Loan Defaulter Credit Risk — Imbalanced Classification Study
A two-notebook study on the Home Credit Default Risk dataset — the canonical real-world imbalanced classification problem (91% non-defaulters vs 9% defaulters). The interesting question wasn’t “can a model hit 99% accuracy” (a constant predict(0) already does that — and is useless). It was: for a problem where the minority class is the one that matters, which combination of techniques actually moves the needle? The work splits into a companion EDA notebook (missing-value handling, feature engineering, demographic-level defaulter analysis) and this modeling notebook. ...