Background
- Time: Jun 2025
- Context: Machine learning project for loan risk modeling.
Highlights
- Built an end-to-end binary classification pipeline for loan default prediction, including preprocessing, feature selection, training, and evaluation.
- Benchmarked Random Forest, Bagging ensemble, and XGBoost under a unified experiment setting.
- Tuned XGBoost hyperparameters and selected it as the final model based on accuracy and training efficiency.
- Generated final predictions for the test set and documented model limitations with practical improvement directions.
Tech Stack
- Python
- Scikit-learn
- XGBoost
- Feature Engineering
- Binary Classification