Random forest and decision tree algorithms for car price prediction
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Published:
Sep 30, 2023
Keywords:
Car price prediction, Classification, Data mining, Decision tree, Machine learning, Random forest
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Computer Science
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Abstract
At this time in the era of cars that use renewable energy fuels such as electric cars which are highly supported by the government so that it has an impact on used cars based on these problems an analysis is needed. Determining whether or not the price of buying or selling a used car is appropriate is one of the obstacles faced by the community in making decisions when buying or selling a car or vehicle. Therefore, most people choose an alternative by buying a used car that is still good and usable. One way to make price predictions is to use the Machine Learning method. In this study the authors used random forest and decision tree methods to predict car prices. The results of the research on car price prediction analysis using the random forest and decision tree methods have different percentage results. Where using the random forest method there is an accuracy: 72.13% whereas with the analysis of the decision tree method accuracy: 67.21%. So it can be concluded that the Random Forest method has better analytical accuracy than the Decision Tree method.
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How to Cite
Putra, P. H. ., Azanuddin, A., Purba, B. ., & Dalimunthe, Y. A. . (2023). Random forest and decision tree algorithms for car price prediction. Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA), 4(1), 81–89. https://doi.org/10.54076/jumpa.v3i2.305
References
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Smarra, F., Di Girolamo, G. D., De Iuliis, V., Jain, A., Mangharam, R., & D’Innocenzo, A. (2020). Data-driven switching modeling for mpc using regression trees and random forests. Nonlinear Analysis: Hybrid Systems, 36, 100882.
Song, Y.-Y., & Ying, L. U. (2015). Decision tree methods: applications for classification and prediction. Shanghai Archives of Psychiatry, 27(2), 130.
Sotarjua, L. M., & Santoso, D. B. (2022). Perbandingan Algoritma Knn, Decision Tree,* Dan Random* Forest Pada Data Imbalanced Class Untuk Klasifikasi Promosi Karyawan. … Informatika Sains Dan …, 7(2), 192–200.
Used Cars Price Prediction using Supervised Learning Techniques. (2019). International Journal of Engineering and Advanced Technology, 9(1S3), 216–223. https://doi.org/10.35940/ijeat.a1042.1291s319
Waqar, M., Afridi, T. A., & Soomro, Q. (2023). Radiation levels of isolation rooms used by radio-iodine ablation patients during hospitalization at NORIN Nawabshah, Pakistan. International Journal of Basic and Applied Science, 11(4), 142–148.
Wu, L. C., Horng, J. T., Huang, H. Da, & Chen, W. L. (2008). Identifying discriminative amino acids within the hemagglutinin of human influenza A H5N1 virus using a decision tree. IEEE Transactions on Information Technology in Biomedicine, 12(6), 689–695. https://doi.org/10.1109/TITB.2008.896871
Yang, B.-S., Di, X., & Han, T. (2008). Random forests classifier for machine fault diagnosis. Journal of Mechanical Science and Technology, 22, 1716–1725.
Zhang, B. (2021). Tactical Decision System of Table Tennis Match based on C4.5 Decision Tree. Proceedings - 2021 13th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2021, 632–635. https://doi.org/10.1109/ICMTMA52658.2021.00146
Chandak, A., Ganorkar, P., Sharma, S., Bagmar, A., & Tiwari, S. (2019). Car Price Prediction Using Machine Learning. International Journal of Computer Sciences and Engineering, 7(5), 444–450. https://doi.org/10.26438/ijcse/v7i5.444450
Dutta, K. K., Sunny, S. A., Victor, A., Nathu, A. G., Ayman Habib, M., & Parashar, D. (2020). Kannada alphabets recognition using decision tree and random forest models. Proceedings of the 3rd International Conference on Intelligent Sustainable Systems, ICISS 2020, 534–541. https://doi.org/10.1109/ICISS49785.2020.9315972
East-West University, Institute of Electrical and Electronics Engineers, Institute of Electrical and Electronics Engineers. Bangladesh Section, & IEEE Robotics and Automation Society. Bangladesh Chapter. (n.d.). 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT 2019)?: May 3-5, 2019, Dhaka, Bangladesh.
Fauzi, C., Pribadi, D. S., & Alifi, M. R. (2023). Spatial data warehouse: An analysis in tourism sector of west java province. International Journal of Basic and Applied Science, 11(4), 160–169
Gajera, P., Gondaliya, A., & Kavathiya, J. (n.d.). OLD CAR PRICE PREDICTION WITH MACHINE LEARNING. In International Research Journal of Modernization in Engineering Technology and Science www.irjmets.com @International Research Journal of Modernization in Engineering.
Guo, Y., Zhou, Y., Hu, X., & Cheng, W. (2019). Research on recommendation of insurance products based on random forest. Proceedings - 2019 International Conference on Machine Learning, Big Data and Business Intelligence, MLBDBI 2019, 308–311. https://doi.org/10.1109/MLBDBI48998.2019.00069
Hasan Putra, P., Syahputra Novelan, M., & Rizki, M. (2022). Analysis K-Nearest Neighbor Method in Classification of Vegetable Quality Based on Color. Journal of Applied Engineering and Technological Science, 3(2), 126–132.
