path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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stringclasses 1
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|---|---|---|---|
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 = train_df.select_dtypes('number')
cat_data = train_df.select_dtypes('object')
num_data.head() | 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_unique_sum(cat_list):
pass
cat_lists = list(train.select_dtypes('object').columns)
get_unique_sum(cat_lists)
X = train.drop(['id', 'target'], axis=1)
y = train['target']
test_df = test.drop('id', axis=1)
train.head() | 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-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_unique_sum(cat_list):
pass
cat_lists = list(train.select_dtypes('object').columns)
get_unique_sum(cat_lists)
X = train.drop(['id', 'target'], axis=1)
y = train['target']
test_df = test.drop('id', axis=1)
ct = make_column_transformer((OrdinalEncoder(), cat_lists), (StandardScaler(), ['cont1', 'cont2', 'cont3', 'cont4', 'cont5', 'cont7', 'cont9', 'cont10', 'cont11', 'cont12', 'cont13']), (RobustScaler(), ['cont0', 'cont6', 'cont8']), remainder='passthrough')
X_train = pd.DataFrame(ct.fit_transform(X))
test = pd.DataFrame(ct.fit_transform(test_df))
test.head() | 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_unique_sum(cat_list):
for i in cat_list:
print(train[i].unique())
cat_lists = list(train.select_dtypes('object').columns)
get_unique_sum(cat_lists) | 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-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_unique_sum(cat_list):
pass
cat_lists = list(train.select_dtypes('object').columns)
get_unique_sum(cat_lists)
X = train.drop(['id', 'target'], axis=1)
y = train['target']
test_df = test.drop('id', axis=1)
ct = make_column_transformer((OrdinalEncoder(), cat_lists), (StandardScaler(), ['cont1', 'cont2', 'cont3', 'cont4', 'cont5', 'cont7', 'cont9', 'cont10', 'cont11', 'cont12', 'cont13']), (RobustScaler(), ['cont0', 'cont6', 'cont8']), remainder='passthrough')
X_train = pd.DataFrame(ct.fit_transform(X))
test = pd.DataFrame(ct.fit_transform(test_df))
print(X_train.shape)
print(test.shape) | 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()
chances = df.groupby('research')['admit_chance'].median()
df.university_rating.value_counts()
sns.scatterplot(x='CGPA', y='university_rating', hue='research', data=df)
plt.show() | 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.research.value_counts()
chances = df.groupby('research')['admit_chance'].median()
df.university_rating.value_counts()
pd.DataFrame(df.groupby('university_rating')['GRE'].mean())
pd.DataFrame(df.groupby('university_rating')['TOEFL'].mean())
df.groupby('university_rating')[['SOP', 'LOR', 'CGPA']].mean()
sns.regplot(x='CGPA', y='admit_chance', data=df, line_kws={'color': 'red'})
plt.title('CGPA vs Chance of Admit')
plt.show() | 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.research.value_counts()
chances = df.groupby('research')['admit_chance'].median()
df.university_rating.value_counts()
pd.DataFrame(df.groupby('university_rating')['GRE'].mean())
print('Avg. TOEFL scores based on University Ratings')
pd.DataFrame(df.groupby('university_rating')['TOEFL'].mean()) | 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()
chances = df.groupby('research')['admit_chance'].median()
df.university_rating.value_counts()
sns.countplot(df.university_rating) | 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.research.value_counts()
chances = df.groupby('research')['admit_chance'].median()
df.university_rating.value_counts()
pd.DataFrame(df.groupby('university_rating')['GRE'].mean())
pd.DataFrame(df.groupby('university_rating')['TOEFL'].mean())
df.groupby('university_rating')[['SOP', 'LOR', 'CGPA']].mean()
admt_sort = df.sort_values(by=df.columns[-1], ascending=False)
admt_sort[admt_sort['admit_chance'] > 0.8].mean().reset_index().T | 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()
chances = df.groupby('research')['admit_chance'].median()
df.university_rating.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()
chances = df.groupby('research')['admit_chance'].median()
sns.regplot(x='GRE', y='TOEFL', data=df, line_kws={'color': 'red'})
plt.title('GRE Score vs TOEFL Score')
plt.show() | 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()
chances = df.groupby('research')['admit_chance'].median()
print(chances)
sns.factorplot('research', 'admit_chance', data=df)
plt.show() | 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()
