[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121411-en":3,"doc-seo-121411-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},121411,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning Approaches to Forecasting Car Prices in the Secondary Market","This study investigates machine learning methods for predicting car prices in the secondary market using a comprehensive dataset of used car listings from the United Kingdom. Random Forest and Neural Network models are trained to capture nonlinear relationships between vehicle attributes and sale prices. Results show engine size and registration year as key determinants, while the Neural Network model delivers highly accurate predictions that closely match observed prices in most cases. Feature-importance visuals and prediction-error analyses further validate model effectiveness, supporting data-driven decisions for consumers, dealers, and policymakers.","2024 IEEE 16th International Conference on Computational Intelligence and Communication Networks (CICN) ©2024 IEEE DOI: 10.1109/CICN63059.2024.10847461| 979-8-3315-0526-4/24/$31.00 |   \nMachine Learning Approaches to Forecasting Car Prices in the Secondary Market  \nFares A. Dael Management Information Systems İzmir Bakırçay University İzmir, Turkey [fares.dael@bakircay.edu.tr](fares.dael@bakircay.edu.tr)  \nIbraheem Shayea  \nElectronic and Communication Engineering Istanbul Technical University Istanbul, Turkey[shayea@itu.edu.tr](shayea@itu.edu.tr)  \nDaulet Talipov Computational and Data Science, Astana IT University Astana, Kazakhstan [231867@astanait.edu.kz](231867@astanait.edu.kz)  \nAsmaganbetova Kamshat Intelligent Systems and Cybersecurity Astana IT University Astana, Kazakhstan  \n[kamshat.asmaganbetova@astanait.edu.kz](kamshat.asmaganbetova@astanait.edu.kz)  \nAbstract— 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.  \nKeywords— Car Price Prediction, Machine Learning, Neural Networks, Random Forest, Secondary Car Market, Regression Models.  \nI. INTRODUCTION  \nThe automotive industry, a cornerstone of modern economies, relies heavily on the secondary car market to provide accessible mobility options for a substantial portion of the population [1]. In regions with developing automotive infrastructures, such as Kazakhstan, where the prevalence of older vehicles is significant [2], the secondary market becomes an even more critical component of the transportation ecosystem. The growing volume of used cars necessitates sophisticated analytical tools to accurately predict vehicle prices, benefiting both buyers and sellers[3] . Traditional econometric models have been employed to estimate car prices, but their limitations in capturing complex, nonlinear relationships between variables have been well-documented [4] . In contrast, machine learning  \n(ML) offers a powerful framework for uncovering hidden patterns and dependencies within large datasets [5], [6], [7],[8] . By leveraging ML algorithms, researchers can develop more accurate and robust predictive models for the dynamic secondary car market.  \nThis study aims to contribute to the growing body of literature on car price prediction by applying ML techniques to a comprehensive dataset of used car listings from the United Kingdom. By examining key factors influencing car prices, including those identified by previous research [9],[10], [11], [12], we seek to develop predictive models that outperform traditional methods. Our focus on Random Forest and Neural Networks aligns with recent studies demonstrating their effectiveness in similar contexts [13],[14] .  \nThrough rigorous data preprocessing and model evaluation, we aim to provide valuable insights for stakeholders in the automotive industry, particularly in emerging markets like Kazakhstan. Our findings can inform pricing strategies, inventory management, and consumer decision-making. By bridging the gap between academic research and in","cbCaifp7MbHGHL8C","https://ap.wps.com/l/cbCaifp7MbHGHL8C","pdf",846522,1,"English","en",105,"# Introduction\n## Research objectives\n# Literature Review\n## Traditional econometric approaches\n## Machine learning methods (SVR, Random Forest, Neural networks)","[{\"question\":\"What dataset and region does the study use for car price prediction?\",\"answer\":\"The study uses a comprehensive dataset of used car listings from the United Kingdom to train and evaluate machine learning models.\"},{\"question\":\"Which factors are identified as key determinants of car prices?\",\"answer\":\"Engine size and registration year are identified as key determinants influencing car prices in the secondary market.\"},{\"question\":\"Why do Random Forest and Neural Network models fit the secondary car price task?\",\"answer\":\"Random Forest can effectively handle numerical and categorical features, while Neural Networks can model complex nonlinear relationships, leading to accurate predictions in most cases.\"}]","Machine Learning Approaches to Forecasting Car Prices in the Secondary Market | PDF",1785735552,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"machine-learning-approaches-to-forecasting-car-prices-in-the-secondary-market","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/machine-learning-approaches-to-forecasting-car-prices-in-the-secondary-market/121411/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What dataset and region does the study use for car price prediction?","Question",{"text":74,"@type":75},"The study uses a comprehensive dataset of used car listings from the United Kingdom to train and evaluate machine learning models.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which factors are identified as key determinants of car prices?",{"text":79,"@type":75},"Engine size and registration year are identified as key determinants influencing car prices in the secondary market.",{"name":81,"@type":72,"acceptedAnswer":82},"Why do Random Forest and Neural Network models fit the secondary car price task?",{"text":83,"@type":75},"Random Forest can effectively handle numerical and categorical features, while Neural Networks can model complex nonlinear relationships, leading to accurate predictions in most cases.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]