USED CAR PRICE PREDICTION SYSTEM USING MACHINE LEARNING REGRESSION TECHNIQUES

A Used Car Price Prediction System using machine learning regression techniques aims to provide an accurate, data-driven approach for estimating the resale value of vehicles. The system leverages historical data such as car brand, model, manufacturing year, fuel type, transmission, mileage, engine capacity, and ownership history to train predictive models. Various regression algorithms, including Linear Regression, Decision Tree Regression, Random Forest Regression, and Gradient Boosting, are implemented and compared to identify the most effective model in terms of accuracy and robustness. Data preprocessing techniques such as handling missing values, encoding categorical variables, and feature scaling are applied to enhance model performance. The dataset is split into training and testing sets to evaluate the predictive capability using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared score. The system also incorporates feature importance analysis to identify key factors influencing car prices. By integrating the trained model into a user-friendly interface, users can input vehicle details and obtain real-time price predictions. This system benefits buyers and sellers by reducing uncertainty and improving transparency in the used car market. Overall, the proposed solution demonstrates how machine learning can be effectively applied to solve real-world pricing problems with improved accuracy and efficiency.