Hasibuan, E., Informasi, S., Ilmu, F., Informasi, T., Gunadarma, U., Margonda, J., No, R., Cina, P., & Jawa, D. (2022). Implementasi Machine Learning untuk Prediksi Harga Mobil Bekas dengan Algoritma Regresi Linear berbasis Web. Jurnal Ilmiah Komputasi, 21(4), 595–602. https://doi.org/10.32409/jikstik.21.4.3327
Jijo, B. T., & Abdulazeez, A. M. (2021). Classification based on decision tree algorithm for machine learning. Evaluation, 6(7).
Kriswantara, B., & Sadikin, R. (2022). Used Car Price Prediction with Random Forest Regressor Model. Journal of Information Systems, Informatics and Computing Issue Period, 6(1), 40–49. https://doi.org/10.52362/jisicom.v6i1.752
Mohandoss, D. P., Shi, Y., & Suo, K. (2021). Outlier Prediction Using Random Forest Classifier. 2021 IEEE 11th Annual Computing and Communication Workshop and Conference, CCWC 2021, 27–33. https://doi.org/10.1109/CCWC51732.2021.9376077
Pamuji, F. Y., & Ramadhan, V. P. (2021). Komparasi Algoritma Random Forest dan Decision Tree untuk Memprediksi Keberhasilan Immunotheraphy. Jurnal Teknologi Dan Manajemen Informatika, 7(1), 46–50. https://doi.org/10.26905/jtmi.v7i1.5982
Papageorgiou, E., & Stylios, C. (2006). A Combined Fuzzy Cognitive Map and Decision Trees Model for Medical Decision Making. Proceedings of the 28th IEEE EMBS Annual International Conference New York City, USA, 6117–6120. https://doi.org/10.1109/IEMBS.2006.260354
Prasad, A. M., Iverson, L. R., & Liaw, A. (2006). Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems, 9, 181–199.
Priyam, A., Abhijeeta, G. R., Rathee, A., & Srivastava, S. (2013). Comparative analysis of decision tree classification algorithms. International Journal of Current Engineering and Technology, 3(2), 334–337.
Putra, P. H., Hasibuan, A., & Marpaung, E. A. (2022). Analisis Klasifikasi Metode X-Means Pada Minat dan Bakat Anak Dimasa Pandemi. 19(2), 424–429.
Saadah, S., & Salsabila, H. (2021). Prediksi Harga Ponsel Menggunakan Metode Random Forest. Jurnal Komputer Terapan, 7(1), 24–32.
Sabri, M. A., Yahyaouy, A., Tairi, H., El Beqqali, O., Benali, H., Ja?mi?at Si?di? Muh?ammad ibn ?Abd Alla?h. Faculty of Sciences Dhar El Mahraz, IEEE Computer Society, & Institute of Electrical and Electronics Engineers. (n.d.). 2018 International Conference on Intelligent Systems and Computer Vision (ISCV)?: April 2-4, 2018, Faculty of Sciences Dhar El Mahraz (FSDM), Fez, Morocco.
Sarker, I. H., Colman, A., Han, J., Khan, A. I., Abushark, Y. B., & Salah, K. (2020). Behavdt: a behavioral decision tree learning to build user-centric context-aware predictive model. Mobile Networks and Applications, 25, 1151–1161.
Smarra, F., Di Girolamo, G. D., De Iuliis, V., Jain, A., Mangharam, R., & D’Innocenzo, A. (2020). Data-driven switching modeling for mpc using regression trees and random forests. Nonlinear Analysis: Hybrid Systems, 36, 100882.
Song, Y.-Y., & Ying, L. U. (2015). Decision tree methods: applications for classification and prediction. Shanghai Archives of Psychiatry, 27(2), 130.
Sotarjua, L. M., & Santoso, D. B. (2022). Perbandingan Algoritma Knn, Decision Tree,* Dan Random* Forest Pada Data Imbalanced Class Untuk Klasifikasi Promosi Karyawan. … Informatika Sains Dan …, 7(2), 192–200.
Used Cars Price Prediction using Supervised Learning Techniques. (2019). International Journal of Engineering and Advanced Technology, 9(1S3), 216–223. https://doi.org/10.35940/ijeat.a1042.1291s319
Waqar, M., Afridi, T. A., & Soomro, Q. (2023). Radiation levels of isolation rooms used by radio-iodine ablation patients during hospitalization at NORIN Nawabshah, Pakistan. International Journal of Basic and Applied Science, 11(4), 142–148.
Wu, L. C., Horng, J. T., Huang, H. Da, & Chen, W. L. (2008). Identifying discriminative amino acids within the hemagglutinin of human influenza A H5N1 virus using a decision tree. IEEE Transactions on Information Technology in Biomedicine, 12(6), 689–695. https://doi.org/10.1109/TITB.2008.896871
Yang, B.-S., Di, X., & Han, T. (2008). Random forests classifier for machine fault diagnosis. Journal of Mechanical Science and Technology, 22, 1716–1725.
Zhang, B. (2021). Tactical Decision System of Table Tennis Match based on C4.5 Decision Tree. Proceedings - 2021 13th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2021, 632–635. https://doi.org/10.1109/ICMTMA52658.2021.00146
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