An end-to-end machine learning project that predicts automotive parts shortages for a major automotive manufacturing and distribution company. This project showcases data preprocessing, exploratory analysis, feature engineering, and robust predictive modeling (including XGBoost) to drive actionable insights under a strict NDA.