Predict credit default with Python using a dataset encompassing credit, demographic, and payment history features. Leverage 23 explanatory variables for accurate risk assessment. Dive into the Jupyter notebook for detailed exploration and prediction.
Amount of Credit: X1 (NT dollar)
Gender: X2 (1 = male; 2 = female)
Education: X3 (1 = graduate; 2 = university; 3 = high school; 4 = others)
Marital Status: X4 (1 = married; 2 = single; 3 = others)
Age: X5 (years)
Payment History: X6-X11 (-1 = pay duly; 1-9 = payment delays)
Bill Statements: X12-X17 (NT dollar)
Previous Payments: X18-X23 (NT dollar)