121152199/cell_7 | [
"text_html_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airbnb-user-pathways/airbnb.csv')
df.drop('id_visitor', axis=1, inplace=True)
df.drop('id_session', axis=1, inplace=True)
df.drop('next_id_session', axis=1, inplace=True)
df.drop('dim_user_agent', axis=1, inplace=Tru... | code |
121152199/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airbnb-user-pathways/airbnb.csv')
df.drop('id_visitor', axis=1, inplace=True)
df.drop('id_session', axis=1, inplace=True)
df.drop('next_id_session', axis=1, inplace=True)
df.drop('dim_user_agent', axis=1, inplace=Tru... | code |
121152199/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airbnb-user-pathways/airbnb.csv')
df.drop('id_visitor', axis=1, inplace=True)
df.drop('id_session', axis=1, inplace=True)
df.drop('next_id_session', axis=1, inplace=True)
df.drop('dim_user_agent', axis=1, inplace=Tru... | code |
121152199/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/airbnb-user-pathways/airbnb.csv')
df.drop('id_visitor', axis=1, inplace=True)
df.drop('id_session', axis=1, inplace=True)
df.drop('next_id_session', axis=1, inplace=True)
df.drop('dim_user_agent', axis=1, inplace=Tru... | code |
88093824/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
from sklearn.metrics import recall_score
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
from sklearn.metrics import confusion_matrix
lr = LogisticRegression(solver='liblinear')
lr.fit(X_... | code |
88093824/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.metrics import f1_score
from sklearn.metrics import recall_score
from xgboost import XGBRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
data.isnull().sum()
data.dtypes
X = data.drop('Class', axis=1)
y... | code |
88093824/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 |
88093824/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
data.isnull().sum()
data.dtypes | code |
88093824/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.metrics import f1_score
from xgboost import XGBRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
data.isnull().sum()
data.dtypes
X = data.drop('Class', axis=1)
y = data['Class']
from xgboost import XGBR... | code |
88093824/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/creditcardfraud/creditcard.csv')
data.isnull().sum()
data.dtypes
data['Class'].value_counts() | code |
88093824/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
data.head() | code |
88093824/cell_12 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
from sklearn.metrics import confusion_matrix
lr = LogisticRegression(solver='liblinear')
lr.fit(X_train, y_train)
y_pred = lr.predict(X_test... | code |
88093824/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/creditcardfraud/creditcard.csv')
data.isnull().sum() | code |
50223616/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
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/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
df = df.drop(['EmployeeNumber', 'Employee... | code |
50223616/cell_4 | [
"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/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
df = df.drop(['EmployeeNumber', 'EmployeeCount', 'StandardHours'], axis=1)
df.isna().sum() | code |
50223616/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
df = df.drop(['EmployeeNumber', 'EmployeeCount', 'StandardHours'], axis=1)
df.isna().sum()
df.isnull().values.any()
df.describe() | code |
50223616/cell_2 | [
"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/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
df.head() | code |
50223616/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
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/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Att... | code |
50223616/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 |
50223616/cell_7 | [
"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/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
df = df.drop(['EmployeeNumber', 'EmployeeCount', 'StandardHours'], axis=1)
df.isna().sum()
df.isnull().values.any()
categorial_col = df.select_... | code |
50223616/cell_8 | [
"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/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
df = df.drop(['EmployeeNumber', 'EmployeeCount', 'StandardHours'], axis=1)
df.isna().sum(... | code |
50223616/cell_10 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
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/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
df = df.drop(['EmployeeNumber', 'Em... | code |
50223616/cell_5 | [
"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/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
df = df.drop(['EmployeeNumber', 'EmployeeCount', 'StandardHours'], axis=1)
df.isna().sum()
df.isnull().values.any() | code |
122251150/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
predict = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train.dtypes.value_counts()
missing_va... | code |
122251150/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
predict = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train.dtypes.value_counts() | code |
122251150/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
predict = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train.dtypes.value_counts()
missing_vals_train = pd.DataFrame(train.isna().sum(), columns=['Su... | code |
122251150/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
predict = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train.dtypes.value_counts()
missing_va... | code |
122251150/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
predict = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train.dtypes.value_counts()
missing_va... | code |
122251150/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 |
122251150/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
predict = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train.info() | code |
122251150/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
predict = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train.dtypes.value_counts()
missing_vals_train = pd.DataFrame(train.isna().sum(), columns=['Su... | code |
122251150/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
predict = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train.dtypes.value_counts()
missing_vals_train = pd.DataFrame(train.isna().sum(), columns=['Su... | code |
122251150/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
predict = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train.dtypes.value_counts()
train.head() | code |
122251150/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)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
predict = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train.dtypes.value_counts()
missing_vals_train = pd.DataFrame(train.isna().sum(), columns=['Su... | code |
122251150/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
predict = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train.dtypes.value_counts()
missing_va... | code |
72107386/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
train.head() | code |
72107386/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
test.head() | code |
72107386/cell_15 | [
"text_html_output_1.png"
] | from catboost import Pool, CatBoostRegressor
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
X_train = train.drop(['id', 'target'], axis=1)
y_train = train['target']
X_test = test.drop(['id'], axis=1)
cat_features = [i for i, col in e... | code |
72107386/cell_17 | [
"text_html_output_1.png"
] | from catboost import Pool, CatBoostRegressor
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
X_train = train.drop(['id', 'target'], axis=1)
y_train = train['target']
X_test = test.drop(['id'], axis=1)
cat_features = [i for i, col in e... | code |
72107386/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
X_train = train.drop(['id', 'target'], axis=1)
y_train = train['target']
X_test = test.drop(['id'], axis=1)
cat_features = [i for i, col in enumerate(X_train.columns) if 'cat' in col]
cat... | code |
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