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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|---|---|---|---|
130007434/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
duplicates_mask = hr.duplicated()
duplicates = hr[duplicates_mask]
duplicates.shape
hr.duplicated().sum()
hr_no_duplicates = hr.drop_duplicates()
hr_no_duplicates
hr_no_duplicates.describe() | code |
130007434/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum() | code |
130007434/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
duplicates_mask = hr.duplicated()
duplicates = hr[duplicates_mask]
duplicates.shape
hr.duplicated().sum()
hr_no_duplicates = hr.drop_duplicates()
hr_no_duplicates
# Determine the number of rows containing outliers
# create a boxplot of the 'time_spend_company' column
_, flier_dict = hr_no_duplicates.boxplot(column='time_spend_company', return_type='both')
# get the values of the outliers
outliers = [flier.get_ydata()[0] for flier in flier_dict['fliers']]
# select the rows that contain the outliers
outlier_rows = hr_no_duplicates['time_spend_company'].isin(outliers)
# count the number of rows that contain the outliers
num_outliers = outlier_rows.sum()
# print the number of rows that contain the outliers
print('Number of rows containing outliers:', num_outliers)
sns.pairplot(hr_no_duplicates) | code |
130007434/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.describe() | code |
130007434/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
duplicates_mask = hr.duplicated()
duplicates = hr[duplicates_mask]
duplicates.shape
hr.duplicated().sum() | code |
130007434/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
pd.set_option('display.max_columns', None)
from xgboost import XGBClassifier
from xgboost import XGBRegressor
from xgboost import plot_importance
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, ConfusionMatrixDisplay, classification_report
from sklearn.metrics import roc_auc_score, roc_curve
from sklearn.tree import plot_tree | code |
130007434/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns | code |
130007434/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
duplicates_mask = hr.duplicated()
duplicates = hr[duplicates_mask]
duplicates.shape
hr.duplicated().sum()
hr_no_duplicates = hr.drop_duplicates()
hr_no_duplicates
# Determine the number of rows containing outliers
# create a boxplot of the 'time_spend_company' column
_, flier_dict = hr_no_duplicates.boxplot(column='time_spend_company', return_type='both')
# get the values of the outliers
outliers = [flier.get_ydata()[0] for flier in flier_dict['fliers']]
# select the rows that contain the outliers
outlier_rows = hr_no_duplicates['time_spend_company'].isin(outliers)
# count the number of rows that contain the outliers
num_outliers = outlier_rows.sum()
# print the number of rows that contain the outliers
print('Number of rows containing outliers:', num_outliers)
percentile25 = hr_no_duplicates['time_spend_company'].quantile(0.25)
percentile75 = hr_no_duplicates['time_spend_company'].quantile(0.75)
iqr = percentile75 - percentile25
print('IQR:', iqr)
upper_limit = percentile75 + 1.5 * iqr
lower_limit = percentile25 - 1.5 * iqr
print('Lower limit:', lower_limit)
print('Upper limit:', upper_limit)
outliers = hr_no_duplicates[(hr_no_duplicates['time_spend_company'] > upper_limit) | (hr_no_duplicates['time_spend_company'] < lower_limit)]
print('Number of rows in the data containing outliers in `time_spend_company`:', len(outliers)) | code |
130007434/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns | code |
130007434/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
duplicates_mask = hr.duplicated()
duplicates = hr[duplicates_mask]
duplicates.shape
hr.duplicated().sum()
hr_no_duplicates = hr.drop_duplicates()
hr_no_duplicates
hr_no_duplicates.boxplot(column='time_spend_company')
plt.show() | code |
130007434/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
duplicates_mask = hr.duplicated()
duplicates = hr[duplicates_mask]
duplicates.shape
hr.duplicated().sum()
hr_no_duplicates = hr.drop_duplicates()
hr_no_duplicates
fig, ax = plt.subplots()
ax.boxplot(hr_no_duplicates['time_spend_company'])
whiskers = ax.lines[2:4]
whisker_values = [whisk.get_ydata()[1] for whisk in whiskers]
print('Whisker values:', whisker_values) | code |
130007434/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.head(10) | code |
130007434/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
duplicates_mask = hr.duplicated()
duplicates = hr[duplicates_mask]
duplicates.shape
hr.duplicated().sum()
hr_no_duplicates = hr.drop_duplicates()
hr_no_duplicates
_, flier_dict = hr_no_duplicates.boxplot(column='time_spend_company', return_type='both')
outliers = [flier.get_ydata()[0] for flier in flier_dict['fliers']]
outlier_rows = hr_no_duplicates['time_spend_company'].isin(outliers)
num_outliers = outlier_rows.sum()
print('Number of rows containing outliers:', num_outliers) | code |
130007434/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
duplicates_mask = hr.duplicated()
duplicates = hr[duplicates_mask]
duplicates.shape | code |
