Analysis and Prediction of Electric Vehicle Cost Using Machine Learning

Electric Vehicles (EVs) are gaining significant attention as a sustainable alternative to conventional fuel-based vehicles. Despite technological advancements and government incentives, the high purchase cost of EVs remains a major challenge for widespread adoption. Accurate cost prediction can assist consumers, manufacturers, and policymakers in effective decision-making. This paper presents a machine learning–based approach for analyzing and predicting electric vehicle costs using technical specifications and market-related factors. Regression models such as Linear Regression, Decision Tree, Random Forest, and XGBoost are implemented and evaluated using performance metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R² score. The experimental results indicate that ensemble learning models provide superior prediction accuracy. The proposed system demonstrates the effectiveness of machine learning techniques in EV cost analysis and supports the growth of sustainable transportation systems.