A logical, reasonably standardized, but flexible project structure for conducting ml research 🍪

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 Python library for building local web apps to manually classify images into custom categories - perfect for preparing ML training datasets.

🔋 Detect and analyze irreversible degradation thresholds in batteries, enhancing health analytics and extending battery life through informed decision-making.