path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
73101177/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
ss = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.describe() | code |
73101177/cell_16 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
ss = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train_df = train.drop('id', axis=1)
num_data = t... | code |
73101177/cell_38 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
ss = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train_df = train.drop('id', axis=1)
def get_uniq... | code |
73101177/cell_35 | [
"image_output_1.png"
] | from sklearn.compose import make_column_transformer
from sklearn.preprocessing import StandardScaler, RobustScaler,OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-... | code |
73101177/cell_31 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
ss = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train_df = train.drop('id', axis=1)
def get_uniq... | code |
73101177/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
ss = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
print(train.shape)
train.head() | code |
73101177/cell_36 | [
"image_output_1.png"
] | from sklearn.compose import make_column_transformer
from sklearn.preprocessing import StandardScaler, RobustScaler,OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-... | code |
16137929/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/Admission_Predict_Ver1.1.csv')
df.describe().T
df.columns
df.columns = ['sno', 'GRE', 'TOEFL', 'university_rating', 'SOP', 'LOR', 'CGPA', 'research', 'admit_chance']
plt.tight_layout()
df.research.value_counts()... | code |
16137929/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/Admission_Predict_Ver1.1.csv')
df.describe().T
df.columns
df.columns = ['sno', 'GRE', 'TOEFL', 'university_rating', 'SOP', 'LOR', 'CGPA', 'research', 'admit_chance']
sns.pairplot(data=df, diag_kind='kde') | code |
16137929/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/Admission_Predict_Ver1.1.csv')
df.describe().T
print('Showing Meta Data :')
df.info() | code |
16137929/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/Admission_Predict_Ver1.1.csv')
df.describe().T
df.columns
df.columns = ['sno', 'GRE', 'TOEFL', 'university_rating', 'SOP', 'LOR', 'CGPA', 'research', 'admit_chance']
pd.isnull(df).sum()
plt.tight_layout()
df.re... | code |
16137929/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/Admission_Predict_Ver1.1.csv')
df.describe().T
df.columns
df.columns = ['sno', 'GRE', 'TOEFL', 'university_rating', 'SOP', 'LOR', 'CGPA', 'research', 'admit_chance']
pd.isnull(df).sum()
plt.tight_layout()
df.re... | code |
16137929/cell_20 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/Admission_Predict_Ver1.1.csv')
df.describe().T
df.columns
df.columns = ['sno', 'GRE', 'TOEFL', 'university_rating', 'SOP', 'LOR', 'CGPA', 'research', 'admit_chance']
plt.tight_layout()
df.research.value_counts()... | code |
16137929/cell_26 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/Admission_Predict_Ver1.1.csv')
df.describe().T
df.columns
df.columns = ['sno', 'GRE', 'TOEFL', 'university_rating', 'SOP', 'LOR', 'CGPA', 'research', 'admit_chance']
pd.isnull(df).sum()
plt.tight_layout()
df.re... | code |
16137929/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/Admission_Predict_Ver1.1.csv')
df.describe().T
df.columns
df.columns = ['sno', 'GRE', 'TOEFL', 'university_rating', 'SOP', 'LOR', 'CGPA', 'research', 'admit_chance']
df.head() | code |
16137929/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/Admission_Predict_Ver1.1.csv')
df.describe().T
df.columns
df.columns = ['sno', 'GRE', 'TOEFL', 'university_rating', 'SOP', 'LOR', 'CGPA', 'research', 'admit_chance']
plt.tight_layout()
df.research.value_counts()... | code |
16137929/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/Admission_Predict_Ver1.1.csv')
type(df) | code |
16137929/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/Admission_Predict_Ver1.1.csv')
df.describe().T
df.columns
df.columns = ['sno', 'GRE', 'TOEFL', 'university_rating', 'SOP', 'LOR', 'CGPA', 'research', 'admit_chance']
