Navigating Car Price Predictions: Unveiling Machine Learning Insights

This research study analyzes car price prediction utilizing machine learning models such as LightGBM, Random Forest, XGBoost, and CatBoost regressors with a comprehensive dataset containing diverse car attributes. Through rigorous evaluation, this study intends to identify the most accurate predictor. LightGBM emerges as a efficient model, achieving minimal Mean Squared Error (MSE) of 0.1 and Root Mean Squared Error (RMSE) of 0.316, closely followed by CatBoost. Additionally, the ensemble model demonstrates strong performance with the lowest MSE (0.08) and RMSE (0.282). These findings contribute to advancing machine learning applications in car valuation, providing valuable guidance to consumers in their purchasing decisions..