Machine Learning: Predicting Credit Defaulters
Data Set Information: This research aimed at the case of customers default payments in Taiwan
Features description:
- LIMIT_BAL: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit.
- SEX: Gender (1 = male; 2 = female).
- EDUCATION: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others).
- MARRIAGE: Marital status (1 = married; 2 = single; 3 = others).
- AGE: Age (year).
- PAY_0 - PAY_6: History of past payment. We tracked the past monthly payment records (from April to September, 2005) as follows: 0 = the repayment status in September, 2005; 1 = the repayment status in August, 2005; . . .; 6 = the repayment status in April, 2005. The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months; . . .; 8 = payment delay for eight months; 9 = payment delay for nine months and above.
- BILL_AMT1-BILL_AMT6: Amount of bill statement (NT dollar). X12 = amount of bill statement in September, 2005; X13 = amount of bill statement in August, 2005; . . .; X17 = amount of bill statement in April, 2005.
- PAY_AMT1-PAY_AMT6: Amount of previous payment (NT dollar).
- default payment next month: positive class: default | negative class: pay