End-to-End ML Pipeline with Deployment

Summary

A complete classical ML project — from raw data to a live, monitored API — demonstrating you can own the full lifecycle, not just the modeling notebook.

• 1Pick a real problem, source real data, and build data → training → deployment → monitoring.
• 2Deploy it publicly with a simple frontend or API docs page.
• 3Write up your modeling decisions and trade-offs in a README.