Hyperparameter Tuning of Machine Learning Model for Price Prediction of Electric Vehicles

This research study presents a price prediction model for electric vehicles (EVs) by leveraging multiple features, including acceleration, top speed, range, and efficiency. The model is developed using a comprehensive dataset encompassing information from various EV manufacturers. Hyperparameter tuning is integral to this approach, enabling optimizing the model's performance. The price prediction of EVs with fair precision is achieved by employing machine learning techniques with rigorous tuning. Several tests have been conducted on a dedicated dataset for model evaluation resulting in arriving at good performance metrics. Valuable insights can be drawn from the model for forecasting EV prices, benefiting both potential buyers and manufacturers. It can serve as a practical tool for informed decision-making, aiding buyers in assessing the affordability of EVs and enabling manufacturers to set competitive prices in the market.