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September 17, 2025Open Access

Car Price Prediction by Machine Learning Approach

Key Points

Random forest regressor achieved the highest performance, with an R² score of 0.9832.
Mean absolute error was recorded at ₹61,682, emphasizing the model's predictive accuracy for used cars.
Preprocessing included handling missing values and scaling features, showcasing robust feature engineering techniques.
Results highlight a cost-effective method for fair valuation in the used car market, enhancing buyer-seller interactions.

Abstract

Abstract: Car price prediction is an important application of machine learning that helps buyers and sellers estimate the fair value of used cars. This study develops a car price prediction system leveraging regression-based machine learning algorithms, including Linear Regression, Random Forest Regressor, and Gradient Boosting. The dataset consists of car attributes such as brand, year of manufacture, mileage, fuel type, transmission, and ownership history. Preprocessing techniques such as handling missing values, encoding categorical variables, and scaling numerical features were applied. Experimental results demonstrate that the Random Forest Regressor achieved the highest performance with an R² score of 0.9832, Mean Absolute Error (MAE) of ₹61,682, and Root Mean Squared Error (RMSE) of ₹108,179. These results confirm that the proposed system provides reliable and accurate predictions, offering a lightweight, offline-friendly, and cost- effective alternative for the used car market.. Keywords: Car Price Prediction, Regression, Random Forest, Machine Learning, Feature Engineering

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