End-to-End R Machine Learning Workflow for Loan Default Prediction

Designed and implemented an end-to-end R machine learning pipeline for loan-default prediction. The workflow covers feature engineering, training multiple classification models, and evaluating performance with a particular focus on minimizing high-risk False Negatives — the costliest error type for a lender, since missing a true default is far more expensive than a false alarm.