Machine Learning Used Car Price Prediction with Random Forest Regressor Model

  • Bambang Kriswantara Universitas Nusa Mandiri
  • Rifki Sadikin LIPI Bandung Jawa Barat


The pandemic has stopped all activities, including the decline in the level of the world economy, this will reduce people's purchasing power. For those who want to buy a car, a used car can be the second option. In some used car sales showrooms, the main task is to determine price predictions based on historical data during previous transactions. The determinants of car prices are heavily influenced by several attributes in the car, for example: type of fuel, for example, km traveled and so on, this is what causes the price prediction process to take a long time. One of the roles in Macine Learning is being able to learn from previous transaction data and this will be a model that can be used to provide used car price predictions. Car price prediction is included in regression, which is looking for a strong relationship from the influence of variable X (predictor) to variable Y (target). In the prediction, of course, the reality data will not be right with the predicted data, for that in the measurement the model will look for the lowest error rate. The experimental results on the test data using the Random Forest Regressor model resulted in MAE = 1.006 and RMSE = 1.452 while the coefficient of determinant R2 = 0.89. And in the previous study with KNN [18], it produced an error rate of MAE = 2.01 and RMSE = 4.01 and the coefficient of determinant R2 = 0.85. While the comparison model uses Linear Regression, Ridge, Decision Tree and Gradient Boosting. Not all Machine Learning models are suitable for all data, for that it is necessary to choose the right machine learning model by experimenting with several models. And the lowest error level (MAE and RMSE) will be determined. The error values ​​for MAE and RMSE which are close to zero are close to the predicted value close to the actual value. On the other hand, if the error rate is very high, the prediction value is very far from the actual value.


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[1] Jafar Alzubi1, Anand Nayyar2, Akshi Kumar3"Machine Learning from Theory to Algorithms" doi:10.1088/1742-6596/1142/1/012012, Second National Conference on Computational Intelligence (NCCI 2018)
[2] Sofianita Mutalib; Nor Aina Azman; Shuzlina Abdul-Rahman "Predicting patients survival using supervised techniques"(DOI: 10.1109/HIS.2011.6122176)Publisher: IEEE,Date of Conference: 5-8 Dec. 2011
[3] Herdianto. 2013, Prediksi Kerusakan Motor Induksi Menggunakan Metode Jaringan Saraf Tiruan Backpropagation. Medan: Universitas Sumatera Utara.
[4] A. V. Joshi, Machine Learning and Artificial Intelligence. Cham: Springer International Publishing, 2020.
[5] Ashutosh Datt Sharma, Vibhor Sharma, “Used Car Price Prediction Using Linear Regression Model”, vol:02,Nov 2020, .irjmets).
[6] Ashutosh Datt Sharma, Vibhor Sharma, Sahil Mittal, Gautam Jain, Sudha Narang* “Predictive Analysis of Used Car Prices Using Machine Learning ”, vol:03,June 2021, .irjmets).
[7] Chuancan Chen, Lulu Hao, Cong Xu, “Comparative Analysis of Used Car Price Evaluation Models’, Conference Proceedings 1839, Mey 2017)
[8] Laveena D’Costa, Ashoka Wilson D’Souza, Abhijith K, Deepthi Maria Varghese, “Predicting True Value of Used Car using Multiple Linear Regression Model”, Volume-8, Issue-5S, January 2020
[9] Mehmet BILEN, “Predicting Used Car Prices with Heuristic Algorithms and Creating a New Dataset”,vol:6, 29-43, 2021
[10] Muhammad Asghar , Khalid Mehmood , Samina Yasin , Zimal Mehboob Khan, “Used Cars Price Prediction using Machine Learning with Optimal Features”, Volume: 4, Number: 2, Pages: 113- 119, Year: 2021
[11] Kanwal Noor, Sadaqat Jan, “Vehicle Price Prediction System using Machine Learning Techniques”,Volume 167 – No.9, June 2017
[12] Nabarun Pal, Dhanasekar Sundararaman, “How much is my car worth? A methodology for predicting used cars prices using Random Forest”, Future of Information and Communications Conference (FICC) 2018
[13] Prashant Gajera, Akshay Gondaliya, Jenish Kavathiya, “OLD CAR PRICE PREDICTION WITH MACHINE LEARNING”, Volume:03/Issue:03/March-2021
[14] K.Samruddhi ¸ Dr. R. Ashok Kumar, “Used Car Price Prediction using K-Nearest Neighbor Based Model”, (IJIRASE) Volume 4, Issue 3, DOI: 10.29027/IJIRASE.v4,i3.2020.686-689, September 2020.
[16] Siji George C G 1, “Grid Search Tuning of Hyperparameters in Random Forest Classifier for Customer Feedback Sentiment Prediction” , (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 11, No. 9, 2020
[17] Igo Cahya Negara, “ Penggunaan Uji Chi-Square untuk mengetahui pengaruh tingkat pendidikan dan Umur terhadap Pengetahuan Penasun mengenai HIV_AIDS di prop DKI”,prosiding Seminar Nasional Matematika dan Terapannya 2018 p-ISSN : 2550-0384; e-ISSN : 2550-0392
How to Cite
KRISWANTARA, Bambang; SADIKIN, Rifki. Machine Learning Used Car Price Prediction with Random Forest Regressor Model. JISICOM (Journal of Information System, Informatics and Computing), [S.l.], v. 6, n. 1, p. 40-49, june 2022. ISSN 2597-3673. Available at: <>. Date accessed: 02 feb. 2023. doi: