path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
33115163/cell_43 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
test = pd.read_csv('../input/mobile-price-classification/test.csv')
train = pd.read_csv('../input/mobile-price-classification/train.csv')
numerical = ['battery_power', 'clock_speed', 'fc', 'int_... | code |
33115163/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
test = pd.read_csv('../input/mobile-price-classification/test.csv')
train = pd.read_csv('../input/mobile-price-classification/train.csv')
numerical = ['battery_power', 'clock_speed', 'fc', 'int_memory', 'm_dep', 'mobile_wt', 'n_cores', 'pc', ... | code |
33115163/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
test = pd.read_csv('../input/mobile-price-classification/test.csv')
train = pd.read_csv('../input/mobile-price-classification/train.csv')
numerical = ['battery_power', 'clock_speed', 'fc', 'int_memory', 'm_dep', 'mobile_wt', 'n_cores', 'pc', ... | code |
33115163/cell_53 | [
"text_html_output_1.png"
] | print(X_train.shape)
print(y_train.shape)
print(X_val.shape)
print(y_val.shape) | code |
33115163/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
test = pd.read_csv('../input/mobile-price-classification/test.csv')
train = pd.read_csv('../input/mobile-price-classification/train.csv')
test.info() | code |
33115163/cell_37 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
test = pd.read_csv('../input/mobile-price-classification/test.csv')
train = pd.read_csv('../input/mobile-price-classification/train.csv')
numerical = ['battery_power', 'clock_speed', 'fc', 'int_memory', 'm_dep', 'mobile_wt', 'n_cores', 'pc', ... | code |
2013301/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
DOWN_AT_HALF = -1
TIE_AT_HALF = 0
UP_AT_HALF = 1
df16 = pd.read_csv('../input/2016-17_teamBoxScore.csv')
df17 = pd.read_csv('../input/2017-18_teamBoxScore.csv')
df = pd.concat((df16, df17))
df2 = ... | code |
2013301/cell_25 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
DOWN_AT_HALF = -1
TIE_AT_HALF = 0
UP_AT_HALF = 1
df16 = pd.read_csv('../input/2016-17_teamBoxScore.csv')
df17 = pd.read_csv('../input/2017-18_teamBoxScore.csv')
df = pd.concat((df16, df17))
df2 = ... | code |
2013301/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
DOWN_AT_HALF = -1
TIE_AT_HALF = 0
UP_AT_HALF = 1
df16 = pd.read_csv('../input/2016-17_teamBoxScore.csv')
df17 = pd.read_csv('../input/2017-18_teamBoxScore.csv')
df = pd.concat((df16, df17))
df2 = df[['teamAbbr', 'tea... | code |
2013301/cell_23 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
DOWN_AT_HALF = -1
TIE_AT_HALF = 0
UP_AT_HALF = 1
df16 = pd.read_csv('../input/2016-17_teamBoxScore.csv')
df17 = pd.read_csv('../input/2017-18_teamBoxScore.csv')
df = pd.concat((df16, df17))
df2 = ... | code |
2013301/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
DOWN_AT_HALF = -1
TIE_AT_HALF = 0
UP_AT_HALF = 1
df16 = pd.read_csv('../input/2016-17_teamBoxScore.csv')
df17 = pd.read_csv('../input/2017-18_teamBoxScore.csv')
df = pd.concat((df16, df17))
df2 = ... | code |
2013301/cell_26 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
DOWN_AT_HALF = -1
TIE_AT_HALF = 0
UP_AT_HALF = 1
df16 = pd.read_csv('../input/2016-17_teamBoxScore.csv')
df17 = pd.read_csv('../input/2017-18_teamBoxScore.csv')
df = pd.concat((df16, df17))
df2 = ... | code |
2013301/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
DOWN_AT_HALF = -1
TIE_AT_HALF = 0
UP_AT_HALF = 1
df16 = pd.read_csv('../input/2016-17_teamBoxScore.csv')
df17 = pd.read_csv('../input/2017-18_teamBoxScore.csv')
df = pd.concat((df16, df17))
df2 = ... | code |
2013301/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
DOWN_AT_HALF = -1
TIE_AT_HALF = 0
UP_AT_HALF = 1
df16 = pd.read_csv('../input/2016-17_teamBoxScore.csv')
df17 = pd.read_csv('../input/2017-18_teamBoxScore.csv')
df = pd.concat((df16, df17))
df2 = ... | code |
2013301/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
DOWN_AT_HALF = -1
TIE_AT_HALF = 0
UP_AT_HALF = 1
df16 = pd.read_csv('../input/2016-17_teamBoxScore.csv')
df17 = pd.read_csv('../input/2017-18_teamBoxScore.csv')
df = pd.concat((df16, df17))
df2 = ... | code |
2013301/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
DOWN_AT_HALF = -1
TIE_AT_HALF = 0
UP_AT_HALF = 1
df16 = pd.read_csv('../input/2016-17_teamBoxScore.csv')
df17 = pd.read_csv('../input/2017-18_teamBoxScore.csv')
df = pd.concat((df16, df17))
df2 = ... | code |
2013301/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
DOWN_AT_HALF = -1
TIE_AT_HALF = 0
UP_AT_HALF = 1
df16 = pd.read_csv('../input/2016-17_teamBoxScore.csv')
df17 = pd.read_csv('../input/2017-18_teamBoxScore.csv')
df = pd.concat((df16, df17))
df2 = ... | code |
2013301/cell_24 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
DOWN_AT_HALF = -1
TIE_AT_HALF = 0
UP_AT_HALF = 1
df16 = pd.read_csv('../input/2016-17_teamBoxScore.csv')
df17 = pd.read_csv('../input/2017-18_teamBoxScore.csv')
df = pd.concat((df16, df17))
df2 = ... | code |
2013301/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
DOWN_AT_HALF = -1
TIE_AT_HALF = 0
UP_AT_HALF = 1
df16 = pd.read_csv('../input/2016-17_teamBoxScore.csv')
df17 = pd.read_csv('../input/2017-18_teamBoxScore.csv')
df = pd.concat((df16, df17))
df2 = ... | code |
