Credit Risk Classification Dataset

Context

This is Customer Transaction and Demographic related data , It holds Risky and Not Risky customer for specific banking products

Content

Dataset is small in nature , It helps budding data scientist 👨‍🔬 👩‍🔬& Data Analyst to experiment Machine Learning and Statistical modelling concept

Data:

payment_data.csv:

payment_data.csv: customer’s card payment history.
id: customer id
OVD_t1: number of times overdue type 1
OVD_t2: number of times overdue type 2
OVD_t3: number of times overdue type 3
OVD_sum: total overdue days
pay_normal: number of times normal payment
prod_code: credit product code
prod_limit: credit limit of product
update_date: account update date
new_balance: current balance of product
highest_balance: highest balance in history
report_date: date of recent payment

customer_data.csv:

customer’s demographic data and category attributes which have been encoded.
Category features are fea_1, fea_3, fea_5, fea_6, fea_7, fea_9.
label is 1, the customer is in high credit risk
label is 0, the customer is in low credit risk

Acknowledgements

Thanks to Google Datasets search

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

This dataset help to find out weather customer is Credit Risky or Credit Worthy in Banking perspective

Q1 - What are the factors contributing to Credit Risky customer ?
Q2 - Behaviour of Credit Worthy Customer ?