Car Prices Using Data Mining Techniques

Abstract

The study aims to examine the effectiveness of the XGBoost regression algorithm and compare it with the Random Forest (RF) algorithm, the Support Vector regression (SVR) algorithm, and the Deep Neural Networks (DNN) algorithm to predict car prices in the United Kingdom, the study found that the Random Forest (RF) algorithm is appropriate and effective for accurately estimating car prices, and therefore can be relied upon to improve and rationalize decisions of car buyers and seller, This conclusion is based on the RF algorithm achieving the highest coefficient of determination (R²) of 95.90 % and the lowest Root Mean Squared Error (RMSE) of 1946.07, compared to the XGBoost regression algorithm, the Support Vector regression (SVR) algorithm, and the Deep Neural Networks (DNN) algorithm