130007434/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.columns
hr.columns = hr.columns.str.lower()
hr.columns = hr.columns.str.replace(' ', '_')
hr.columns = hr.columns.str.replace('\\W', '_', regex=True)
hr.columns
hr.isna().sum()
duplicates_mask = hr.duplicated()
duplicates = hr[duplicates_mask]
duplicates.shape
hr.duplicated().sum()
hr_no_duplicates = hr.drop_duplicates()
hr_no_duplicates | code |
130007434/cell_5 | [
"image_output_1.png"
] | import pandas as pd
dataset_path = '/kaggle/input/hr-analytics-and-job-prediction/HR_comma_sep.csv'
hr = pd.read_csv(dataset_path)
hr.info() | code |
130012889/cell_6 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train) | code |
130012889/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.impute import SimpleImputer
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score | code |
130012889/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
preds_valid = model.predict(X_valid)
print(accuracy_score(y_valid, preds_valid)) | code |
18148304/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import statsmodels.api as sm # https://www.statsmodels.org/stable/index.html
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
input_data = pd.read_csv('../input/US_graduate_schools_admission_parameters_dataset_private.csv')
X = input_data[['GRE Score', 'IELTS Score', 'SOP', 'LOR ', 'CGPA']]
y = input_data['Chance of Admit ']
X = sm.add_constant(X)
multiple_linear_regression_model = sm.OLS(y, X)
multiple_linear_regression_model_fit = multiple_linear_regression_model.fit()
print(multiple_linear_regression_model_fit.params) | code |
18148304/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt # documentation: https://matplotlib.org/api/pyplot_api.html
import pandas as pd
import statsmodels.api as sm # https://www.statsmodels.org/stable/index.html
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
input_data = pd.read_csv('../input/US_graduate_schools_admission_parameters_dataset_private.csv')
X = input_data[['GRE Score', 'IELTS Score', 'SOP', 'LOR ', 'CGPA']]
y = input_data['Chance of Admit ']
X = sm.add_constant(X)
multiple_linear_regression_model = sm.OLS(y, X)
multiple_linear_regression_model_fit = multiple_linear_regression_model.fit()
X1 = input_data['GRE Score']
y = input_data['Chance of Admit ']
plt.scatter(X1, y)
plt.show() | code |
18148304/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import statsmodels.api as sm # https://www.statsmodels.org/stable/index.html
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
input_data = pd.read_csv('../input/US_graduate_schools_admission_parameters_dataset_private.csv')
X = input_data[['GRE Score', 'IELTS Score', 'SOP', 'LOR ', 'CGPA']]
y = input_data['Chance of Admit ']
X = sm.add_constant(X)
multiple_linear_regression_model = sm.OLS(y, X)
multiple_linear_regression_model_fit = multiple_linear_regression_model.fit()
multiple_linear_regression_model_fit.summary() | code |
18148304/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
input_data = pd.read_csv('../input/US_graduate_schools_admission_parameters_dataset_private.csv')
input_data.head() | code |
33109004/cell_25 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
special_cases_price_zero = car_us_df[car_us_df.price == 0]
special_cases_price_zero.shape
special_cases_price_great = car_us_df[car_us_df.price >= 30000]
special_cases_price_great.head() | code |
33109004/cell_56 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split,cross_val_score,KFold,cross_val_predict,GridSearchCV
from xgboost import XGBRegressor
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get_height() / 2, f'{int(p.get_width())}')
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
special_cases_price_zero = car_us_df[car_us_df.price == 0]
special_cases_price_zero.shape
special_cases_price_great = car_us_df[car_us_df.price >= 30000]
car_us_df[car_us_df.price == car_us_df.price.max()]
price_estimator_df = car_us_df.copy()
features_to_drop = ['vin', 'lot', 'country', 'condition']
price_estimator_df.drop(features_to_drop, axis=1, inplace=True)
price_estimator_df
cat_features = [col for col in price_estimator_df.select_dtypes('object')]
cat_features
price_estimator_df.isnull().any()
train = price_estimator_df.drop('price', axis=1)
target = price_estimator_df.price
params = {'n_estimator': [1, 1000, 50], 'n_jobs': [1, 5, 1]}
model = XGBRegressor()
result = abs(cross_val_score(model, train, target, cv=KFold(n_splits=10), scoring='r2')).mean()
result | code |
33109004/cell_33 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get_height() / 2, f'{int(p.get_width())}')
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
special_cases_price_zero = car_us_df[car_us_df.price == 0]
special_cases_price_zero.shape
special_cases_price_great = car_us_df[car_us_df.price >= 30000]
car_us_df[car_us_df.price == car_us_df.price.max()]
plot_bar_vertical(car_us_df.groupby(['brand', 'model']).brand.count().sort_values().tail(15)) | code |
33109004/cell_44 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get_height() / 2, f'{int(p.get_width())}')
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
special_cases_price_zero = car_us_df[car_us_df.price == 0]