chances = df.groupby('research')['admit_chance'].median()
sns.regplot(x='GRE', y='CGPA', data=df, line_kws={'color': 'red'})
plt.title('GRE Score vs CGPA')
plt.show() | 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.research.value_counts()
chances = df.groupby('research')['admit_chance'].median()
df.university_rating.value_counts()
pd.DataFrame(df.groupby('university_rating')['GRE'].mean())
pd.DataFrame(df.groupby('university_rating')['TOEFL'].mean())
df.groupby('university_rating')[['SOP', 'LOR', 'CGPA']].mean() | 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', 'research']].hist(figsize=(10, 8), bins=15, linewidth='1', edgecolor='black')
plt.tight_layout()
plt.show() | 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.research.value_counts()
chances = df.groupby('research')['admit_chance'].median()
df.university_rating.value_counts()
print('Avg. GRE scores based on University Ratings')
pd.DataFrame(df.groupby('university_rating')['GRE'].mean()) | 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.split('/').apply(lambda x: x[1]).unique().shape
df.category_name.fillna('//').str.split('/').apply(lambda x: x[2]).unique().shape
df.groupby('category_name').agg({'price': 'mean'}).sort_values('price', ascending=False).head(10) | 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.split('/').apply(lambda x: x[1]).unique().shape | 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.split('/').apply(lambda x: x[1]).unique().shape
df.category_name.fillna('//').str.split('/').apply(lambda x: x[2]).unique().shape
df.groupby('category_name').agg({'price': 'mean'}).sort_values('price', ascending=False).head(10)
sum(df.brand_name.isnull())
df.brand_name.unique().shape
df.groupby('brand_name').agg({'price': 'mean'}).sort_values('price', ascending=False).head(10) | 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.split('/').apply(lambda x: x[1]).unique().shape
df.category_name.fillna('//').str.split('/').apply(lambda x: x[2]).unique().shape
df.groupby('category_name').agg({'price': 'mean'}).sort_values('price', ascending=False).head(10)
sum(df.brand_name.isnull())
df.brand_name.unique().shape
df.groupby('brand_name').agg({'price': 'mean'}).sort_values('price', ascending=False).head(10)
sum(df.price.isnull()) | 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.split('/').apply(lambda x: x[1]).unique().shape
df.category_name.fillna('//').str.split('/').apply(lambda x: x[2]).unique().shape
df.groupby('category_name').agg({'price': 'mean'}).sort_values('price', ascending=False).head(10)
sum(df.brand_name.isnull())
df.brand_name.unique().shape | 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.split('/').apply(lambda x: x[1]).unique().shape
df.category_name.fillna('//').str.split('/').apply(lambda x: x[2]).unique().shape
df.groupby('category_name').agg({'price': 'mean'}).sort_values('price', ascending=False).head(10)
sum(df.brand_name.isnull())
df.brand_name.unique().shape
df.groupby('brand_name').agg({'price': 'mean'}).sort_values('price', ascending=False).head(10)
sum(df.price.isnull())
df.price.quantile(0.9)
df.price.quantile(0.99)
sum(df.price == 0) | 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_name.fillna('//').str.split('/').apply(lambda x: x[1]).unique().shape
df.category_name.fillna('//').str.split('/').apply(lambda x: x[2]).unique().shape
df.groupby('category_name').agg({'price': 'mean'}).sort_values('price', ascending=False).head(10)
sum(df.brand_name.isnull())
df.brand_name.unique().shape
df.groupby('brand_name').agg({'price': 'mean'}).sort_values('price', ascending=False).head(10)
sum(df.price.isnull())
df.price.quantile(0.9)
df.price.quantile(0.99)
sum(df.price == 0)
plot = np.log10(df.price + 1).hist(bins=20, log=True)
plot.set_ylabel('Count')
plot.set_xlabel('log10(price)') | 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.split('/').apply(lambda x: x[1]).unique().shape
df.category_name.fillna('//').str.split('/').apply(lambda x: x[2]).unique().shape
df.groupby('category_name').agg({'price': 'mean'}).sort_values('price', ascending=False).head(10)
sum(df.brand_name.isnull()) | 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.split('/').apply(lambda x: x[1]).unique().shape
df.category_name.fillna('//').str.split('/').apply(lambda x: x[2]).unique().shape
df.groupby('category_name').agg({'price': 'mean'}).sort_values('price', ascending=False).head(10)
sum(df.brand_name.isnull())
df.brand_name.unique().shape
df.groupby('brand_name').agg({'price': 'mean'}).sort_values('price', ascending=False).head(10)
sum(df.price.isnull())
