Using machine learning to generate predictions based on the information extracted from automobile ads

Abstract

In this paper we address the issue of predicting the reselling price of cars based on ads extracted from popular websites for reselling cars. To obtain the most accurate predictions, we have used two machine learning algorithms (multiple linear regression and random forest) to build multiple models to reflect the importance of different combinations of features in the final price of the cars. The predictions are generated based on the models trained on the ads extracted from such sites. The developed system provides the user with an interface that allows navigation through ads to assess the fairness of prices compared to the predicted ones.