special_cases_price_zero.shape
special_cases_price_great = car_us_df[car_us_df.price >= 30000]
car_us_df[car_us_df.price == car_us_df.price.max()]
price_estimator_df = car_us_df.copy()
features_to_drop = ['vin', 'lot', 'country', 'condition']
price_estimator_df.drop(features_to_drop, axis=1, inplace=True)
price_estimator_df | code |
33109004/cell_55 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get_height() / 2, f'{int(p.get_width())}')
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
special_cases_price_zero = car_us_df[car_us_df.price == 0]
special_cases_price_zero.shape
special_cases_price_great = car_us_df[car_us_df.price >= 30000]
car_us_df[car_us_df.price == car_us_df.price.max()]
price_estimator_df = car_us_df.copy()
features_to_drop = ['vin', 'lot', 'country', 'condition']
price_estimator_df.drop(features_to_drop, axis=1, inplace=True)
price_estimator_df
cat_features = [col for col in price_estimator_df.select_dtypes('object')]
cat_features
price_estimator_df.isnull().any()
train = price_estimator_df.drop('price', axis=1)
target = price_estimator_df.price
X_trin, X_val, y_train, y_val = train | code |
33109004/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
car_us_df.info() | code |
33109004/cell_40 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get_height() / 2, f'{int(p.get_width())}')
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
special_cases_price_zero = car_us_df[car_us_df.price == 0]
special_cases_price_zero.shape
special_cases_price_great = car_us_df[car_us_df.price >= 30000]
car_us_df[car_us_df.price == car_us_df.price.max()]
plot_bar_vertical(car_us_df.state.value_counts().sort_values()) | code |
33109004/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
special_cases_price_zero = car_us_df[car_us_df.price == 0]
special_cases_price_zero.shape
special_cases_price_great = car_us_df[car_us_df.price >= 30000]
car_us_df[car_us_df.price == car_us_df.price.max()] | code |
33109004/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get_height() / 2, f'{int(p.get_width())}')
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
special_cases_price_zero = car_us_df[car_us_df.price == 0]
special_cases_price_zero.shape
special_cases_price_great = car_us_df[car_us_df.price >= 30000]
plot_bar_vertical(special_cases_price_great.groupby(['brand', 'model']).brand.count().sort_values()) | code |
33109004/cell_48 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get_height() / 2, f'{int(p.get_width())}')
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
special_cases_price_zero = car_us_df[car_us_df.price == 0]
special_cases_price_zero.shape
special_cases_price_great = car_us_df[car_us_df.price >= 30000]
car_us_df[car_us_df.price == car_us_df.price.max()]
price_estimator_df = car_us_df.copy()
features_to_drop = ['vin', 'lot', 'country', 'condition']
price_estimator_df.drop(features_to_drop, axis=1, inplace=True)
price_estimator_df
cat_features = [col for col in price_estimator_df.select_dtypes('object')]
cat_features
price_estimator_df.isnull().any() | code |
33109004/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
33109004/cell_54 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder,OneHotEncoder
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get_height() / 2, f'{int(p.get_width())}')
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
special_cases_price_zero = car_us_df[car_us_df.price == 0]
special_cases_price_zero.shape
special_cases_price_great = car_us_df[car_us_df.price >= 30000]
car_us_df[car_us_df.price == car_us_df.price.max()]
price_estimator_df = car_us_df.copy()
features_to_drop = ['vin', 'lot', 'country', 'condition']
price_estimator_df.drop(features_to_drop, axis=1, inplace=True)
price_estimator_df
cat_features = [col for col in price_estimator_df.select_dtypes('object')]
cat_features
price_estimator_df.isnull().any()
train = price_estimator_df.drop('price', axis=1)
target = price_estimator_df.price
cat_transformer = LabelEncoder()
for col in cat_features:
train[col] = cat_transformer.fit_transform(train[col])
train | code |
33109004/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
sns.distplot(car_us_df.price, kde=False) | code |
33109004/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get_height() / 2, f'{int(p.get_width())}')
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
special_cases_price_zero = car_us_df[car_us_df.price == 0]
special_cases_price_zero.shape
ax = special_cases_price_zero.groupby(['brand', 'model']).brand.count().plot.pie(figsize=(10, 15), subplots=True) | code |
33109004/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
car_us_df.describe() | code |
33109004/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get_height() / 2, f'{int(p.get_width())}')
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
special_cases_price_zero = car_us_df[car_us_df.price == 0]
special_cases_price_zero.shape
plot_bar_vertical(special_cases_price_zero.groupby(['brand', 'model']).brand.count().sort_values()) | code |
33109004/cell_51 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get_height() / 2, f'{int(p.get_width())}')
