text_plain_output_1.png
from sklearn.impute import SimpleImputer

import numpy as np

import pandas as pd

payments = pd.read_csv('../input/credit-risk-classification-dataset/payment_data.csv')
payments = payments.set_index('id')
customers = pd.read_csv('../input/credit-risk-classification-dataset/customer_data.csv')
customers = customers.set_index('id')
customer_data = customers.join(payments)
customer_data

customer_data.isnull().sum(axis=0)

customer_data = customer_data.drop(['prod_limit', 'report_date', 'update_date', 'prod_code', 'fea_1', 'fea_3', 'fea_5', 'fea_6', 'fea_7', 'fea_9'], axis=1)

data = customer_data.to_numpy(na_value=np.nan).astype(float)
imputer = SimpleImputer(verbose=1)
data = imputer.fit_transform(data)
data