plt.tight_layout()
df.research.value_counts()... | code |
16137929/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/Admission_Predict_Ver1.1.csv')
print('Descriptive Statastics of our Data:')
df.describe().T | code |
16137929/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/Admission_Predict_Ver1.1.csv')
df.describe().T
df.columns
df.columns = ['sno', 'GRE', 'TOEFL', 'university_rating', 'SOP', 'LOR', 'CGPA', 'research', 'admit_chance']
df.research.value_counts() | code |
16137929/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/Admission_Predict_Ver1.1.csv')
df.describe().T
df.columns
df.columns = ['sno', 'GRE', 'TOEFL', 'university_rating', 'SOP', 'LOR', 'CGPA', 'research', 'admit_chance']
plt.tight_layout()
df.research.value_counts()... | code |
16137929/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/Admission_Predict_Ver1.1.csv')
df.describe().T
df.columns
df.columns = ['sno', 'GRE', 'TOEFL', 'university_rating', 'SOP', 'LOR', 'CGPA', 'research', 'admit_chance']
plt.tight_layout()
df.research.value_counts()... | code |
16137929/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/Admission_Predict_Ver1.1.csv')
df.describe().T
df.columns
df.columns = ['sno', 'GRE', 'TOEFL', 'university_rating', 'SOP', 'LOR', 'CGPA', 'research', 'admit_chance']
pd.isnull(df).sum()
plt.tight_layout()
df.re... | code |
16137929/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/Admission_Predict_Ver1.1.csv')
df.describe().T
df.columns
df.columns = ['sno', 'GRE', 'TOEFL', 'university_rating', 'SOP', 'LOR', 'CGPA', 'research', 'admit_chance']
df[['GRE', 'TOEFL', 'university_rating', 'CGPA', 'SOP', 'LOR', 'resea... | code |
16137929/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/Admission_Predict_Ver1.1.csv')
df.describe().T
df.columns
df.columns = ['sno', 'GRE', 'TOEFL', 'university_rating', 'SOP', 'LOR', 'CGPA', 'research', 'admit_chance']
pd.isnull(df).sum()
plt.tight_layout()
df.re... | code |
16137929/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/Admission_Predict_Ver1.1.csv')
df.describe().T
df.columns | code |
16137929/cell_12 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/Admission_Predict_Ver1.1.csv')
df.describe().T
df.columns
df.columns = ['sno', 'GRE', 'TOEFL', 'university_rating', 'SOP', 'LOR', 'CGPA', 'research', 'admit_chance']
pd.isnull(df).sum() | code |
16137929/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/Admission_Predict_Ver1.1.csv')
df.head(10) | code |
2034924/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.tsv', sep='\t', index_col=0)
sum(df.name.isnull())
df.item_condition_id.unique()
df.category_name.unique().shape
sum(df.category_name.isnull()) | code |
2034924/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.tsv', sep='\t', index_col=0)
df_test = pd.read_csv('../input/test.tsv', sep='\t', index_col=0)
df_test.info() | code |
2034924/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.tsv', sep='\t', index_col=0)
sum(df.name.isnull())
df.item_condition_id.unique()
df.category_name.unique().shape
sum(df.category_name.isnull())
df.category_name.fillna('//').str.split('/').apply(lambda x: x[0]).unique().shape
df.category_name.fillna('//').str.... | code |
2034924/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.tsv', sep='\t', index_col=0) | code |
2034924/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.tsv', sep='\t', index_col=0)
sum(df.name.isnull())
df.item_condition_id.unique()
df.category_name.unique().shape
sum(df.category_name.isnull())
df.category_name.fillna('//').str.split('/').apply(lambda x: x[0]).unique().shape
df.category_name.fillna('//').str.... | code |
2034924/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.tsv', sep='\t', index_col=0)
sum(df.name.isnull())
df.item_condition_id.unique()
df.category_name.unique().shape
sum(df.category_name.isnull())
df.category_name.fillna('//').str.split('/').apply(lambda x: x[0]).unique().shape
df.category_name.fillna('//').str.... | code |
2034924/cell_33 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.tsv', sep='\t', index_col=0)
sum(df.name.isnull())
