A reproducible ML pipeline for predicting NBA player performance.
Built an end-to-end Python data pipeline to ingest, validate, store, model, and evaluate NBA player-game data. The project uses leakage-safe rolling features, expanding-window cross-validation, serialized model artifacts, and automated tests in CI.