Conversational Predictive Maintenance Assistant for EV Fleet

The goal is to prepare EV battery data for machine learning models by normalizing and converting time series data. Classical algorithms like Decision Trees and Random Forest are applied to classify battery health and predict remaining useful life. Deep learning models such as XGBoost and Stacking Classifiers enhance predictions. A conversational assistant using NLP will provide fleet managers actionable insights. System performance is tested with accuracy, precision, recall, and F1-score, and the solution is validated using publicly accessible databases.