How much is my car worth? A methodology for predicting used cars prices using Random Forest

TL;DR

This paper presents a Random Forest-based model for predicting used car prices by analyzing vehicle features, achieving high accuracy and aiding sellers in setting realistic prices.

Contribution

It introduces a supervised learning approach using Random Forest with feature analysis for accurate used car price prediction.

Findings

Training accuracy of 95.82%

Testing accuracy of 83.63%

Effective feature selection improves prediction

Abstract

Cars are being sold more than ever. Developing countries adopt the lease culture instead of buying a new car due to affordability. Therefore, the rise of used cars sales is exponentially increasing. Car sellers sometimes take advantage of this scenario by listing unrealistic prices owing to the demand. Therefore, arises a need for a model that can assign a price for a vehicle by evaluating its features taking the prices of other cars into consideration. In this paper, we use supervised learning method namely Random Forest to predict the prices of used cars. The model has been chosen after careful exploratory data analysis to determine the impact of each feature on price. A Random Forest with 500 Decision Trees were created to train the data. From experimental results, the training accuracy was found out to be 95.82%, and the testing accuracy was 83.63%. The the model can predict the price of cars accurately by choosing the most correlated features.