Medicine Supply Chain Disruption Predictor Built

Medicine Supply Chain Disruption Predictor

Built a production-ready ML system that predicts drug shortage risk using real FDA data. Collected and cleaned 1703 real drug shortage records, trained an XGBoost model, and deployed it as a live REST API — accessible to anyone worldwide.

The biggest challenge was identifying and removing 6 sources of data leakage that were causing fake 100% accuracy. After fixing this, the model delivers honest, generalisable predictions.

The entire system is containerised with Docker, automatically rebuilt and redeployed via a Jenkins CI/CD pipeline on every code push, and visualised through an interactive Power BI dashboard.

Result: A complete ML + DevOps project — from raw data to live deployed API — built independently in under 2 weeks.

Live API: https://medicine-supply-predictor.onrender.com/docs

GitHub: https://github.com/nehaM906/medicine-supply-predictor