Predicting the resale price of used cars is a critical problem in the automotive industry, affecting buyers, sellers, and online marketplaces. This study presents a machine learning-based approach to predict the resale price of used cars in India using features such as brand, manufacturing year, kilometers driven, fuel type, transmission, and number of previous owners. Two regression algorithms are compared: Linear Regression and Random Forest Regressor. The dataset comprises 200 records generated based on realistic Indian used car market price ranges. The models are evaluated using standard metrics including R² Score, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and prediction accuracy. Experimental results demonstrate that Random Forest Regressor achieves a superior accuracy of 95.42% (R² = 0.9542) compared to Linear Regression at 89.18% (R² = 0.8918), owing to its ability to capture non-linear relationships such as depreciation curves and brand-specific pricing. The proposed system provides a reliable, data-driven framework for resale price estimation that can assist buyers, sellers, and dealers in making informed decisions.

Keywords: Machine Learning, Linear Regression, Random Forest, Used Car Price Prediction, Regression Analysis, Vehicle Resale Value.