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
special_cases_price_zero = car_us_df[car_us_df.price == 0]
special_cases_price_zero.shape
special_cases_price_great = car_us_df[car_us_df.price >= 30000]
car_us_df[car_us_df.price == car_us_df.price.max()]
price_estimator_df = car_us_df.copy()
features_to_drop = ['vin', 'lot', 'country', 'condition']
price_estimator_df.drop(features_to_drop, axis=1, inplace=True)
price_estimator_df
cat_features = [col for col in price_estimator_df.select_dtypes('object')]
cat_features
price_estimator_df.isnull().any()
train = price_estimator_df.drop('price', axis=1)
target = price_estimator_df.price
train | code |
33109004/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
special_cases_price_zero = car_us_df[car_us_df.price == 0]
display(special_cases_price_zero.head(10))
special_cases_price_zero.shape | code |
33109004/cell_46 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get_height() / 2, f'{int(p.get_width())}')
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
special_cases_price_zero = car_us_df[car_us_df.price == 0]
special_cases_price_zero.shape
special_cases_price_great = car_us_df[car_us_df.price >= 30000]
car_us_df[car_us_df.price == car_us_df.price.max()]
price_estimator_df = car_us_df.copy()
features_to_drop = ['vin', 'lot', 'country', 'condition']
price_estimator_df.drop(features_to_drop, axis=1, inplace=True)
price_estimator_df
cat_features = [col for col in price_estimator_df.select_dtypes('object')]
cat_features | code |
33109004/cell_22 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get_height() / 2, f'{int(p.get_width())}')
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
special_cases_price_zero = car_us_df[car_us_df.price == 0]
special_cases_price_zero.shape
ax = special_cases_price_zero.groupby(['brand','model']).brand.count().plot.pie(figsize=(10,15),subplots=True)
special_cases_price_zero[special_cases_price_zero.title_status == 'clean vehicle'] | code |
33109004/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
car_us_df.price.describe() | code |
33109004/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
car_us_df.head() | code |
33109004/cell_36 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
def plot_bar_vertical(df, figsize=(10, 15), xlabel='Count Number'):
ax = df.plot.barh(figsize=figsize)
for p in ax.patches:
ax.text(p.get_x() + p.get_width(), p.get_y() + p.get_height() / 2, f'{int(p.get_width())}')
car_us_df = pd.read_csv('/kaggle/input/usa-cers-dataset/USA_cars_datasets.csv', index_col=0)
special_cases_price_zero = car_us_df[car_us_df.price == 0]
special_cases_price_zero.shape
special_cases_price_great = car_us_df[car_us_df.price >= 30000]
car_us_df[car_us_df.price == car_us_df.price.max()]
plot_bar_vertical(car_us_df.groupby(['brand', 'model']).price.mean().sort_values(), (12, 50), 'price') | code |
104124059/cell_9 | [
"text_plain_output_1.png"
] | (1.0 - 4.85883614 / 97) * 100 | code |
104124059/cell_7 | [
"text_plain_output_1.png"
] | import scipy.sparse as sps
train_input = sps.load_npz('../input/open-problems-msci-multiome-sparse-matrices/train_multiome_input_sparse.npz')
def get_size(sparse_m):
size_gb = (sparse_m.indices.nbytes + sparse_m.indptr.nbytes + sparse_m.data.nbytes) * 1e-09
return f'Size: {size_gb} GB'
get_size(train_input) | code |
32069364/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO'], how='all')
df2 | code |
32069364/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df['MORTDUE'].value_counts() | code |
32069364/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum() | code |
32069364/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df['REASON'].value_counts() | code |
32069364/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO'], how='all')
mode = float(df2['VALUEMORTDUE'].mode())
mode
MORTDUEMEDIA = {'MORTDUE': df2['VALUE'] / mode}
df2 = df2.fillna(value=MORTDUEMEDIA)
VALUEMEDIA = {'VALUE': df2['MORTDUE'] * mode}
df2 = df2.fillna(value=VALUEMEDIA)
df2.isnull().sum()
df2.isnull().sum()
df2.isnull().sum() | code |
32069364/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO'], how='all')
df2.head(30) | code |
32069364/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO'], how='all')
mode = float(df2['VALUEMORTDUE'].mode())
mode
MORTDUEMEDIA = {'MORTDUE': df2['VALUE'] / mode}
df2 = df2.fillna(value=MORTDUEMEDIA)
VALUEMEDIA = {'VALUE': df2['MORTDUE'] * mode}
df2 = df2.fillna(value=VALUEMEDIA)
df2.isnull().sum()
df2.info() | code |
32069364/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO'], how='all')
mode = float(df2['VALUEMORTDUE'].mode())
mode
MORTDUEMEDIA = {'MORTDUE': df2['VALUE'] / mode}
df2 = df2.fillna(value=MORTDUEMEDIA)
VALUEMEDIA = {'VALUE': df2['MORTDUE'] * mode}
df2 = df2.fillna(value=VALUEMEDIA)
df2.isnull().sum()
df2.isnull().sum()
import plotly.express as px
fig = px.bar(df2, x='REASON', y='JOB', color='JOB')
fig.show() | code |
32069364/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df['MORTDUE'].min() | code |
32069364/cell_26 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO'], how='all')
mode = float(df2['VALUEMORTDUE'].mode())
mode
MORTDUEMEDIA = {'MORTDUE': df2['VALUE'] / mode}