df.price.describe() | 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.split('/').apply(lambda x: x[1]).unique().shape
df.category_name.fillna('//').str.split('/').apply(lambda x: x[2]).unique().shape | 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.split('/').apply(lambda x: x[1]).unique().shape
df.category_name.fillna('//').str.split('/').apply(lambda x: x[2]).unique().shape
df.groupby('category_name').agg({'price': 'mean'}).sort_values('price', ascending=False).head(10)
sum(df.brand_name.isnull())
df.brand_name.unique().shape
df.groupby('brand_name').agg({'price': 'mean'}).sort_values('price', ascending=False).head(10)
sum(df.price.isnull())
df.price.quantile(0.9)
df.price.quantile(0.99) | 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.split('/').apply(lambda x: x[1]).unique().shape
df.category_name.fillna('//').str.split('/').apply(lambda x: x[2]).unique().shape
df.groupby('category_name').agg({'price': 'mean'}).sort_values('price', ascending=False).head(10)
sum(df.brand_name.isnull())
df.brand_name.unique().shape
df.groupby('brand_name').agg({'price': 'mean'}).sort_values('price', ascending=False).head(10)
sum(df.price.isnull())
df.price.quantile(0.9) | 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 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.nlargest(20, 'booking_status')['booking_status'].index
cm = np.corrcoef(data[cols].values.T)
fig = plt.gcf()
fig.set_size_inches(10, 8)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
g = sns.PairGrid(data, y_vars=['booking_status'], x_vars=data.columns)
g = g.map_diag(plt.hist)
g = g.map_offdiag(sns.scatterplot)
y = data.booking_status
X = data.drop(['booking_status', 'id'], axis=1)
X_train, X_val, y_train, y_val = train_test_split(X, y, train_size=0.8, test_size=0.2)
scaler = RobustScaler()
X = pd.DataFrame(scaler.fit_transform(X))
X_train = pd.DataFrame(scaler.fit_transform(X_train))
X_val = pd.DataFrame(scaler.fit_transform(X_val))
param_grid = {'n_estimators': [1750, 2000, 2250, 2500], 'learning_rate': [0.05, 0.065, 0.08, 0.9], 'max_depth': [3, 4, 5, 6, 8, 10, 12, 15], 'min_child_weight': [3, 4, 5, 6, 7], 'gamma': [0.2, 0.3, 0.4, 0.5, 0.6], 'colsample_bytree': [0.4, 0.5, 0.6, 0.65, 0.7]}
param_grid = {'n_estimators': [2000], 'learning_rate': [0.15], 'max_depth': [8], 'l2_leaf_reg': [5], 'subsample': [0.5], 'colsample_bylevel': [0.8], 'bagging_temperature': [0.0], 'grow_policy': ['SymmetricTree', 'Depthwise', 'Lossguide']}
RS_CB = RandomizedSearchCV(estimator=CatBoostClassifier(early_stopping_rounds=10), param_distributions=param_grid, n_iter=3, cv=10, n_jobs=-1, random_state=0)
RS_CB.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_val, y_val)], verbose=0)
RS_CB.best_params_ | 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 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.nlargest(20, 'booking_status')['booking_status'].index
cm = np.corrcoef(data[cols].values.T)
fig = plt.gcf()
fig.set_size_inches(10, 8)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
g = sns.PairGrid(data, y_vars=['booking_status'], x_vars=data.columns)
g = g.map_diag(plt.hist)
g = g.map_offdiag(sns.scatterplot)
y = data.booking_status
X = data.drop(['booking_status', 'id'], axis=1)
X_train, X_val, y_train, y_val = train_test_split(X, y, train_size=0.8, test_size=0.2)
scaler = RobustScaler()
X = pd.DataFrame(scaler.fit_transform(X))
X_train = pd.DataFrame(scaler.fit_transform(X_train))
X_val = pd.DataFrame(scaler.fit_transform(X_val))
param_grid = {'n_estimators': [1750, 2000, 2250, 2500], 'learning_rate': [0.05, 0.065, 0.08, 0.9], 'max_depth': [3, 4, 5, 6, 8, 10, 12, 15], 'min_child_weight': [3, 4, 5, 6, 7], 'gamma': [0.2, 0.3, 0.4, 0.5, 0.6], 'colsample_bytree': [0.4, 0.5, 0.6, 0.65, 0.7]}
param_grid = {'n_estimators': [2000], 'learning_rate': [0.15], 'max_depth': [8], 'l2_leaf_reg': [5], 'subsample': [0.5], 'colsample_bylevel': [0.8], 'bagging_temperature': [0.0], 'grow_policy': ['SymmetricTree', 'Depthwise', 'Lossguide']}
RS_CB = RandomizedSearchCV(estimator=CatBoostClassifier(early_stopping_rounds=10), param_distributions=param_grid, n_iter=3, cv=10, n_jobs=-1, random_state=0)
RS_CB.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_val, y_val)], verbose=0) | 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.nlargest(20, 'booking_status')['booking_status'].index
cm = np.corrcoef(data[cols].values.T)
fig = plt.gcf()
fig.set_size_inches(10, 8)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values) | 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.nlargest(20, 'booking_status')['booking_status'].index
