This repository contains code for analyzing battery data from NASA's battery testing dataset. The analysis involves processing battery impedance, electrolyte resistance, and charge transfer resistance across charge/discharge cycles to track the aging and performance of various batteries.

The project analyzes battery cycling data to predict degradation patterns and performance metrics using both deep learning (LSTM) and traditional machine learning (XGBoost) approaches. The implementation enables accurate estimation of battery health, which is crucial for battery management systems in various applications.