This project aims to predict the State of Charge (SOC) of lithium-ion batteries under various operating conditions using supervised machine learning models. By leveraging datasets from NASA's ...

It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning ...

Supervised latent variable regression methods such as partial least squares (PLS) and dynamic PLS have found wide applications in data analytics, quality prediction, and fault monitoring in various ...