cm = np.corrcoef(data[cols].values.T)
fig = plt.gcf()
fig.set_size_inches(10, 8)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
g = sns.PairGrid(data, y_vars=['booking_status'], x_vars=data.columns)
g = g.map_diag(plt.hist)
g = g.map_offdiag(sns.scatterplot)
plt.show() | 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 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.nlargest(20, 'booking_status')['booking_status'].index
cm = np.corrcoef(data[cols].values.T)
fig = plt.gcf()
fig.set_size_inches(10, 8)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
g = sns.PairGrid(data, y_vars=['booking_status'], x_vars=data.columns)
g = g.map_diag(plt.hist)
g = g.map_offdiag(sns.scatterplot)
y = data.booking_status
X = data.drop(['booking_status', 'id'], axis=1)
X_train, X_val, y_train, y_val = train_test_split(X, y, train_size=0.8, test_size=0.2)
scaler = RobustScaler()
X = pd.DataFrame(scaler.fit_transform(X))
X_train = pd.DataFrame(scaler.fit_transform(X_train))
X_val = pd.DataFrame(scaler.fit_transform(X_val))
param_grid = {'n_estimators': [1750, 2000, 2250, 2500], 'learning_rate': [0.05, 0.065, 0.08, 0.9], 'max_depth': [3, 4, 5, 6, 8, 10, 12, 15], 'min_child_weight': [3, 4, 5, 6, 7], 'gamma': [0.2, 0.3, 0.4, 0.5, 0.6], 'colsample_bytree': [0.4, 0.5, 0.6, 0.65, 0.7]}
RS_XGB = RandomizedSearchCV(estimator=XGBClassifier(early_stopping_rounds=30), param_distributions=param_grid, n_iter=3, cv=6, n_jobs=-1, random_state=0)
RS_XGB.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_val, y_val)], verbose=0) | 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 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.nlargest(20, 'booking_status')['booking_status'].index
cm = np.corrcoef(data[cols].values.T)
fig = plt.gcf()
fig.set_size_inches(10, 8)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
g = sns.PairGrid(data, y_vars=['booking_status'], x_vars=data.columns)
g = g.map_diag(plt.hist)
g = g.map_offdiag(sns.scatterplot)
y = data.booking_status
X = data.drop(['booking_status', 'id'], axis=1)
X_train, X_val, y_train, y_val = train_test_split(X, y, train_size=0.8, test_size=0.2)
scaler = RobustScaler()
X = pd.DataFrame(scaler.fit_transform(X))
X_train = pd.DataFrame(scaler.fit_transform(X_train))
X_val = pd.DataFrame(scaler.fit_transform(X_val))
param_grid = {'n_estimators': [1750, 2000, 2250, 2500], 'learning_rate': [0.05, 0.065, 0.08, 0.9], 'max_depth': [3, 4, 5, 6, 8, 10, 12, 15], 'min_child_weight': [3, 4, 5, 6, 7], 'gamma': [0.2, 0.3, 0.4, 0.5, 0.6], 'colsample_bytree': [0.4, 0.5, 0.6, 0.65, 0.7]}
RS_XGB = RandomizedSearchCV(estimator=XGBClassifier(early_stopping_rounds=30), param_distributions=param_grid, n_iter=3, cv=6, n_jobs=-1, random_state=0)
RS_XGB.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_val, y_val)], verbose=0)
RS_XGB.best_params_ | 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
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.nlargest(20, 'booking_status')['booking_status'].index
cm = np.corrcoef(data[cols].values.T)
fig = plt.gcf()
fig.set_size_inches(10, 8)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
g = sns.PairGrid(data, y_vars=['booking_status'], x_vars=data.columns)
g = g.map_diag(plt.hist)
g = g.map_offdiag(sns.scatterplot)
y = data.booking_status
X = data.drop(['booking_status', 'id'], axis=1)
X_train, X_val, y_train, y_val = train_test_split(X, y, train_size=0.8, test_size=0.2)
scaler = RobustScaler()
X = pd.DataFrame(scaler.fit_transform(X))
X_train = pd.DataFrame(scaler.fit_transform(X_train))
X_val = pd.DataFrame(scaler.fit_transform(X_val))
param_grid = {'n_estimators': [1750, 2000, 2250, 2500], 'learning_rate': [0.05, 0.065, 0.08, 0.9], 'max_depth': [3, 4, 5, 6, 8, 10, 12, 15], 'min_child_weight': [3, 4, 5, 6, 7], 'gamma': [0.2, 0.3, 0.4, 0.5, 0.6], 'colsample_bytree': [0.4, 0.5, 0.6, 0.65, 0.7]}
RS_XGB = RandomizedSearchCV(estimator=XGBClassifier(early_stopping_rounds=30), param_distributions=param_grid, n_iter=3, cv=6, n_jobs=-1, random_state=0)
RS_XGB.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_val, y_val)], verbose=0)
RS_XGB.best_params_
xgb_models = []
scores = []
feature_importances = []
skf = StratifiedKFold(n_splits=30, shuffle=True, random_state=0)
for fold, (train_idx, val_idx) in enumerate(skf.split(X, y)):
X_train, y_train = (X.iloc[train_idx], y.iloc[train_idx])
X_valid, y_valid = (X.iloc[val_idx], y.iloc[val_idx])
evaluation = [(X_train, y_train), (X_valid, y_valid)]
model = XGBClassifier(**RS_XGB.best_params_, n_jobs=-1, early_stopping_rounds=300)
model.fit(X_train, y_train, eval_set=evaluation, verbose=0)
