Forecasting Pre-Owned Car Prices Using Machine Learning

Abstract

Over 70 million passenger cars were produced in 2016 shows that automotive manufacturing has been steadily rising during the previous ten years. As a result, the market for used vehicles was created, and it has since flourished as a separate industry. With the advent of online marketplaces, it is now simpler for buyers and sellers to comprehend the current patterns influencing the market value of worn cars. The manufacture of second-hand cars has been gradually rising due to the epidemic. Making the proper decisions while purchasing an automobile is crucial. Many internet portals are accessible to help sellers and buyers discover the market worth of used cars. Utilizing the internet, customers may quickly comprehend used car pricing. For customers, we may export used automobiles. Because of Covid, this car marketing is thriving in India right now. The project’s main crisp is moving forward with multiple machine learning models that will, without vagueness, forecast the price of the used car based on specific parameters. The buyer and seller will utilize web resources to learn about market pattern recognition for used cars. A number of strategies are employed in concert to establish a forecasting model that predicts the cost of a cast-off car. Application of Neural Network Models like penalized models, linear Regression, and Regression Trees. We’ll try to devise synthetic data that can predict the cost of a used automobile based on prior customers’ info and set of indicators. We can anticipate new outcomes using prior data from customers, and we can compare the predicted results to identify the best one.