2013301/cell_10 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
DOWN_AT_HALF = -1
TIE_AT_HALF = 0
UP_AT_HALF = 1
df16 = pd.read_csv('../input/2016-17_teamBoxScore.csv')
df17 = pd.read_csv('../input/2017-18_teamBoxScore.csv')
df = pd.concat((df16, df17))
df2 = ... | code |
2013301/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
DOWN_AT_HALF = -1
TIE_AT_HALF = 0
UP_AT_HALF = 1
df16 = pd.read_csv('../input/2016-17_teamBoxScore.csv')
df17 = pd.read_csv('../input/2017-18_teamBoxScore.csv')
df = pd.concat((df16, df17))
df2 = ... | code |
104127726/cell_4 | [
"text_plain_output_1.png"
] | !nvidia-smi | code |
104127726/cell_3 | [
"text_plain_output_1.png"
] | # Installing requierd libraires
!pip install --upgrade -q fastai
!pip install timm -q
!pip install albumentations==0.4.6 -q
!pip install transformers -q | code |
104127726/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
print(f'Using device: {device}') | code |
1005555/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/911.csv')
dataset[dataset['Category'] == 'Traffic']['Sub-Category'].value_counts().head(6) | code |
1005555/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/911.csv')
dataset['Category'].value_counts() | code |
1005555/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/911.csv')
dataset.info() | code |
1005555/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/911.csv')
dataset['title'].value_counts().head(5) | code |
1005555/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/911.csv')
dataset[dataset['Category'] == 'EMS']['Sub-Category'].value_counts().head(6) | code |
1005555/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/911.csv')
dataset['dayofweek'].value_counts() | code |
1005555/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
dataset = pd.read_csv('../input/911.csv')
sns.countplot('dayofweek', data=dataset) | code |
1005555/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
dataset = pd.read_csv('../input/911.csv')
sns.countplot('Category', data=dataset) | code |
1005555/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
dataset = pd.read_csv('../input/911.csv')
sns.countplot('timezone', data=dataset) | code |
1005555/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/911.csv')
dataset['title'].nunique() | code |
1005555/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/911.csv')
dataset[dataset['Category'] == 'Fire']['Sub-Category'].value_counts().head(6) | code |
1005555/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/911.csv')
dataset.head(5) | code |
74060454/cell_13 | [
"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
import matplotlib.pyplot as plt
import seaborn as sns
house_df = pd.read_csv('../input/housesalesprediction/kc_house_data.csv')
house_df.isnull().sum()
house_df.drop(15870, inplace=True)
pl... | code |
74060454/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
house_df = pd.read_csv('../input/housesalesprediction/kc_house_data.csv')
my_submission = pd.DataFrame({'Id': test.Id, 'SalePrice': predicted_prices})
my_submission.to_csv('submission.csv', ind... | code |
74060454/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
house_df = pd.read_csv('../input/housesalesprediction/kc_house_data.csv')
house_df.isnull().sum()
house_df.info() | code |
74060454/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
house_df = pd.read_csv('../input/housesalespredicti... | code |
74060454/cell_2 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
house_df = pd.read_csv('../input/housesalesprediction/kc_house_data.csv')
house_df.head() | code |
74060454/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
import matplotlib.pyplot as plt
import seaborn as sns
house_df = pd.read_csv('../input/housesalesprediction/kc_house_data.csv')
house_df.isnull().sum()
house_df.drop(15870, inplace=True)
pl... | code |
74060454/cell_19 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
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
import matplotlib.pyplot as plt
import seaborn as sns
house_df = pd.read_csv('../input/... | code |
74060454/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 |
74060454/cell_7 | [
"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
import matplotlib.pyplot as plt
import seaborn as sns
house_df = pd.read_csv('../input/housesalesprediction/kc_house_data.csv')
house_df.isnull().sum()
plt.figure(figsize=(12, 10))
sns.scatt... | code |
74060454/cell_18 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
house_df = pd.read_csv('../input/housesalesprediction/kc_house_data.csv')
house_df.isnul... | code |
74060454/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
house_df = pd.read_csv('../input/housesalesprediction/kc_house_data.csv')
house_df.isnull().sum()
house_df[house_df['bedrooms'] > 30] | code |
74060454/cell_16 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
house_df = pd.read_csv('../input/housesalesprediction/kc_house_data.csv')
house_df.isnul... | code |
74060454/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
house_df = pd.read_csv('../input/housesalesprediction/kc_house_data.csv')
house_df.isnull().sum() | code |
74060454/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sklearn.metrics as m
import matplotlib.pyplot as plt
import seaborn as sns
house_df = pd.read_csv('../input/housesalesprediction/kc_hou... | code |
