Integrated Approach for Car Price Prediction Utilizing Random Forest Regression and Image Processing

Since the automobile industry has had quick growth in the last few years, it should have the most accurate and efficient systems related to forecasting the prices of cars so that the business can both cope with the market and meet the demands of the customers. As a result, this paper proposes an integrated approach based on car price prediction. It combines Random Forest Regression with an image processing approach. The approach is built on a database that has in it the numeric features of the vehicle and the visual features of the vehicle. Random Forest Regression is made use of as it is a very strong algorithm that can easily manage complex, high-dimensional data and image processing is used to tune the visual interfacing of cars. This combination is likely to increase the accuracy of the forecast and, at the same time, solves the problems that are often encountered in the models. The experimental data demonstrate the fact that the suggested strategy outperforms the stand-alone methods in terms of both the mean squared error and R-squared metrics. The model of hybrid concept presented in this article is a very innovative and practical solution for automobile industries as it is a mixture of machine learning as well as computer vision technology to produce a trustworthy tool for pricing estimation