PREDICTING THE PRICE OF USED CARS USING MACHINE LEARNING TECHNIQUES

Abstract

The used car market has experienced significant growth due to increasing consumer demand for affordable transportation options and the rapid expansion of online automobile marketplaces. Determining the fair market value of a used car is a complex task influenced by various factors such as vehicle age, brand, model, mileage, fuel type, transmission, condition, and market trends. Traditional pricing methods often rely on manual assessment and subjective judgment, which may result in inaccurate valuations. This paper presents a machine learning-based framework for predicting the price of used cars using historical vehicle data and advanced predictive analytics techniques. The proposed system analyzes multiple vehicle attributes and employs machine learning algorithms to identify patterns and relationships affecting resale value. Data preprocessing, feature selection, and model optimization techniques are utilized to improve prediction accuracy and model performance. Experimental analysis demonstrates that machine learning models effectively estimate used car prices with high precision and reliability. The proposed framework supports buyers, sellers, dealers, and online automotive platforms in making informed pricing decisions and enhancing market transparency. The developed system provides an intelligent and scalable solution for automated used car valuation.