XGBoost for Credit Approval

Summary

A credit-approval classification project using gradient-boosted decision trees, with a reported test accuracy of 87.69%.

Context

The project uses tree-based supervised learning for a practical credit-decision classification task.

Main contributions

• Trains an XGBoost model for credit approval.
• Evaluates out-of-sample classification performance.
• Reports 87.69% test accuracy in the course project.