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The COVID-19 pandemic has disrupted the functioning of the car market in Turkey. Consequently, various innovative car-buying and selling practices, including online inspections, remote appraising, and others, have emerged. Machine learning implementation is particularly beneficial for predicting prices due to the easy writing of code. Therefore, the present paper concerned Random Forest Regressor, Linear Regression, Decision Tree, and XGBRegressor algorithms use in the determination of the car price. A dataset for the 2020 Turkish car sold market was considered in this paper. The research objectives for this project are the following: conducting the literature review related to machine learning and used car prices prediction and preprocessing x-determinants data; comparing target variables formatted to analyze techniques like Linear Regression, Decision Tree Regressor, Random Forest Regressor, and XGBRegressor; and deciding which model is more beneficial based on evaluation metrics. The hyperparameter tuning optimized the performance of 4 models. XGBRegressor shows the highest R2 score (93%) and the lowest (MSE, MAE, RMSE) among other models. Linear Regression, Decision Tree Regressor, Random Forest Regressor, respectively, performed an R2 score of (69%), (83%), and (91%). The results show the performance of machine learning models to provide an accurate car price prediction, while helping the customer to make good decisions within automobiles industries.

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Predicting Car Prices Using Machine Learning Algorithms

  • 会议论文

  • 发表日期：

    2025年

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  • Hayder Mohammedqasim,
  • Roa’a Mohammedqasem,
  • Bilal A. Ozturk,
  • Habsa Omar Ibrahim

摘要

The COVID-19 pandemic has disrupted the functioning of the car market in Turkey. Consequently, various innovative car-buying and selling practices, including online inspections, remote appraising, and others, have emerged. Machine learning implementation is particularly beneficial for predicting prices due to the easy writing of code. Therefore, the present paper concerned Random Forest Regressor, Linear Regression, Decision Tree, and XGBRegressor algorithms use in the determination of the car price. A dataset for the 2020 Turkish car sold market was considered in this paper. The research objectives for this project are the following: conducting the literature review related to machine learning and used car prices prediction and preprocessing x-determinants data; comparing target variables formatted to analyze techniques like Linear Regression, Decision Tree Regressor, Random Forest Regressor, and XGBRegressor; and deciding which model is more beneficial based on evaluation metrics. The hyperparameter tuning optimized the performance of 4 models. XGBRegressor shows the highest R2 score (93%) and the lowest (MSE, MAE, RMSE) among other models. Linear Regression, Decision Tree Regressor, Random Forest Regressor, respectively, performed an R2 score of (69%), (83%), and (91%). The results show the performance of machine learning models to provide an accurate car price prediction, while helping the customer to make good decisions within automobiles industries.