74060454/cell_14 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
house_df = pd.read_csv('../input/housesalesprediction/kc_house_data.csv')
house_df.isnull().sum()
house_df.drop(15870, inplace=True)
from... | code |
74060454/cell_22 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error,mean_absolute_error
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import... | code |
74060454/cell_10 | [
"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
import matplotlib.pyplot as plt
import seaborn as sns
house_df = pd.read_csv('../input/housesalesprediction/kc_house_data.csv')
house_df.isnull().sum()
house_df.drop(15870, inplace=True)
pl... | code |
74060454/cell_12 | [
"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
import matplotlib.pyplot as plt
import seaborn as sns
house_df = pd.read_csv('../input/housesalesprediction/kc_house_data.csv')
house_df.isnull().sum()
house_df.drop(15870, inplace=True)
pl... | code |
74060454/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
house_df = pd.read_csv('../input/housesalesprediction/kc_house_data.csv')
house_df.isnull().sum()
house_df.describe() | code |
128049389/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report
from sklearn.metrics ... | code |
128049389/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
df_test = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
df_test | code |
128049389/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold
from sklearn.preprocessing import StandardScaler
import pandas as pd
df_train = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
df_test = pd.read_csv('/kaggle/input/... | code |
128049389/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
i... | code |
128049389/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold
from sklearn.preprocessing import StandardScaler
import pandas as pd
df_train = pd.read_csv('/k... | code |
128049389/cell_8 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report
from sklearn.model_se... | code |
128049389/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
df_test = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
df_train.info() | code |
128049389/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold
from sklearn.preprocessing import StandardScaler
import pandas as pd
df_train = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
df_test = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')
print(df_train.is... | code |
2008965/cell_4 | [
"text_plain_output_1.png"
] | child_happiness = np.full((n_gift_type, n_children), -1 * multiplier, dtype=np.int16)
gift_happiness = np.full((n_gift_type, n_children), -1, dtype=np.int16)
to_add = (np.arange(n_gift_pref, 0, -1) * ratio_child_happiness + 1) * int(multiplier)
for child, wishlist in tqdm(enumerate(child_wishlists)):
child_happines... | code |
2008965/cell_8 | [
"text_plain_output_1.png"
] | children, gifts = zip(*random_sub)
for _ in range(100):
score = avg_normalized_happiness(children, gifts)
print('ANH', score) | code |
2008965/cell_12 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | for _ in range(100):
score = avg_normalized_happiness(random_sub)
print('ANH', score) | code |
73092218/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
import xgboost as xgb
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-day... | code |
73092218/cell_4 | [
"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)
sub = pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv')
test.info() | code |
73092218/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)
sub = pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv')
y = train.target
X_all = pd.get_dummies(train.drop(['target'], axis=1).append(test)).copy()... | code |
73092218/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import pandas as pd
import xgboost as xgb
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-... | code |
73092218/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
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)
sub = pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv')
y = train.target
X_all = pd.get_dummi... | code |
73092218/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import pandas as pd
import xgboost as xgb
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-... | code |
73092218/cell_3 | [
"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)
sub = pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv')
train.info() | code |
73092218/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import pandas as pd
import xgboost as xgb
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-... | code |
73092218/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import pandas as pd
import xgboost as xgb
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-... | code |
73092218/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import pandas as pd
import xgboost as xgb
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-... | code |
72078585/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/tabulardata-kfolds-created/train_folds.csv')
df_test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
sample_submission = pd.read_csv('../input/tabular-playground-series-aug-2021/sample_submission.csv')
