Machine Learning Approaches to Forecasting Car Prices in the Secondary Market
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This 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.
Açıklama
IEEE MP Section; Institution of Electronics and Telecommunications Engineers (IETE)
16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024 -- 22 December 2024 through 23 December 2024 -- Indore -- 206392
16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024 -- 22 December 2024 through 23 December 2024 -- Indore -- 206392
Anahtar Kelimeler
Car Price Prediction, Machine Learning, Neural Networks, Random Forest, Regression Models, Secondary Car Market