val_preds = model.predict_proba(X_valid)[:, 1]
val_score = roc_auc_score(y_valid, val_preds)
best_iter = model.best_iteration
feature_importances.append({i: j for i in model.feature_names_in_ for j in model.feature_importances_})
print(f' auc :{val_score:.5f} best iteration :{best_iter}')
if val_score > 0.9:
scores.append(val_score)
xgb_models.append(model)
mean_val_auc = np.array(scores).mean(0)
print(f'Mean AUC: {mean_val_auc}') | 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 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.nlargest(20, 'booking_status')['booking_status'].index
cm = np.corrcoef(data[cols].values.T)
fig = plt.gcf()
fig.set_size_inches(10, 8)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
g = sns.PairGrid(data, y_vars=['booking_status'], x_vars=data.columns)
g = g.map_diag(plt.hist)
g = g.map_offdiag(sns.scatterplot)
y = data.booking_status
X = data.drop(['booking_status', 'id'], axis=1)
X_train, X_val, y_train, y_val = train_test_split(X, y, train_size=0.8, test_size=0.2)
scaler = RobustScaler()
X = pd.DataFrame(scaler.fit_transform(X))
X_train = pd.DataFrame(scaler.fit_transform(X_train))
X_val = pd.DataFrame(scaler.fit_transform(X_val))
param_grid = {'n_estimators': [1750, 2000, 2250, 2500], 'learning_rate': [0.05, 0.065, 0.08, 0.9], 'max_depth': [3, 4, 5, 6, 8, 10, 12, 15], 'min_child_weight': [3, 4, 5, 6, 7], 'gamma': [0.2, 0.3, 0.4, 0.5, 0.6], 'colsample_bytree': [0.4, 0.5, 0.6, 0.65, 0.7]}
param_grid = {'n_estimators': [2000], 'learning_rate': [0.15], 'max_depth': [8], 'l2_leaf_reg': [5], 'subsample': [0.5], 'colsample_bylevel': [0.8], 'bagging_temperature': [0.0], 'grow_policy': ['SymmetricTree', 'Depthwise', 'Lossguide']}
RS_CB = RandomizedSearchCV(estimator=CatBoostClassifier(early_stopping_rounds=10), param_distributions=param_grid, n_iter=3, cv=10, n_jobs=-1, random_state=0)
RS_CB.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_val, y_val)], verbose=0)
RS_CB.best_params_
RS_CB = RandomizedSearchCV(estimator=LGBMClassifier(early_stopping_rounds=300), param_distributions=param_grid, n_iter=3, cv=30, n_jobs=-1, random_state=0)
RS_CB.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_val, y_val)], verbose=0) | 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, XGBClassifier
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.nlargest(20, 'booking_status')['booking_status'].index
cm = np.corrcoef(data[cols].values.T)
fig = plt.gcf()
fig.set_size_inches(10, 8)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
g = sns.PairGrid(data, y_vars=['booking_status'], x_vars=data.columns)
g = g.map_diag(plt.hist)
g = g.map_offdiag(sns.scatterplot)
y = data.booking_status
X = data.drop(['booking_status', 'id'], axis=1)
X_train, X_val, y_train, y_val = train_test_split(X, y, train_size=0.8, test_size=0.2)
scaler = RobustScaler()
X = pd.DataFrame(scaler.fit_transform(X))
X_train = pd.DataFrame(scaler.fit_transform(X_train))
X_val = pd.DataFrame(scaler.fit_transform(X_val))
param_grid = {'n_estimators': [1750, 2000, 2250, 2500], 'learning_rate': [0.05, 0.065, 0.08, 0.9], 'max_depth': [3, 4, 5, 6, 8, 10, 12, 15], 'min_child_weight': [3, 4, 5, 6, 7], 'gamma': [0.2, 0.3, 0.4, 0.5, 0.6], 'colsample_bytree': [0.4, 0.5, 0.6, 0.65, 0.7]}
RS_XGB = RandomizedSearchCV(estimator=XGBClassifier(early_stopping_rounds=30), param_distributions=param_grid, n_iter=3, cv=6, n_jobs=-1, random_state=0)
RS_XGB.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_val, y_val)], verbose=0)
RS_XGB.best_params_
xgb_models = []
scores = []
feature_importances = []
skf = StratifiedKFold(n_splits=30, shuffle=True, random_state=0)
for fold, (train_idx, val_idx) in enumerate(skf.split(X, y)):
X_train, y_train = (X.iloc[train_idx], y.iloc[train_idx])
X_valid, y_valid = (X.iloc[val_idx], y.iloc[val_idx])
evaluation = [(X_train, y_train), (X_valid, y_valid)]
model = XGBClassifier(**RS_XGB.best_params_, n_jobs=-1, early_stopping_rounds=300)
model.fit(X_train, y_train, eval_set=evaluation, verbose=0)
val_preds = model.predict_proba(X_valid)[:, 1]
val_score = roc_auc_score(y_valid, val_preds)
best_iter = model.best_iteration
feature_importances.append({i: j for i in model.feature_names_in_ for j in model.feature_importances_})
if val_score > 0.9:
scores.append(val_score)
xgb_models.append(model)
mean_val_auc = np.array(scores).mean(0)
catboost_models = []
scores = []
feature_importances = []
skf = StratifiedKFold(n_splits=30, shuffle=True, random_state=0)
for fold, (train_idx, val_idx) in enumerate(skf.split(X, y)):
X_train, y_train = (X.iloc[train_idx], y.iloc[train_idx])
