Goal: To predict whether a person would default on their credit card payment based on different factors, such as salary, sex, and age.

• Used SVM and random forest machine learning models
• Modeled data with heatmaps and PCA/t-SNE dimensionality reduction

Awards: Capital One’s “Best Financial Hack” award at Hacklytics 2020

This research employed a binary variable, default payment (Yes = 1, No = 0), as the response variable. This study reviewed the literature and used the following 23 variables as explanatory variables:

• 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 April to September, 2005) as follows: X6 = the repayment status in September, 2005; X7 = the repayment status in August, 2005; . . .; X11 = 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.

• X12-X17: 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.

• X18-X23: Amount of previous payment (NT dollar). X18 = amount paid in September, 2005; X19 = amount paid in August, 2005; . . .;X23 = amount paid in April, 2005.