useful_features = [c for c in df_train.co... | code |
72078585/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
from xgboost import XGBRegressor
import pandas as pd
df_train = pd.read_csv('../input/tabulardata-kfolds-created/train_folds.csv')
df_test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
sample_submission = pd.read_csv('../input/tabular-playground-... | code |
72078585/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/tabulardata-kfolds-created/train_folds.csv')
df_test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
sample_submission = pd.read_csv('../input/tabular-playground-series-aug-2021/sample_submission.csv')
sample_submission.head() | code |
1006593/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('../input/train.csv')
X = dataset.iloc[:, [4, 2, 6, 5]... | code |
1006593/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('../i... | code |
1006593/cell_19 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd
dataset = pd.read_csv('../input/train.csv')
X = dataset.iloc[:, [4, 2, 6, 5]].values
y = dataset.iloc[:, 1].values
from sklearn.model_selection... | code |
1006593/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('../input/train.csv')
X = dataset.iloc[:, [4, 2, 6, 5]].values
y = dataset.iloc[:, 1].values
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
fig.se... | code |
1006593/cell_8 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import pandas as pd
dataset = pd.read_csv('../input/train.csv')
X = dataset.iloc[:, [4, 2, 6, 5]].values
y = dataset.iloc[:, 1].values
from sklearn.model_selection import train_test_split
X_train, X_test, y_train,... | code |
1006593/cell_16 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/train.csv')
realdata = pd.read_csv('../input/test.csv')
X_real = realdata.iloc[:, [3, 1, 5, 4]].values
X_real | code |
1006593/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/train.csv')
dataset.head() | code |
1006593/cell_17 | [
"image_output_1.png"
] | from sklearn.preprocessing import Imputer
from sklearn.preprocessing import Imputer
import pandas as pd
dataset = pd.read_csv('../input/train.csv')
X = dataset.iloc[:, [4, 2, 6, 5]].values
y = dataset.iloc[:, 1].values
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values='NaN', strategy='mean... | code |
1006593/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/train.csv')
realdata = pd.read_csv('../input/test.csv')
realdata.head() | code |
1006593/cell_10 | [
"text_html_output_1.png"
] | from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import pandas as pd
dataset = pd.read_csv('../input/train.csv')
X = dataset.iloc[:, [4, 2, 6, 5]].values
y = dataset... | code |
1006593/cell_12 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('../input/train.csv')
X = dataset.iloc[:, [4, 2, 6, 5]... | code |
49124799/cell_13 | [
"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/hackerearth-love-in-the-time-of-screens/data.csv')
df.drop(columns=['user_id', 'username'], inplace=True)
df.shape
lan = []
for i in df['language']:
l = i.split(',')
for j in l:
if j not in lan:
... | code |
49124799/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('../input/hackerearth-love-in-the-time-of-screens/data.csv')
df.drop(columns=['user_id', 'username'], inplace=True)
df.shape | code |
49124799/cell_34 | [
"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/hackerearth-love-in-the-time-of-screens/data.csv')
data = pd.DataFrame()
data.head() | code |
49124799/cell_23 | [
"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/hackerearth-love-in-the-time-of-screens/data.csv')
df.drop(columns=['user_id', 'username'], inplace=True)
df.shape
df.drop(columns=['language', 'bio'], inplace=True)
df.shape
a = df.dtypes
df.shape | code |
49124799/cell_20 | [
"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/hackerearth-love-in-the-time-of-screens/data.csv')
df.drop(columns=['user_id', 'username'], inplace=True)
df.shape
df.drop(columns=['language', 'bio'], inplace=True)
df.shape
a = df.dtypes
df.head() | code |
49124799/cell_6 | [
"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/hackerearth-love-in-the-time-of-screens/data.csv')
df.drop(columns=['user_id', 'username'], inplace=True)
df.head() | code |
49124799/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.metrics.pairwise import cosine_similarity
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/hackerearth-love-in-the-time-of-screens/data.csv')
df.drop(columns=['user_id', 'username'], inplace=True)
df.shape
lan = []
for i in df['language']:
l = i.spl... | code |
49124799/cell_26 | [
"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/hackerearth-love-in-the-time-of-screens/data.csv')
df.drop(columns=['user_id', 'username'], inplace=True)
df.shape
df.drop(columns=['language', 'bio'], inplace=True)
df.shape
a = df.dtypes
df.shape
X = df.values
X... | code |
49124799/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('../input/hackerearth-love-in-the-time-of-screens/data.csv')
df.drop(columns=['user_id', 'username'], inplace=True)
df.shape
lan = []
for i in df['language']:
l = i.split(',')
for j in l:
if j not in lan:
... | code |
49124799/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 |
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