AI-Powered Wheels: Machine Learning Approaches for Predicting Used Car Prices
subject: Automotive market insights; Gradient boosting machines (GBM); Linear regression; Logistic regression; Machine learning models; Predictive analytics; Random forest; Support vector machine (SVM); Used car price prediction
description: Predictive analytics is now an essential tool for dealers, buyers, and sellers due to the used car markets increasing need for precise pricing models. This study compares the capability of Logistic Regression, Random Forest, Linear Regression, Support Vector Machine (SVM), and Gradient Boosting Machines (GBM) for predicting used car pricing. The results demonstrate that Random Forest and Gradient Boosting scored the best accuracy (87%), with Random Forest also demonstrating better precision (90%). Logistic and Linear Regression both achieved comparable accuracy of 85%, with precision scores of 88% and 89%, respectively. SVM, while significantly less accurate (83%) and precise (86%), produced comparable results for high-dimensional data. In terms of training time, Linear Regression (0.0089 seconds) and Logistic Regression (0.0094 seconds) were the fastest, whereas Gradient Boosting (0.8312 seconds) and Random Forest (0.4766 seconds) took much longer. These results demonstrate a trade-off between model complexity, accuracy, and computing efficiency, with simpler models performing better in terms of speed and ensemble models doing better in terms of prediction accuracy. This study presents practical insights to help stakeholders choose machine learning models for predicting used car prices depending on their specific requirements. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
creator: Shakti Dheerays, S.; Gopakumar, Aparna; Keerthana, B.; Kokatnoor, Sujatha Arun
source: Lecture Notes in Networks and Systems;Volume;1374 LNNS;pp.273-287
publisher: Springer Science and Business Media Deutschland GmbH
date: 01-01-2026
rights: Restricted Access; Hardcopy may be available in the library
relation: ISSN: 23673370; ISBN: 978-981965731-5;
language: English
type: Conference paper
coverage: Shakti Dheerays S., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Gopakumar A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Keerthana B., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India