Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Shakti Dheerays, S.; Gopakumar, Aparna; Keerthana, B.; Kokatnoor, Sujatha Arun Title A name given to the resource AI-Powered Wheels: Machine Learning Approaches for Predicting Used Car Prices Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1374 LNNS;pp.273-287 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-5732-2_21" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-5732-2_21</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105020568988?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020568988?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource 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 Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981965731-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online