df2 = df2.fillna(value=MORTDUEMEDIA)
VALUEMEDIA = {'VALUE': df2['MORTDUE'] * mode}
df2 = df2.fillna(value=VALUEMEDIA)
df2.info() | code |
32069364/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df.describe() | code |
32069364/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 | code |
32069364/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 |
32069364/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.io as pio
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO'], how='all')
mode = float(df2['VALUEMORTDUE'].mode())
mode
MORTDUEMEDIA = {'MORTDUE': df2['VALUE'] / mode}
df2 = df2.fillna(value=MORTDUEMEDIA)
VALUEMEDIA = {'VALUE': df2['MORTDUE'] * mode}
df2 = df2.fillna(value=VALUEMEDIA)
df2.isnull().sum()
df2.isnull().sum()
import plotly.io as pio
GRAREASON = [dict(type='bar', x=df2['JOB'], y=df2['REASON'].value_counts(dropna=False), mode='markers')]
layout = dict(title='REASON = 1')
fig_dict = dict(data=GRAREASON, layout=layout)
pio.show(fig_dict, validate=False) | code |
32069364/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.select_dtypes('object').head() | code |
32069364/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
plt.figure(figsize=(4, 4))
sns.countplot(y='REASON', data=df)
plt.figure(figsize=(8, 8))
sns.countplot(y='JOB', data=df)
plt.figure(figsize=(8, 8))
sns.countplot(y='DEROG', data=df)
plt.figure(figsize=(8, 8))
sns.countplot(y='DELINQ', data=df)
plt.figure(figsize=(8, 8))
sns.countplot(y='NINQ', data=df) | code |
32069364/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df[['LOAN', 'MORTDUE', 'VALUE', 'YOJ', 'CLAGE', 'CLNO', 'DEBTINC']].hist(figsize=[20, 20]) | code |
32069364/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.info() | code |
32069364/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO'], how='all')
mode = float(df2['VALUEMORTDUE'].mode())
mode
MORTDUEMEDIA = {'MORTDUE': df2['VALUE'] / mode}
df2 = df2.fillna(value=MORTDUEMEDIA)
VALUEMEDIA = {'VALUE': df2['MORTDUE'] * mode}
df2 = df2.fillna(value=VALUEMEDIA)
df2.isnull().sum()
df2.isnull().sum() | code |
32069364/cell_24 | [
"image_output_5.png",
"image_output_4.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO'], how='all')
mode = float(df2['VALUEMORTDUE'].mode())
mode | code |
32069364/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO'], how='all')
mode = float(df2['VALUEMORTDUE'].mode())
mode
MORTDUEMEDIA = {'MORTDUE': df2['VALUE'] / mode}
df2 = df2.fillna(value=MORTDUEMEDIA)
VALUEMEDIA = {'VALUE': df2['MORTDUE'] * mode}
df2 = df2.fillna(value=VALUEMEDIA)
df2.isnull().sum() | code |
32069364/cell_37 | [
"text_html_output_2.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO'], how='all')
mode = float(df2['VALUEMORTDUE'].mode())
mode
MORTDUEMEDIA = {'MORTDUE': df2['VALUE'] / mode}
df2 = df2.fillna(value=MORTDUEMEDIA)
VALUEMEDIA = {'VALUE': df2['MORTDUE'] * mode}
df2 = df2.fillna(value=VALUEMEDIA)
df2.isnull().sum()
df2.isnull().sum()
df2.isnull().sum()
df2.isnull().sum()
corr = df2.corr(method='spearman')
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
f, ax = plt.subplots(figsize=(20, 18))
sns.heatmap(corr, mask=mask, cmap='YlGnBu', vmax=0.3, center=0, square=True, linewidths=0.5, cbar_kws={'shrink': 0.5}) | code |
32069364/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df['BAD'].value_counts().reset_index() | code |
32069364/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df['JOB'].value_counts() | code |
32069364/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/hmeq-data/hmeq.csv')
df.isnull().sum()
df2 = df
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'NINQ', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'CLNO', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'DEBTINC'], how='all')
df2 = df2.dropna(subset=['MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO'], how='all')
mode = float(df2['VALUEMORTDUE'].mode())
mode
MORTDUEMEDIA = {'MORTDUE': df2['VALUE'] / mode}
df2 = df2.fillna(value=MORTDUEMEDIA)
VALUEMEDIA = {'VALUE': df2['MORTDUE'] * mode}
df2 = df2.fillna(value=VALUEMEDIA)
df2.isnull().sum()
df2.isnull().sum()
df2.isnull().sum()
df2.isnull().sum() | code |
32065554/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | def function(stuff):
"""
Describe your function
"""
y = 0
return y
x = function(stuff)
x | code |
32065554/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def get_number_of_elements_nested_list(list_of_keyword_lists):
counter = 0
for lst in list_of_keyword_lists:
counter += len(lst)
return counter
def evaluate_text_via_list_of_list_of_keywords(text, list_of_keyword_lists):
num_keyword_lists = len(list_of_keyword_lists)
len_text = len(text)
arr_keyword_list_hits = np.zeros(num_keyword_lists)
for num_word in range(len_text):
for num_keyword_list in range(num_keyword_lists):
if text[num_word] in list_of_keyword_lists[num_keyword_list]:
arr_keyword_list_hits[num_keyword_list] += 1
return arr_keyword_list_hits
def evaluate_journal_by_keywords(df_paper, list_of_keyword_lists):
"""
df_paper pd.DataFrame with Covid 19 papers
list_of_keyword_lists Nested list. Contains multiple lists with keywords. Each list is a subgroup/clustering of keywords.
