Car Price Prediction Using Machine Learning Algorithms

This preprint presents a machine learning based model for predicting car prices using historical vehicle data. The study analyzes important features such as brand, model, year of manufacture, mileage, fuel type, transmission, and engine specifications that affect the resale value of cars. Multiple regression algorithms are implemented and compared to evaluate their predictive performance. Data preprocessing techniques including data cleaning, feature selection, and normalization are applied to improve accuracy. The results indicate that machine learning models can effectively capture the relationship between vehicle attributes and market price, making the approach useful for buyers, sellers, and automotive businesses. This work is shared as a preprint for academic and educational purposes.