text_html_output_1.png text_plain_output_1.png
import matplotlib.pyplot as plt

import pandas as pd

import seaborn as sns

import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)

cr = pd.read_csv('../input/Loan payments data.csv')

cr.profile_report(style={'full_width': True})

cr.fillna('0', axis=1, inplace=True)

cr.sample(10)

plt.figure(figsize=(15, 10))
sns.boxplot(data=cr, x='loan_status', y='age', hue='education', linewidth=2, order=['PAIDOFF', 'COLLECTION_PAIDOFF', 'COLLECTION'])
plt.show()