"""
num_entries = df_paper.shape[0]
dct_abstracts = {}
num_keywords = get_number_of_elements_nested_list(list_of_keyword_lists)
for num_paper in range(num_entries):
if type(df_paper.iloc[num_paper, 8]) != float:
txt_abstract = df_paper.iloc[num_paper, 8].split()
idx = num_paper
arr_keyword_list_hits = evaluate_text_via_list_of_list_of_keywords(txt_abstract, list_of_keyword_lists)
int_keyword_frequency = sum(arr_keyword_list_hits)
num_keyword_sources = len(arr_keyword_list_hits[arr_keyword_list_hits > 0])
num_keyword_lists = len(list_of_keyword_lists)
numerator = num_keyword_sources + int_keyword_frequency
denominator = num_keyword_lists + num_keywords
abstract_score = numerator / denominator
dct_abstracts.update({num_paper: {'Abstract Score': abstract_score}})
return dct_abstracts
data_dir = '../input/CORD-19-research-challenge/'
data_file = 'metadata.csv'
data = pd.read_csv(data_dir + data_file)
lst_corona = ['Corona', 'corona', 'corona virus', 'coronavirus', 'corona viruses', 'coronaviruses', 'Coronaviridae', 'coronaviridae', 'COVID-19', 'Covid-19', 'covid-19', 'COVID', 'COV', 'SARS']
lst_genetics = ['genetics']
lst_origin = ['origin', 'member', 'family']
lst_evolution = ['evolution', 'development', 'develops', 'developed']
lst_task = [lst_genetics, lst_origin, lst_evolution]
lst_subtask_1_genome = ['Genome', 'genome']
lst_subtask_1_dissemination = ['dissemination', 'Dissemination', 'propagation', 'Propagation', 'spread', 'Spread', 'spreading', 'Spreading']
lst_subtask_1_treatment = ['treatment', 'Treatment', 'diagnostic', 'Diagnostic', 'diagnostics', 'Diagnostics', 'therapeutics', 'Therapeutics']
lst_subtask_1_variation = ['Difference', 'in contrast', 'variation', 'deviation', 'shows mutations', 'enrichment', 'similarities']
lst_subtask_1_reference = ['Accession number', 'reference', 'sample', 'identification of']
lst_subtask_1_known = ['Known', 'already published', 'already reported']
lst_subtask_1 = [lst_subtask_1_genome, lst_subtask_1_dissemination, lst_subtask_1_treatment, lst_subtask_1_variation, lst_subtask_1_reference, lst_subtask_1_known, lst_corona]
lst_subtask_2_ = []
lst_subtask_2_ = []
lst_subtask_2_ = []
lst_subtask_2_ = []
lst_subtask_2_ = []
lst_subtask_2 = []
lst_subtask_3_2_livestock = ['farm', 'wildlife', 'wild animal', 'undomesticated', 'livestock']
lst_subtask_3_2_area = ['Southeast-Asia']
lst_subtask_3_2_control = ['surveil', 'control', 'screen', 'check', 'monitor', 'examine']
lst_subtask_3_2 = [lst_subtask_3_2_livestock, lst_subtask_3_2_area, lst_subtask_3_2_control, lst_corona]
lst_subtask_3_1_host = ['host', 'organism', 'human']
lst_subtask_3_2_infection = ['infection', 'disease', 'respiratory syndrom']
lst_subtask_3_3_lab = ['experimental', 'laboratory', 'under conditions']
lst_subtask_3 = [lst_subtask_3_1_host, lst_subtask_3_2_infection, lst_subtask_3_3_lab, lst_corona]
lst_subtask_4_host = ['animal', 'host', 'hosts', 'Host', 'Hosts', 'human', 'Human', 'Humans', 'humans', 'CoV-Host', 'organism']
lst_subtask_4_transmission = ['pathogen', 'spill-over', 'intraspecies', 'interaction', 'host-shift', 'spread', 'evolution', 'transmission', 'infection']
lst_subtask_4_evidence = ['evidence', 'proof', 'association', 'connection', 'associated']
lst_subtask_4 = [lst_subtask_4_host, lst_subtask_4_transmission, lst_subtask_4_evidence, lst_corona]
lst_subtask_5_ = []
lst_subtask_5_ = []
lst_subtask_5_ = []
lst_subtask_5_ = []
lst_subtask_5 = []
lst_subtask_6_ = []
lst_subtask_6_ = []
lst_subtask_6_ = []
lst_subtask_6_ = []
lst_subtask_6_ = []
lst_subtask_6 = []
dct_res = evaluate_journal_by_keywords(data, lst_subtask_1)
lst_ranked = sorted(dct_res, key=lambda x: dct_res[x]['Abstract Score'], reverse=True)
lst_ranked[:3] | code |
32065554/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_dir = '../input/CORD-19-research-challenge/'
data_file = 'metadata.csv'
data = pd.read_csv(data_dir + data_file)
data.iloc[23643, 8] | code |
129029092/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
df.shape
data.shape
df.drop('PassengerId', axis=1)
