[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127216-en":3,"doc-seo-127216-105":30,"detail-sidebar-cat-0-en-105":92},{"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":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127216,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",8,"Research & Report","Car Price Prediction Using Machine Learning - Analyzing the DVM-CAR Dataset","Car Price Prediction Using Machine Learning focuses on predicting car prices with the DVM-CAR dataset, which contains more than 1.4 million images and car specifications covering 899 car models. The study examines correlations between price and key factors including mileage, engine power, and registration year. Data preprocessing includes filling missing values, detecting outliers, and normalizing numerical variables. Categorical attributes such as make and body type are encoded with one-hot encoding. Multiple Logistic Regression is used to assess linear relationships, while Random Forest models capture nonlinear patterns. Performance is measured using MSE and MSEP for fit and generalization.","East Tennessee State University  \nDigital Commons @ East Tennessee State University  \n\n| Electronic Theses and Dissertations | Student Works |\n| --- | --- |\n| 5-2025\u003Cbr>Car Price Prediction Using Machine Learning: Analyzing the DVM-CAR Dataset\u003Cbr>Yaman Abu Ghareebaih\u003Cbr>East Tennessee State University\u003Cbr>Follow this and additional works at: [https://dc.etsu.edu/etd](https://dc.etsu.edu/etd)\u003Cbr> Part of the Algebra Commons, Analysis Commons, Applied Mathematics Commons, Data Science Commons, and the Statistics and Probability Commons |  |\n\nRecommended Citation  \nAbu Ghareebaih, Yaman, \"Car Price Prediction Using Machine Learning: Analyzing the DVM-CAR Dataset\"(2025) . Electronic Theses and Dissertations. Paper 4533. [https://dc.etsu.edu/etd/4533](https://dc.etsu.edu/etd/4533)  \n[This Thesis-unrestricted is brought to you for free and open access by the Student Works at Digital Commons @](This Thesis-unrestricted is brought to you for free and open access by the Student Works at Digital Commons @)[ ](This Thesis-unrestricted is brought to you for free and open access by the Student Works at Digital Commons @)[East Tennessee State University. It has been accepted for inclusion in Electronic Theses and Dissertations by an](East Tennessee State University. It has been accepted for inclusion in Electronic Theses and Dissertations by an)[ ](East Tennessee State University. It has been accepted for inclusion in Electronic Theses and Dissertations by an)[authorized administrator of Digital Commons @ East Tennessee State University. For more information](authorized administrator of Digital Commons @ East Tennessee State University. For more information), please [contact](contact digilib@etsu.edu)[ digilib@etsu.edu](contact digilib@etsu.edu).  \nCar Price Prediction Using Machine Learning: Analyzing the DVM-CAR Dataset  \nA thesis  \npresented to  \nthe faculty of the Department of Mathematics East Tennessee State University  \nIn partial fulfillment of the requirements for the degree  \nMaster of Science in Mathematical Sciences  \nby  \nYaman Abu Ghareebaih  \nMay 2025  \nJe↵ Knisley, Ph.D. , Chair  \nMichele Joyner, Ph.D.  \nMostafa Zahed, Ph.D.  \nKeywords: DVM-CAR dataset, car price prediction, machine learning  \nABSTRACT  \nCar Price Prediction Using Machine Learning: Analyzing the DVM-CAR Dataset  \nby  \nYaman Abu Ghareebaih  \nThe objective of this study is to predict car prices using machine learning models and the DVM-CAR dataset, which includes over 1 .4 million images and car specifications from 899 car models. Key factors such as mileage, engine power, and year of registration were analyzed for their correlation with car prices.  \nExtensive data cleaning was performed, including filling missing values, identifying outliers, and normalizing numerical variables. Discrete variables like car make and body type were encoded using one-hot encoding. Linear relationships were analyzed with Multiple Logistic Regression, and Random Forest models were used for nonlinear patterns.  \nModel performance was evaluated using Mean Squared Error (MSE) to assess fit and Mean Squared Error of Prediction (MSEP) to test generalization. The findings enable the development of an automated system for car price estimation, benefiting both buyers and sellers.  \nCopyright 2025 by Yaman Abu Ghareebaih All rights reserved.  \n3  \nACKNOWLEDGMENTS  \nI would like to thank the Almighty God for His grace towards me throughout this journey. I would like to express my profound gratitude to my able supervisor, Dr. Je↵ Randall Knisley, for his immense support and guidance. His consistent advice, direction, and patience towards me are deeply appreciated. I would like to also thank Dr. Michelle Joyner and Dr.Mostafa Zahed for accepting to be part of my committee. I also want to thank my family for their immense support.  \nTABLE OF CONTENTS  \nABSTRACT .................................. 2  \nACKNOWLEDGMENTS ........................... 4  \nLIST OF TABLES ............................... 11  \nL","cbCaijAgn7QCBnRh","https://ap.wps.com/l/cbCaijAgn7QCBnRh","pdf",27338311,1,268,"English","en",105,"# ABSTRACT\n# ACKNOWLEDGMENTS\n# LIST OF TABLES\n# LIST OF FIGURES\n# 1 INTRODUCTION\n## 1.1 Background of the Study\n## 1.2 Review of Related Literature\n## 1.3 Problem Statements\n## 1.4 Research Objectives\n## 1.5 Research Questions\n## 1.6 Significance of the Study\n## 1.7 Scope of the study\n## 1.8 Thesis Organization\n# 2 SURVEY OF LITERATURE AND RESEARCH GAPS\n## 2.1 Data Preprocessing and Predictive Modeling Pipeline for Car Prices\n## 2.4 The DVM-CAR Dataset\n## 2.6 Data Collection and Processing\n## 2.7 Feature Engineering and Selection\n## 2.8 Machine Learning Methods\n# 3 METHODOLOGY, RESULTS AND DISCUSSION\n## 3.2 Data Description and Preprocessing\n## 3.3 Feature Engineering","[{\"question\":\"What is the primary goal of the study?\",\"answer\":\"To predict car prices using machine learning models based on the DVM-CAR dataset and to identify how factors such as mileage, engine power, and registration year relate to price.\"},{\"question\":\"How was the dataset prepared before modeling?\",\"answer\":\"The study performed data cleaning by filling missing values, identifying outliers, and normalizing numerical variables; it also applied one-hot encoding for discrete attributes like car make and body type.\"},{\"question\":\"Which modeling approaches and evaluation metrics were used?\",\"answer\":\"Multiple Logistic Regression was used for linear relationships, while Random Forest models addressed nonlinear patterns. Model quality was assessed with Mean Squared Error (MSE) and Mean Squared Error of Prediction (MSEP).\"}]","Car Price Prediction Using Machine Learning - Analyzing the DVM-CAR Dataset | PDF",1785937582,675,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"car-price-prediction-using-machine-learning-analyzing-the-dvm-car-dataset","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/car-price-prediction-using-machine-learning-analyzing-the-dvm-car-dataset/127216/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the primary goal of the study?","Question",{"text":76,"@type":77},"To predict car prices using machine learning models based on the DVM-CAR dataset and to identify how factors such as mileage, engine power, and registration year relate to price.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How was the dataset prepared before modeling?",{"text":81,"@type":77},"The study performed data cleaning by filling missing values, identifying outliers, and normalizing numerical variables; it also applied one-hot encoding for discrete attributes like car make and body type.",{"name":83,"@type":74,"acceptedAnswer":84},"Which modeling approaches and evaluation metrics were used?",{"text":85,"@type":77},"Multiple Logistic Regression was used for linear relationships, while Random Forest models addressed nonlinear patterns. 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