Machine Learning Model for Prediction of Electric Vehicle Prices

Abstract

This research paper explores the use of machine learning models for predicting the price of electric vehicles (EVs), focusing on improving forecast accuracy and understanding market dynamics. The growing demand for EVs, driven by environmental concerns, technological advancements, and government incentives, underscores the need for accurate price prediction to support decision-making by businesses, policymakers, and consumers. The research applies and compares the performance of three machine learning models — Linear Regression, Decision Tree, and Random Forest — using a dataset of electric vehicles registered with the Washington State Department of Licensing. Results demonstrate that Random Forest outperforms other models in terms of predictive accuracy, highlighting its capacity to handle difficult, non-linear relationships and reduce variance. The study provides valuable insights for improving market strategies and enhancing the adoption of electric vehicles.