df.drop('Name', axis=1, inplace=True)
data.drop('PassengerId', axis=1)
data.drop('Name', axis=1, inplace=True)
df.isnull().sum()
data.isnull().sum()
new_df = pd.concat([df, data])
new_df.shape
sns.pairplot(data=new_df, hue='Transported') | code |
129029092/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
df.shape
sns.heatmap(df.isnull()) | code |
129029092/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
df.shape
data.shape
df.drop('PassengerId', axis=1)
df.drop('Name', axis=1, inplace=True)
data.drop('PassengerId', axis=1)
data.drop('Name', axis=1, inplace=True)
df.isnull().sum()
data.isnull().sum()
new_df = pd.concat([df, data])
new_df.shape
df_test = new_df[new_df['Transported'].isnull()]
df_train = new_df[~new_df['Transported'].isnull()]
df.drop('Cabin', axis=1, inplace=True)
data.drop('Cabin', axis=1, inplace=True)
X = df_train.drop('Transported', axis=1)
y = df_train['Transported']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.33, shuffle=True)
from sklearn.tree import DecisionTreeClassifier
y = df['Transported']
features = ['Destination', 'CryoSleep', 'HomePlanet', 'VIP']
X = pd.get_dummies(df[features])
X_test = pd.get_dummies(data[features])
model = DecisionTreeClassifier(max_depth=7)
model.fit(X, y)
data.columns
y_pred = model.predict(X_test)
output = pd.DataFrame({'PassengerId': data.PassengerId, 'Transported': y_pred})
output.to_csv('submission.csv', index=False)
print('Submission successful!') | code |
129029092/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
df.shape
data.shape
df.drop('PassengerId', axis=1)
df.drop('Name', axis=1, inplace=True)
data.drop('PassengerId', axis=1)
data.drop('Name', axis=1, inplace=True)
df.isnull().sum()
data.isnull().sum()
new_df = pd.concat([df, data])
new_df.shape
sns.histplot(data=new_df, x='Age', bins=20, color='pink') | code |
129029092/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.head() | code |
129029092/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
df.shape
data.shape
df.drop('PassengerId', axis=1)
df.drop('Name', axis=1, inplace=True)
data.drop('PassengerId', axis=1)
data.drop('Name', axis=1, inplace=True)
df.isnull().sum()
data.isnull().sum()
new_df = pd.concat([df, data])
new_df.shape
df_test = new_df[new_df['Transported'].isnull()]
df_train = new_df[~new_df['Transported'].isnull()]
df.drop('Cabin', axis=1, inplace=True)
data.drop('Cabin', axis=1, inplace=True)
X = df_train.drop('Transported', axis=1)
y = df_train['Transported']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.33, shuffle=True)
from sklearn.tree import DecisionTreeClassifier
y = df['Transported']
features = ['Destination', 'CryoSleep', 'HomePlanet', 'VIP']
X = pd.get_dummies(df[features])
X_test = pd.get_dummies(data[features])
model = DecisionTreeClassifier(max_depth=7)
model.fit(X, y) | code |
129029092/cell_2 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns | code |
129029092/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
df.shape
data.shape
df.drop('PassengerId', axis=1)
df.drop('Name', axis=1, inplace=True)
data.drop('PassengerId', axis=1)
data.drop('Name', axis=1, inplace=True)
df.head() | code |
129029092/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
df.shape
data.shape
df.drop('PassengerId', axis=1)
df.drop('Name', axis=1, inplace=True)
data.drop('PassengerId', axis=1)
data.drop('Name', axis=1, inplace=True)
df.isnull().sum()
data.isnull().sum()
new_df = pd.concat([df, data])
new_df.shape
plt.figure(figsize=(15, 10))
sns.heatmap(new_df.corr(), annot=True) | code |
129029092/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 |
129029092/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
df.shape | code |
129029092/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
df.shape
data.shape
df.drop('PassengerId', axis=1)
df.drop('Name', axis=1, inplace=True)
data.drop('PassengerId', axis=1)
data.drop('Name', axis=1, inplace=True)
df.isnull().sum()
data.isnull().sum()
new_df = pd.concat([df, data])
new_df.shape | code |
129029092/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import tree
