An interpretable battery health engine that detects hidden points of no return instead of just predicting health %. It models stress, buffer, and degradation intensity, discovers Stable/Drifting/Irreversible regimes via GMM, and learns simple Decision Tree thresholds, with a Streamlit app for diagnostics and what-if scenarios.

A long-form systems essay arguing that machine learning fails when used as an automated decision-maker in unstable environments. It reframes ML as an early-warning instrument that exposes pressure, instability, and shrinking intervention windows, preserving human judgment instead of replacing it with late, brittle decisions.