The model takes in 23 variables as an input and then produces a 1 if the model thinks they will default and a 0 if not.
The variables can be describers here:
X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit.
X2: Gender (1 = male; 2 = female).
X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others).
X4: Marital status (1 = married; 2 = single; 3 = others).
X5: Age (year).
X6 - X11: History of past payment. We tracked the past monthly payment records from the past 6 months as follows: X6 = the most recent repayment status; ;X11 = the repayment status from 6 months ago. 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.
X12-X17: Amount of bill statement (NT dollar for the past 6 months). X12 = amount of bill statement from the most recent bill; X17 = amount of bill statement from 6 months ago.
X18-X23: Amount of previous payment (NT dollar for the past 6 months). X18 = amount paid in the most recent month; X23 = amount paid in 6 months ago.
Example data set Input:
LIMIT_BAL | SEX | EDUCATION | MARRIAGE | AGE | PAY_0 | PAY_2 | PAY_3 | PAY_4 | PAY_5 | PAY_6 | BILL_AMT1 | BILL_AMT2 | BILL_AMT3 | BILL_AMT4 | BILL_AMT5 | BILL_AMT6 | PAY_AMT1 | PAY_AMT2 | PAY_AMT3 | PAY_AMT4 | PAY_AMT5 | PAY_AMT6 |
20000 | 2 | 2 | 1 | 24 | 2 | 2 | -1 | -1 | -2 | -2 | 3913 | 3102 | 689 | 0 | 0 | 0 | 0 | 689 | 0 | 0 | 0 | 0 |
120000 | 2 | 2 | 2 | 26 | -1 | 2 | 0 | 0 | 0 | 2 | 2682 | 1725 | 2682 | 3272 | 3455 | 3261 | 0 | 1000 | 1000 | 1000 | 0 | 2000 |