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
df.shape
data.shape
df.drop('PassengerId', axis=1)
df.drop('Name', axis=1, inplace=True)
data.drop('PassengerId', axis=1)
data.drop('Name', axis=1, inplace=True)
df.isnull().sum()
data.isnull().sum()
new_df = pd.concat([df, data])
new_df.shape
columns = ['CryoSleep', 'Destination', 'VIP', 'HomePlanet']
for col in columns:
fig, ax = plt.subplots(figsize=(5, 3))
sns.countplot(data=new_df, x=col, hue='Transported', ax=ax, color='pink')
df_test = new_df[new_df['Transported'].isnull()]
df_train = new_df[~new_df['Transported'].isnull()]
df.drop('Cabin', axis=1, inplace=True)
data.drop('Cabin', axis=1, inplace=True)
X = df_train.drop('Transported', axis=1)
y = df_train['Transported']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.33, shuffle=True)
from sklearn.tree import DecisionTreeClassifier
y = df['Transported']
features = ['Destination', 'CryoSleep', 'HomePlanet', 'VIP']
X = pd.get_dummies(df[features])
X_test = pd.get_dummies(data[features])
model = DecisionTreeClassifier(max_depth=7)
model.fit(X, y)
from sklearn import tree
plt.figure(figsize=(50, 5))
tree.plot_tree(model, filled=True) | code |
129029092/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data.shape | code |
129029092/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
df.shape
data.shape
df.drop('PassengerId', axis=1)
df.drop('Name', axis=1, inplace=True)
data.drop('PassengerId', axis=1)
data.drop('Name', axis=1, inplace=True)
data.isnull().sum() | code |
129029092/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
df.shape
data.shape
df.drop('PassengerId', axis=1)
df.drop('Name', axis=1, inplace=True)
data.drop('PassengerId', axis=1)
data.drop('Name', axis=1, inplace=True)
df.isnull().sum()
data.isnull().sum()
new_df = pd.concat([df, data])
new_df.head() | code |
129029092/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
df.shape
data.shape
df.drop('PassengerId', axis=1)
df.drop('Name', axis=1, inplace=True)
data.drop('PassengerId', axis=1)
data.drop('Name', axis=1, inplace=True)
df.isnull().sum() | code |
129029092/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
df.shape
data.shape
df.drop('PassengerId', axis=1)
df.drop('Name', axis=1, inplace=True)
data.drop('PassengerId', axis=1)
data.drop('Name', axis=1, inplace=True)
df.isnull().sum()
data.isnull().sum()
new_df = pd.concat([df, data])
new_df.shape
columns = ['CryoSleep', 'Destination', 'VIP', 'HomePlanet']
for col in columns:
fig, ax = plt.subplots(figsize=(5, 3))
sns.countplot(data=new_df, x=col, hue='Transported', ax=ax, color='pink') | code |
129029092/cell_27 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
df.shape
data.shape
df.drop('PassengerId', axis=1)
df.drop('Name', axis=1, inplace=True)
data.drop('PassengerId', axis=1)
data.drop('Name', axis=1, inplace=True)
df.isnull().sum()
data.isnull().sum()
df.drop('Cabin', axis=1, inplace=True)
data.drop('Cabin', axis=1, inplace=True)
data.columns | code |
129029092/cell_5 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
df.head() | code |
106210118/cell_9 | [
"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('../input/marketing-strategy-personalised-offer/sample.csv')
df1 = pd.read_csv('../input/marketing-strategy-personalised-offer/train_data.csv')
feature_list = df1.columns[:-1].values
label = [df1.columns[-1]]
df1.describe() | code |
106210118/cell_2 | [
"text_html_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
106210118/cell_11 | [
"text_html_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('../input/marketing-strategy-personalised-offer/sample.csv')
df1 = pd.read_csv('../input/marketing-strategy-personalised-offer/train_data.csv')
feature_list = df1.columns[:-1].values
label = [df1.columns[-1]]
df1.shape | code |
106210118/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/marketing-strategy-personalised-offer/sample.csv')
df1 = pd.read_csv('../input/marketing-strategy-personalised-offer/train_data.csv')
df1.head(10) | code |
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