df.item_condition_id.unique()
df.category_name.unique().shape
sum(df.category_name.isnull())
df.category_name.fillna('//').str.split('/').apply(lambda x: x[0]).unique().shape
df.category_name.fillna('//').str.... | code |
2034924/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.tsv', sep='\t', index_col=0)
sum(df.name.isnull())
df.item_condition_id.unique()
df.category_name.unique().shape | code |
2034924/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.tsv', sep='\t', index_col=0)
df.info() | code |
2034924/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.tsv', sep='\t', index_col=0)
sum(df.name.isnull())
df.item_condition_id.unique()
df.category_name.unique().shape
sum(df.category_name.isnull())
df.category_name.fillna('//').str.split('/').apply(lambda x: x[0]).unique().shape
df.category_name.fillna('//').str.... | code |
2034924/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.tsv', sep='\t', index_col=0)
sum(df.name.isnull())
df.item_condition_id.unique()
df.category_name.unique().shape
sum(df.category_name.isnull())
df.category_name.fillna('//').str.split('/').apply(lambda x: x[0]).unique().shape
df.category_name.fillna('//').str.... | code |
2034924/cell_41 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.tsv', sep='\t', index_col=0)
sum(df.name.isnull())
df.item_condition_id.unique()
df.category_name.unique().shape
sum(df.category_name.isnull())
df.category_name.fillna('//').str.split('/').apply(lambda x: x[0]).unique().shape
df.category_na... | code |
2034924/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.tsv', sep='\t', index_col=0)
df.head() | code |
2034924/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.tsv', sep='\t', index_col=0)
sum(df.name.isnull())
df.item_condition_id.unique()
df.category_name.unique().shape
sum(df.category_name.isnull())
df.category_name.fillna('//').str.split('/').apply(lambda x: x[0]).unique().shape
df.category_name.fillna('//').str.... | code |
2034924/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.tsv', sep='\t', index_col=0)
sum(df.name.isnull())
df.item_condition_id.unique() | code |
2034924/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.tsv', sep='\t', index_col=0)
sum(df.name.isnull())
df.item_condition_id.unique()
df.category_name.unique().shape
sum(df.category_name.isnull())
df.category_name.fillna('//').str.split('/').apply(lambda x: x[0]).unique().shape
df.category_name.fillna('//').str.... | code |
2034924/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.tsv', sep='\t', index_col=0)
sum(df.name.isnull())
df.item_condition_id.unique()
df.category_name.unique().shape
sum(df.category_name.isnull())
df.category_name.fillna('//').str.split('/').apply(lambda x: x[0]).unique().shape
df.category_name.fillna('//').str.... | code |
2034924/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.tsv', sep='\t', index_col=0)
sum(df.name.isnull()) | code |
2034924/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.tsv', sep='\t', index_col=0)
sum(df.name.isnull())
df.item_condition_id.unique()
df.category_name.unique().shape
sum(df.category_name.isnull())
df.category_name.fillna('//').str.split('/').apply(lambda x: x[0]).unique().shape | code |
2034924/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.tsv', sep='\t', index_col=0)
df_test = pd.read_csv('../input/test.tsv', sep='\t', index_col=0)
df_test.head() | code |
2034924/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.tsv', sep='\t', index_col=0)
sum(df.name.isnull())
df.item_condition_id.unique()
df.category_name.unique().shape
sum(df.category_name.isnull())
df.category_name.fillna('//').str.split('/').apply(lambda x: x[0]).unique().shape
df.category_name.fillna('//').str.... | code |
2034924/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.tsv', sep='\t', index_col=0)
sum(df.name.isnull())
df.item_condition_id.unique()
df.category_name.unique().shape
sum(df.category_name.isnull())
df.category_name.fillna('//').str.split('/').apply(lambda x: x[0]).unique().shape
df.category_name.fillna('//').str.... | code |
121153285/cell_21 | [
"text_plain_output_1.png"
] | from catboost import CatBoostClassifier
