Car Price Prediction Using Machine Learning: Analyzing the DVM-CAR Dataset

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.