path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
72101116/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import math, random
from sklearn.model_selection import KFold, train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import LabelEncoder
pd.set_option(... | code |
72101116/cell_28 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | test_feature_matrix.head() | code |
72101116/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import math, random
from sklearn.model_selection import KFold, train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import LabelEncoder
pd.set_option(... | code |
72101116/cell_17 | [
"text_plain_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | study = optuna.create_study(direction='minimize')
study.optimize(objective, n_trials=5)
print('Number of finished trials:', len(study.trials))
print('Best trial:', study.best_trial.params)
print('Best score:', study.best_trial.value) | code |
72101116/cell_22 | [
"text_html_output_2.png"
] | study.best_params | code |
72101116/cell_27 | [
"text_plain_output_1.png"
] | import featuretools as ft
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import math, random
from sklearn.model_selection import KFold, train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import ... | code |
130022661/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn import preprocessing
... | code |
130022661/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn import preprocessing
... | code |
130022661/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn import preprocessing
... | code |
130022661/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn import preprocessing
from sklearn.model_sele... | code |
130022661/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn import preprocessing
... | code |
130022661/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn import preprocessing
... | code |
130022661/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 |
130022661/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn import preprocessing
... | code |
130022661/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn import preprocessing
... | code |
130022661/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn import preprocessing
... | code |
130022661/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn import preprocessing
from sklearn.model_sele... | code |
130022661/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn import preprocessing
... | code |
130022661/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn import preprocessing
... | code |
130022661/cell_10 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn import preprocessing
... | code |
130022661/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn import preprocessing
... | code |
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