Advancing Electric Vehicle Adoption: Insights from Predictive Analytics and Market Trends in Sustainable Transportation
Abstract
This study explores electric vehicle Exploratory Data Analysis (EDA) adoption through predictive analytics, employing advanced regression models like Random Forest and Lasso Regression to analyze market trends and predict EV selling prices. Our approach included comprehensive exploratory data analysis (EDA) to identify patterns in the EV market. Key findings demonstrate that Random Forest Regression notably outperforms other models in accuracy, providing crucial insights for EV manufacturers and marketers. The study highlights the importance of addressing challenges in EV range prediction and energy efficiency to boost consumer confidence and sustainable adoption. In summary, our research underscores the value of a data-driven approach in the EV sector, offering guidance for future strategies and innovations in sustainable transportation.