Big data analytics on used car information

Abstract

In this research, a decision support system is implemented on a used car dataset. The main purpose is to predict the price information and reveal the related features. The price prediction problem is classified as a regression problem. The key point is to find the best-fitting model and obtain the best accurate prediction outcomes. Should we buy this car, or at what price may I sell my car? This work is about to answer these questions. Various regression models are compared, and detailed results are explained correspondingly. The constructed models will help customers to know about their car price and salability. And they can identify the buying opportunities. The percentage error approach which is detailed in the results section will be a guideline for customers/firms to make a market analysis or detect fraudulent listing information.