X_valid, y_valid = (X.iloc[val_idx], y.iloc[val_idx])
evaluation = [(X_train, y_train), (X_valid, y_valid)]
model = CatBoostClassifier(subsample=0.5, n_estimators=2000, max_depth=8, learning_rate=0.15, l2_leaf_reg=5, grow_policy='Lossguide', colsample_bylevel=0.8, bagging_temperature=0.0, early_stopping_rounds=300)
model.fit(X_train, y_train, eval_set=evaluation, verbose=0)
val_preds = model.predict_proba(X_valid)[:, 1]
val_score = roc_auc_score(y_valid, val_preds)
print(f' auc :{val_score:.5f}')
if val_score > 0.9:
scores.append(val_score)
catboost_models.append(model)
break
mean_val_auc = np.array(scores).mean(0)
print(f'Mean AUC: {mean_val_auc}') | 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, XGBClassifier
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.nlargest(20, 'booking_status')['booking_status'].index
cm = np.corrcoef(data[cols].values.T)
fig = plt.gcf()
fig.set_size_inches(10, 8)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
g = sns.PairGrid(data, y_vars=['booking_status'], x_vars=data.columns)
g = g.map_diag(plt.hist)
g = g.map_offdiag(sns.scatterplot)
y = data.booking_status
X = data.drop(['booking_status', 'id'], axis=1)
X_train, X_val, y_train, y_val = train_test_split(X, y, train_size=0.8, test_size=0.2)
scaler = RobustScaler()
X = pd.DataFrame(scaler.fit_transform(X))
X_train = pd.DataFrame(scaler.fit_transform(X_train))
X_val = pd.DataFrame(scaler.fit_transform(X_val))
param_grid = {'n_estimators': [1750, 2000, 2250, 2500], 'learning_rate': [0.05, 0.065, 0.08, 0.9], 'max_depth': [3, 4, 5, 6, 8, 10, 12, 15], 'min_child_weight': [3, 4, 5, 6, 7], 'gamma': [0.2, 0.3, 0.4, 0.5, 0.6], 'colsample_bytree': [0.4, 0.5, 0.6, 0.65, 0.7]}
RS_XGB = RandomizedSearchCV(estimator=XGBClassifier(early_stopping_rounds=30), param_distributions=param_grid, n_iter=3, cv=6, n_jobs=-1, random_state=0)
RS_XGB.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_val, y_val)], verbose=0)
RS_XGB.best_params_
xgb_models = []
scores = []
feature_importances = []
skf = StratifiedKFold(n_splits=30, shuffle=True, random_state=0)
for fold, (train_idx, val_idx) in enumerate(skf.split(X, y)):
X_train, y_train = (X.iloc[train_idx], y.iloc[train_idx])
X_valid, y_valid = (X.iloc[val_idx], y.iloc[val_idx])
evaluation = [(X_train, y_train), (X_valid, y_valid)]
model = XGBClassifier(**RS_XGB.best_params_, n_jobs=-1, early_stopping_rounds=300)
model.fit(X_train, y_train, eval_set=evaluation, verbose=0)
val_preds = model.predict_proba(X_valid)[:, 1]
val_score = roc_auc_score(y_valid, val_preds)
best_iter = model.best_iteration
feature_importances.append({i: j for i in model.feature_names_in_ for j in model.feature_importances_})
if val_score > 0.9:
scores.append(val_score)
xgb_models.append(model)
mean_val_auc = np.array(scores).mean(0)
catboost_models = []
scores = []
feature_importances = []
skf = StratifiedKFold(n_splits=30, shuffle=True, random_state=0)
for fold, (train_idx, val_idx) in enumerate(skf.split(X, y)):
X_train, y_train = (X.iloc[train_idx], y.iloc[train_idx])
X_valid, y_valid = (X.iloc[val_idx], y.iloc[val_idx])
evaluation = [(X_train, y_train), (X_valid, y_valid)]
model = CatBoostClassifier(subsample=0.5, n_estimators=2000, max_depth=8, learning_rate=0.15, l2_leaf_reg=5, grow_policy='Lossguide', colsample_bylevel=0.8, bagging_temperature=0.0, early_stopping_rounds=300)
model.fit(X_train, y_train, eval_set=evaluation, verbose=0)
val_preds = model.predict_proba(X_valid)[:, 1]
val_score = roc_auc_score(y_valid, val_preds)
if val_score > 0.9:
scores.append(val_score)
catboost_models.append(model)
break
mean_val_auc = np.array(scores).mean(0)
test = pd.read_csv('/kaggle/input/playground-series-s3e7/test.csv')
X_test = test.drop(['id'], axis=1)
scaler = RobustScaler()
X_test = pd.DataFrame(scaler.fit_transform(X_test))
test_preds = []
for m in catboost_models:
preds = m.predict_proba(X_test)[:, 1]
test_preds.append(preds)
test_preds = np.array(test_preds).mean(0)
sub = pd.read_csv('/kaggle/input/playground-series-s3e7/sample_submission.csv', index_col=1)
sub[1] = test_preds
sub.to_csv('submission.csv')
sub.head() | 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 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.nlargest(20, 'booking_status')['booking_status'].index
cm = np.corrcoef(data[cols].values.T)
fig = plt.gcf()
fig.set_size_inches(10, 8)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
g = sns.PairGrid(data, y_vars=['booking_status'], x_vars=data.columns)
g = g.map_diag(plt.hist)
g = g.map_offdiag(sns.scatterplot)
y = data.booking_status
X = data.drop(['booking_status', 'id'], axis=1)