from sklearn.model_selection import train_test_split, cross_val_predict, cross_val_score, StratifiedKFold, RandomizedSearchCV
from sklearn.preprocessing import RobustScaler
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas ... | code |
121153285/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/playground-series-s3e7/train.csv')
data.info() | code |
121153285/cell_23 | [
"text_plain_output_1.png"
] | !pip install lightgbm
from lightgbm import LGBMClassifier | code |
121153285/cell_20 | [
"text_plain_output_1.png"
] | from catboost import CatBoostClassifier
from sklearn.model_selection import train_test_split, cross_val_predict, cross_val_score, StratifiedKFold, RandomizedSearchCV
from sklearn.preprocessing import RobustScaler
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas ... | code |
121153285/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/playground-series-s3e7/train.csv')
corrmat = data.corr()
cols = corrmat.nlarges... | code |
121153285/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 |
121153285/cell_7 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/playground-series-s3e7/train.csv')
corrmat = data.corr()
cols = corrmat.nlarges... | code |
121153285/cell_18 | [
"text_plain_output_1.png"
] | !pip install catboost
from catboost import CatBoostClassifier | code |
121153285/cell_15 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split, cross_val_predict, cross_val_score, StratifiedKFold, RandomizedSearchCV
from sklearn.preprocessing import RobustScaler
from xgboost import XGBRegressor, XGBClassifier
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import... | code |
121153285/cell_16 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split, cross_val_predict, cross_val_score, StratifiedKFold, RandomizedSearchCV
from sklearn.preprocessing import RobustScaler
from xgboost import XGBRegressor, XGBClassifier
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import... | code |
121153285/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error, roc_auc_score
from sklearn.model_selection import train_test_split, cross_val_predict, cross_val_score, StratifiedKFold, RandomizedSearchCV
from sklearn.preprocessing import RobustScaler
from xgboost import XGBRegressor, XGBClassifier
import matplotlib.pyplot as plt
... | code |
121153285/cell_24 | [
"text_plain_output_1.png"
] | from catboost import CatBoostClassifier
from lightgbm import LGBMClassifier
from sklearn.model_selection import train_test_split, cross_val_predict, cross_val_score, StratifiedKFold, RandomizedSearchCV
from sklearn.preprocessing import RobustScaler
import matplotlib.pyplot as plt
import numpy as np
import numpy a... | code |
121153285/cell_22 | [
"text_plain_output_1.png"
] | from catboost import CatBoostClassifier
from sklearn.metrics import mean_absolute_error, roc_auc_score
from sklearn.model_selection import train_test_split, cross_val_predict, cross_val_score, StratifiedKFold, RandomizedSearchCV
from sklearn.preprocessing import RobustScaler
from xgboost import XGBRegressor, XGBCla... | code |
121153285/cell_27 | [
"text_plain_output_1.png"
] | from catboost import CatBoostClassifier
from sklearn.metrics import mean_absolute_error, roc_auc_score
from sklearn.model_selection import train_test_split, cross_val_predict, cross_val_score, StratifiedKFold, RandomizedSearchCV
from sklearn.preprocessing import RobustScaler
from xgboost import XGBRegressor, XGBCla... | code |
121153285/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split, cross_val_predict, cross_val_score, StratifiedKFold, RandomizedSearchCV
from sklearn.preprocessing import RobustScaler
from xgboost import XGBRegressor, XGBClassifier
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import... | code |
34146326/cell_42 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score,confusion_matrix
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
data = pd.read_csv('../input/amazon-alexa-reviews/amazon_alexa.tsv', sep='\t')... | code |
34146326/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/amazon-alexa-reviews/amazon_alexa.tsv', sep='\t')
