How Much Is My Car Worth? A Methodology for Predicting Used Cars’ Prices Using Random Forest

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 model can predict the price of cars accurately by choosing the most correlated features.

Analysis of Random Forest, Multiple Regression, and Backpropagation Methods in Predicting Apartment Price Index in Indonesia

“…This model produces an accuracy of 0.977 or 97.7%. This value is excellent as the accuracy measurement carried out by previous studies, one of which is stated in research [23] which has an R2 score on data testing of 83.63%.…”

İkinci El Araba Fiyatlarının Tahmini

“…In the study, he used 252645 vehicle data and 139 variables and achieved a success rate of approximately 66% [28]. Pal et al (2018) used Random Forest, a controlled learning method to estimate the price of used cars. A Random Forest with 500 Decision Trees was created to train the data.…”

“…From the test results, it was found that the accuracy of the training was 95.82% and the test accuracy was 83.63%. The model was able to accurately estimate the price of the cars by choosing the most suitable features [17]. Noor and Jan (2017) offer a vehicle price forecasting system using the supervised machine learning technique in their articles.…”

“…Indeed, it has been found that many features such as the front view camera, the brand, model of the car, age, traction, mileage, gear, fuel type, torque, width, fuel tank volume, ABS, panoramic glass roof, rear window defroster, power steering, start / stop, sunroof, cooled torpedo affect the price of the car [16]. Pal et al (2018) used Random Forest, a controlled learning method to estimate the price of used cars. The model was chosen after careful exploration data analysis to determine the effect of each feature on the price.…”

Predicting True Value of Used Car using Multiple Linear Regression Model

“…In the model proposed by Nabarun Pal et al [2], factors such as age of the car, its make, the origin of the car (original country of the manufacturer), its mileage (number of kilometers has run) and its horsepower were considered as the attributes. Fuel price is also given great importance.…”