Machine Learning Approaches to Forecasting Car Prices in the Secondary Market

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dc.contributor.authorDael, Fares A.
dc.contributor.authorTalipov, Daulet
dc.contributor.authorShayea, Ibraheem
dc.contributor.authorKamshat, Asmaganbetova
dc.date.accessioned2025-03-20T09:44:58Z
dc.date.available2025-03-20T09:44:58Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.descriptionIEEE MP Section; Institution of Electronics and Telecommunications Engineers (IETE)
dc.description16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024 -- 22 December 2024 through 23 December 2024 -- Indore -- 206392
dc.description.abstractThis study investigates the use of machine learning techniques to predict car prices in the secondary market. Utilizing a comprehensive dataset of used car listings from the United Kingdom, we applied advanced machine learning models, including Random Forest and Neural Networks, to understand the factors influencing car prices and to develop accurate predictive models. Our analysis identified engine size and registration year as key determinants of car prices. The Neural Network model provided highly accurate predictions, closely matching actual prices in the majority of cases. Visual representations of feature importance and prediction errors further elucidate the model's effectiveness. This research demonstrates that machine learning can significantly enhance the accuracy of price predictions in the used car market, offering valuable insights for consumers, dealers, and policymakers. By leveraging these predictive models, stakeholders can make more informed decisions, optimize pricing strategies, and better understand market dynamics. © 2024 IEEE.
dc.identifier.doi10.1109/CICN63059.2024.10847461
dc.identifier.endpage380
dc.identifier.isbn979-833150526-4
dc.identifier.scopus2-s2.0-85218013607
dc.identifier.scopusqualityN/A
dc.identifier.startpage373
dc.identifier.urihttps://doi.org/10.1109/CICN63059.2024.10847461
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2100
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofProceedings - 2024 IEEE 16th International Conference on Communication Systems and Network Technologies, CICN 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250319
dc.subjectCar Price Prediction
dc.subjectMachine Learning
dc.subjectNeural Networks
dc.subjectRandom Forest
dc.subjectRegression Models
dc.subjectSecondary Car Market
dc.titleMachine Learning Approaches to Forecasting Car Prices in the Secondary Market
dc.typeConference Object

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