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Used Car Price Prediction Using Machine Learning

D. NANDHINI., MCA

(Assistant professor, Department of Master of Computer Applications)

J. VELAVADASAN., MCA

Christ College of Engineering and Technology

Moolakulam, Oulgaret Municipality, Puducherry – 605010

Abstract—The rapid growth of the automobile resale market has increased the demand for accurate and transparent pricing of used vehicles. However, determining the fair resale value of a used car is a complex task influenced by multiple factors such as brand, model, year of manufacture, mileage, fuel type, transmission type, and ownership history. Traditional pricing methods rely heavily on manual evaluation and subjective judgment, which often results in inconsistent and inaccurate pricing [1], [3]. To address these limitations, this project proposes a Machine Learning–based Used Car Price Prediction System that provides automated and reliable price estimation.

The proposed system utilizes historical used car data and applies regression-based machine learning techniques to analyze patterns that affect car prices [5], [9]. Data preprocessing and feature extraction are performed to improve model accuracy, and prediction results are generated through a web-based interface developed using Flask. Experimental results demonstrate that the system achieves high prediction accuracy and responds efficiently to user inputs. This approach reduces human bias, improves transparency, and assists buyers and sellers in making informed decisions in the used car market [7], [14].

Keywords—Used Car Price Prediction, Machine Learning, Supervised Learning Algorithms, Regression Models, Data Preprocessing and Feature Engineering, Automotive Data Analytics, Predictive Modeling in Automobile Industry, Vehicle Resale Value Estimation, Market Price Analysis, Price Forecasting Systems, Machine Learning in Automotive Applications, Data-Driven Decision Support Systems, Real-Time Price Prediction, Web-Based Predictive Systems, Evaluation Metrics for Regression Models, Intelligent Pricing Systems.