data.drop(columns=['date'], inplace=True)
data.head() | code |
34146326/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
data = pd.read_csv('../input/amazon-alexa-reviews/amazon_alexa.tsv', sep='\t')
data.drop(columns=['date'], inplace=True)
sns.countplot(x='feedback', data=data) | code |
34146326/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/amazon-alexa-reviews/amazon_alexa.tsv', sep='\t')
data.drop(columns=['date'], inplace=True)
data.describe() | code |
34146326/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | (x_train.shape, y_train.shape) | code |
34146326/cell_4 | [
"text_plain_output_1.png"
] | import os
import os
os.listdir('../input/amazon-alexa-reviews') | code |
34146326/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/amazon-alexa-reviews/amazon_alexa.tsv', sep='\t')
data.drop(columns=['date'], inplace=True)
x = data[['rating', 'variation', 'verified_reviews']].copy()
y = data.feedback
x.head() | code |
34146326/cell_33 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score,confusion_matrix
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
data = pd.read_csv('../input/amazon-alexa-reviews/amazon_alexa.tsv', sep='\t')... | code |
34146326/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/amazon-alexa-reviews/amazon_alexa.tsv', sep='\t')
data.head() | code |
34146326/cell_41 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score,confusion_matrix
transformer = ct(transformers=[('review_counts', cv(), 'verified_reviews'), ('variation_dummies', ohe(), ['variation'])], remainder='passthrough')
pipe = mp(transformer, dtc(random_state=42))
pipe
(x_train.shape, y_train.shape)
pipe.fit(x_train, y_train)
... | code |
34146326/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
data = pd.read_csv('../input/amazon-alexa-reviews/amazon_alexa.tsv', sep='\t')
data.drop(columns=['date'], inplace=True)
sns.countplot(x='rating', data=data, hue='feedbac... | code |
34146326/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/amazon-alexa-reviews/amazon_alexa.tsv', sep='\t')
data.drop(columns=['date'], inplace=True)
data.head() | code |
34146326/cell_18 | [
"text_html_output_1.png"
] | transformer = ct(transformers=[('review_counts', cv(), 'verified_reviews'), ('variation_dummies', ohe(), ['variation'])], remainder='passthrough')
pipe = mp(transformer, dtc(random_state=42))
pipe | code |
34146326/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score,confusion_matrix
transformer = ct(transformers=[('review_counts', cv(), 'verified_reviews'), ('variation_dummies', ohe(), ['variation'])], remainder='passthrough')
pipe = mp(transformer, dtc(random_state=42))
pipe
(x_train.shape, y_train.shape)
pipe.fit(x_train, y_train)
... | code |
34146326/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/amazon-alexa-reviews/amazon_alexa.tsv', sep='\t')
data.drop(columns=['date'], inplace=True)
data.info() | code |
34146326/cell_38 | [
"text_html_output_1.png"
] | transformer = ct(transformers=[('review_counts', cv(), 'verified_reviews'), ('variation_dummies', ohe(), ['variation'])], remainder='passthrough')
pipe = mp(transformer, dtc(random_state=42))
pipe
(x_train.shape, y_train.shape)
pipe.fit(x_train, y_train)
pred = pipe.predict(x_test)
pipe = mp(transformer, rfc(n_est... | code |
34146326/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
data = pd.read_csv('../input/amazon-alexa-reviews/amazon_alexa.tsv', sep='\t')
data.drop(columns=['date'], inplace=True)
plt.figure(figsi... | code |
34146326/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | transformer = ct(transformers=[('review_counts', cv(), 'verified_reviews'), ('variation_dummies', ohe(), ['variation'])], remainder='passthrough')
pipe = mp(transformer, dtc(random_state=42))
pipe
(x_train.shape, y_train.shape)
pipe.fit(x_train, y_train) | code |
34146326/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
data = pd.read_csv('../input/amazon-alexa-reviews/amazon_alexa.tsv', sep='\t')
data.drop(columns=['date'], inplace=True)
sns.distplot(data['rating']) | code |
50224568/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