X_train, X_val, y_train, y_val = train_test_split(X, y, train_size=0.8, test_size=0.2)
scaler = RobustScaler()
X = pd.DataFrame(scaler.fit_transform(X))
X_train = pd.DataFrame(scaler.fit_transform(X_train))
X_val = pd.DataFrame(scaler.fit_transform(X_val))
XGB_model = XGBRegressor(random_state=0)
XGB_scores = cross_val_score(XGB_model, X, y, cv=3, scoring='roc_auc')
XGB_mean_score = XGB_scores.mean()
XGB_std_score = XGB_scores.std()
print(f'RMSE = {XGB_scores}')
print(f'Mean RMSE = {XGB_mean_score:.2f}')
print(f'StDev RMSE = {XGB_std_score:.2f}') | 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')
data.drop(columns=['date'], inplace=True)
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_estimators=150, random_state=42))
pipe.fit(x_train, y_train)
pred = pipe.predict(x_test)
sns.heatmap(confusion_matrix(y_test, pred), annot=True, fmt='.0f') | 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')
data.drop(columns=['date'], inplace=True)
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)
sns.heatmap(confusion_matrix(y_test, pred), annot=True, fmt='.0f') | 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)
pred = pipe.predict(x_test)
pipe = mp(transformer, rfc(n_estimators=150, random_state=42))
pipe.fit(x_train, y_train)
pred = pipe.predict(x_test)
accuracy_score(y_test, pred) | 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='feedback') | 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)
pred = pipe.predict(x_test)
accuracy_score(y_test, pred) | 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_estimators=150, random_state=42))
pipe.fit(x_train, y_train) | 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(figsize=(24, 12))
sns.countplot(x='variation', hue='feedback', data=data) | 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(dftrain['1'])) | 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_features = []
for feature in range(300):
y = sns.regplot(x=str(feature), y='target', data=dftrain)
detailed_features.append({'feature': feature, 'slope': getCorr(dftrain['target'], dftrain[str(feature)])}) | 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-ii/test.csv')
y=sns.regplot(x='1',y='target',data=dftrain)
detailed_features=[]
for feature in range(300):
y=sns.regplot(x=str(feature),y='target',data=dftrain)
detailed_features.append({'feature':feature,'slope':getCorr(dftrain['target'],dftrain[str(feature)])})
detailed_features.sort(key=getSlope, reverse=True)
features_to_save = []
for iteration in range(6):
features_to_save.append(detailed_features[iteration]['feature'])
def getDfProcessed(df_toProcess, features_to_save):
df_processed = pd.DataFrame()
for iteration in features_to_save:
feature = df_toProcess[str(iteration)].values
df_processed[str(iteration)] = feature
return df_processed
dftrain_processed = getDfProcessed(dftrain, features_to_save)
y_train = dftrain.pop('target')
model = tf.keras.models.Sequential([tf.keras.layers.Flatten(), tf.keras.layers.Dense(7, activation=tf.nn.relu), tf.keras.layers.Dense(2, activation=tf.nn.softmax)])
model.compile(loss='binary_crossentropy', optimizer='Adam', metrics=['accuracy'])
model.fit(dftrain_processed, y_train, epochs=10) | 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-ii/test.csv')
y=sns.regplot(x='1',y='target',data=dftrain)
detailed_features=[]
for feature in range(300):
y=sns.regplot(x=str(feature),y='target',data=dftrain)
detailed_features.append({'feature':feature,'slope':getCorr(dftrain['target'],dftrain[str(feature)])})
detailed_features.sort(key=getSlope, reverse=True)
features_to_save = []
for iteration in range(6):
features_to_save.append(detailed_features[iteration]['feature'])
def getDfProcessed(df_toProcess, features_to_save):
df_processed = pd.DataFrame()
for iteration in features_to_save:
feature = df_toProcess[str(iteration)].values
df_processed[str(iteration)] = feature
return df_processed
dftrain_processed = getDfProcessed(dftrain, features_to_save)
dftest_processed = getDfProcessed(dftest, features_to_save)
y_train = dftrain.pop('target')
model = tf.keras.models.Sequential([tf.keras.layers.Flatten(), tf.keras.layers.Dense(7, activation=tf.nn.relu), tf.keras.layers.Dense(2, activation=tf.nn.softmax)])
model.compile(loss='binary_crossentropy', optimizer='Adam', metrics=['accuracy'])
model.fit(dftrain_processed, y_train, epochs=10)
predictions_list = list(model.predict(dftest_processed))
def getPredictions(prediction_list):
prediction_list_processced = []
for iteration in prediction_list:
prediction_list_processced.append(round(iteration[1]))
