Performance Comparison of Random Forest and XGBoost Algorithms for Used Car Price Prediction Using a Kaggle Dataset

Abstract

Accurate prediction of used car prices plays an essential role in supporting fair vehicle valuation and informed decision-making within the automotive market. This study aims to compare the performance of the Random Forest and XGBoost algorithms for predicting used car prices using a publicly available Kaggle dataset. The proposed methodology includes data collection, data preprocessing, feature encoding, model training, and model evaluation. The dataset is divided into training and testing subsets to ensure an objective assessment of predictive performance. Both algorithms are evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R² Score). The comparative analysis is intended to identify the algorithm that provides superior prediction accuracy and better generalization capability. The findings of this research are expected to contribute to the development of reliable machine learning models for used car price prediction and provide valuable insights for buyers, sellers, and automotive businesses.