Degree Name
MS (Master of Science)
Program
Mathematical Sciences
Date of Award
5-2025
Committee Chair or Co-Chairs
Jeff Knisley
Committee Members
Michele Joyner, Mostafa Zahed
Abstract
The 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 specifi- cations from 899 car models. Key factors such as mileage, engine power, and year of registration were analyzed for their correlation with car prices. Extensive 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. Model 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.
Document Type
Thesis - unrestricted
Recommended Citation
Abu 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
Copyright
Copyright by the authors.Yaman Abu Ghareebaih, 2025
Included in
Algebra Commons, Analysis Commons, Applied Mathematics Commons, Data Science Commons, Statistics and Probability Commons