return prediction_list_processced
dfsubmission = pd.DataFrame()
dfsubmission['id'] = dftest['id']
dfsubmission['target'] = getPredictions(predictions_list)
dfsubmission | 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_64', 'D_66', 'D_68']
cat_cols_save_str = ['D_63', 'D_64']
str_cols = ['customer_ID']
date_cols = ['S_2']
df_dtype = {col: 'float16' for col in all_cols if col not in cat_cols + str_cols + date_cols}
for col in str_cols + cat_cols_save_str:
df_dtype[col] = 'str'
nulls = train.isnull().sum().to_frame()
count_row = train.shape[0]
nulls['Index'] = nulls.index
nulls['Class'] = nulls['Index'].str.rpartition('_')[0]
nulls['ClassID'] = nulls['Index'].str.rpartition('_')[2]
nulls = nulls.reindex(columns=['Index', 'Class', 'ClassID', 0])
nulls = nulls.sort_values(by=['Class', 'ClassID'])
for index, row in nulls.iterrows():
if row[0] != 0:
train[index] = train[index].fillna(train[index].median())
nulls = train.isnull().sum().to_frame()
count_row = train.shape[0]
nulls['Index'] = nulls.index
nulls['Class'] = nulls['Index'].str.rpartition('_')[0]
nulls['ClassID'] = nulls['Index'].str.rpartition('_')[2]
nulls = nulls.reindex(columns=['Index', 'Class', 'ClassID', 0])
nulls = nulls.sort_values(by=['Class', 'ClassID'])
print('---------------after fill-----------------')
for index, row in nulls.iterrows():
if row[0] != 0:
train[index] = train[index].fillna(train[index].median())
print('---------------Done fill-----------------') | 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)
print('Type of S:')
print(train.filter(regex='S_').dtypes)
print('Type of P:')
print(train.filter(regex='P_').dtypes)
print('Type of B:')
print(train.filter(regex='B_').dtypes)
print('Type of R:')
print(train.filter(regex='R_').dtypes) | 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_64', 'D_66', 'D_68']
cat_cols_save_str = ['D_63', 'D_64']
str_cols = ['customer_ID']
date_cols = ['S_2']
df_dtype = {col: 'float16' for col in all_cols if col not in cat_cols + str_cols + date_cols}
for col in str_cols + cat_cols_save_str:
df_dtype[col] = 'str'
with pd.option_context('display.max_rows', 1000):
print('Count of NaN of D: \n' + str(train.filter(regex='D_').isna().sum()))
print('Count of NaN of S: \n' + str(train.filter(regex='S_').isna().sum()))
print('Count of NaN of P: \n' + str(train.filter(regex='P_').isna().sum()))
print('Count of NaN of B: \n' + str(train.filter(regex='B_').isna().sum()))
print('Count of NaN of R: \n' + str(train.filter(regex='R_').isna().sum())) | 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_64', 'D_66', 'D_68']
cat_cols_save_str = ['D_63', 'D_64']
str_cols = ['customer_ID']
date_cols = ['S_2']
df_dtype = {col: 'float16' for col in all_cols if col not in cat_cols + str_cols + date_cols}
for col in str_cols + cat_cols_save_str:
df_dtype[col] = 'str'
for col in cat_cols_save_str:
print(col)
print(train[col].unique()) | 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_64', 'D_66', 'D_68']
cat_cols_save_str = ['D_63', 'D_64']
str_cols = ['customer_ID']
date_cols = ['S_2']
df_dtype = {col: 'float16' for col in all_cols if col not in cat_cols + str_cols + date_cols}
for col in str_cols + cat_cols_save_str:
df_dtype[col] = 'str'
nulls = train.isnull().sum().to_frame()
count_row = train.shape[0]
nulls['Index'] = nulls.index
nulls['Class'] = nulls['Index'].str.rpartition('_')[0]
nulls['ClassID'] = nulls['Index'].str.rpartition('_')[2]
nulls = nulls.reindex(columns=['Index', 'Class', 'ClassID', 0])
nulls = nulls.sort_values(by=['Class', 'ClassID'])
print('---------------before fill-----------------')
for index, row in nulls.iterrows():
if row[0] != 0:
print(index, row[0], round(row[0] / count_row * 100, 2))
train[index] = train[index].fillna(train[index].median())
print('---------------Done fill-----------------') | 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().sum()))
print('Count of NaN of S: \n' + str(train.filter(regex='S_').isna().sum()))
print('Count of NaN of P: \n' + str(train.filter(regex='P_').isna().sum()))
print('Count of NaN of B: \n' + str(train.filter(regex='B_').isna().sum()))
print('Count of NaN of R: \n' + str(train.filter(regex='R_').isna().sum())) | 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.shape[0]
df.isnull().sum() | 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_selection import cross_val_score
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
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.shape[0]
sns.histplot(data=df['Age'], color='teal', kde=True)
plt.show() | code |
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