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

  • Nabarun Pal
  • Priya Arora
  • Puneet Kohli
  • Dhanasekar Sundararaman
  • Sai Sumanth Palakurthy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 886)


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.


Car price prediction Random forests Regression Decision trees 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nabarun Pal
    • 1
  • Priya Arora
    • 2
  • Puneet Kohli
    • 2
  • Dhanasekar Sundararaman
    • 3
  • Sai Sumanth Palakurthy
    • 4
  1. 1.Department of Metallurgical and Materials EngineeringIndian Institute of Technology RoorkeeRoorkeeIndia
  2. 2.Department of Computer ScienceTexas A&M UniversityCollege StationUSA
  3. 3.Department of Information TechnologySSN College of EngineeringChennaiIndia
  4. 4.Department of Computer Science and EngineeringIIT (ISM) DhanbadDhanbadIndia

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