A Comparative Study of Linear Regression and Random Forest Models for Predicting Used Car Prices

DOI

10.2991/978-2-38476-585-0_18How to use a DOI?

Keywords

Used Car Price Prediction; Linear Regression; Random Forest Regression

Abstract

This study deeply analyzed the problem of used car price prediction based on machine learning methods. By constructing two models, linear regression and random forest, and comparing their prediction performance, the essential influence of model structure on price prediction accuracy and generalization ability was explored. The study used public data sets for strict data preprocessing and feature engineering. The results showed that the random forest model was significantly better than linear regression in prediction accuracy, which was particularly prominent in the scatter plot of actual and predicted prices. At the same time, through the feature importance analysis of random forests, it was found that the number of engine cylinders and fuel type have a key impact on vehicle pricing, which further confirmed the ability of random forests to effectively capture nonlinear features. Although there is a certain skewness in the residual distribution of random forests, it is suggested that advanced models such as gradient boosting trees and external data can be further introduced in the future to improve prediction accuracy and robustness.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Chiyu Zhou
PY  - 2026
DA  - 2026/06/18
TI  - A Comparative Study of Linear Regression and Random Forest Models for Predicting Used Car Prices
BT  - Proceedings of the 2025 International Conference on Hybrid Commerce, Human Capital, and Economic Dynamics (ICHCH 2025)
PB  - Atlantis Press
SP  - 152
EP  - 160
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-2-38476-585-0_18
DO  - 10.2991/978-2-38476-585-0_18
ID  - Zhou2026
ER  -