plt.scatter(dftrain['299'], dftrain['1'])
plt.title('My PCA graph')
plt.xlabel('0 -{0}%'.format(dftrain['299']))
plt.ylabel('target -{0}%'.format(d... | code |
50224568/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y = sns.regplot(x='1', y='target', data=dftrain) | code |
50224568/cell_8 | [
"text_html_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y=sns.regplot(x='1',y='target',data=dftrain)
detailed_fea... | code |
50224568/cell_17 | [
"text_html_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
import tensorflow as tf
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
def getSlope(df):
return abs(df['slope'])
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-i... | code |
50224568/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
import tensorflow as tf
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
def getSlope(df):
return abs(df['slope'])
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-i... | code |
50224568/cell_5 | [
"image_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
dftrain | code |
105197156/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_feather('../input/amex-train-dropped-1/train_dropped.feather')
typeTable = pd.concat([train.filter(regex='D_').dtypes, train.filter(regex='S_').dtypes], axis=1)
all_cols = train.columns.to_list()
cat_cols = ['B_30', 'B_38', 'D_114', 'D_116', 'D_117', 'D_120', 'D_126', 'D_63', 'D_6... | code |
105197156/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_feather('../input/amex-train-dropped-1/train_dropped.feather')
typeTable = pd.concat([train.filter(regex='D_').dtypes, train.filter(regex='S_').dtypes], axis=1)
with pd.option_context('display.max_rows', 1000):
print('Type of D:')
print(train.filter(regex='D_').dtypes)
prin... | code |
105197156/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_feather('../input/amex-train-dropped-1/train_dropped.feather')
typeTable = pd.concat([train.filter(regex='D_').dtypes, train.filter(regex='S_').dtypes], axis=1)
all_cols = train.columns.to_list()
cat_cols = ['B_30', 'B_38', 'D_114', 'D_116', 'D_117', 'D_120', 'D_126', 'D_63', 'D_6... | code |
105197156/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_feather('../input/amex-train-dropped-1/train_dropped.feather')
typeTable = pd.concat([train.filter(regex='D_').dtypes, train.filter(regex='S_').dtypes], axis=1)
all_cols = train.columns.to_list()
cat_cols = ['B_30', 'B_38', 'D_114', 'D_116', 'D_117', 'D_120', 'D_126', 'D_63', 'D_6... | code |
105197156/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_feather('../input/amex-train-dropped-1/train_dropped.feather')
train.info() | code |
105197156/cell_14 | [
"text_plain_output_1.png"
] | """
for col in cat_cols:
arr = np.array(train[col].unique())
arr.sort()
print (arr)
label = []
for val in range(int(arr[0]), int(arr[len(arr)-1])+1):
label.append(col + ' ' + str(int(val)))
print(label)
""" | code |
105197156/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_feather('../input/amex-train-dropped-1/train_dropped.feather')
typeTable = pd.concat([train.filter(regex='D_').dtypes, train.filter(regex='S_').dtypes], axis=1)
all_cols = train.columns.to_list()
cat_cols = ['B_30', 'B_38', 'D_114', 'D_116', 'D_117', 'D_120', 'D_126', 'D_63', 'D_6... | code |
105197156/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_feather('../input/amex-train-dropped-1/train_dropped.feather')
typeTable = pd.concat([train.filter(regex='D_').dtypes, train.filter(regex='S_').dtypes], axis=1)
with pd.option_context('display.max_rows', 1000):
print('Count of NaN of D: \n' + str(train.filter(regex='D_').isna(... | code |
104124784/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
df = pd.concat([train, test], axis=0).reset_index(drop=True)
df = df.drop(['PassengerId', 'Survived', 'Name', 'Ticket', 'Cabin'], axis=1)
df.isnull().sum().sort_values(ascending=False) * 100 / df.sh... | code |
104124784/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
sns.set()
import matplotlib.pyplot as plt
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selec... | code |
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