Car Price Prediction Using Artificial Neural Networks: A Data-Driven Approach

Authors

  • Abass Taiwo Olabisi Onabanjo University
  • Lukman Ogundele Olabisi Onabanjo University
  • Lukman Ogundele Olabisi Onabanjo University
  • Femi Ayo Olabisi Onabanjo University
  • Adekunle Ejidokun Kampala International University

Keywords:

Hybrid feature selection, Correlation, Prediction, Performance, Used car data

Abstract

Used cars suffer from depreciation and require reevaluation from time to time to ascertain the actual price at which the car can be purchased or sold by buyers and sellers. Car price prediction is important because of the increase in the rate of purchase of used car compared with that of new cars due to inflation, fluctuation in exchange rates, currency devaluation and so on. To address the issues of accuracy and error rate, this work suggests a hybrid feature selection approach that extracts the most crucial properties from the dataset. The most important attributes in the dataset were then used as input for the prediction phase using deep learning approach. The deep learning model's output is contrasted with that of other machine learning techniques to identify the most effective approach. In comparison to the Decision Tree and Support Vector Machine (SVM) models, which performed at 87.8% and 88.3%, respectively, the suggested hybrid feature selection using deep learning model attained an accuracy of 96.9%, according to the evaluation data. However, the other two classifiers indicate a lower error rate as compared to the ANN model. 

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References

[1] A. A. Alhakamy, A. Alhowaity, A. A. Alatawi and H. Alsaadi, "Are Used Cars More Sustainable? Price Prediction Based on Linear Regression," Sustainability, vol. 15, no. 2, p. 911, 2023.

[2] Abishek, "Car Price Prediction using Machine Learning Techniques," International Research Journal of Modernization in Engineering Technology and Science, vol. 4 , no. 2, pp. 45-50, 2022.

[3] E. Costa, P. Wells, L. Wang and G. Costa, " The electric vehicle and renewable energy: Changes in boundary conditions that enhance business model innovations," Journal of cleaner production, vol. 333, p. 130034., 2022.

[4] D. M. &. N. R. C. Gomes, "Techno-economic analysis of vehicle-to-grid technology: Efficient integration of electric vehicles into the grid in Portugal.," Journal of Energy Storage, vol. 97, p. 112769, 2024.

[5] M. Collard, "Price prediction for used cars: a comparison of machine learning regression models.," Mid Sweden University,Bsc. Thesis Computer engineering, 2022.

[6] C. Trivendra, " The Price Prediction for Used Cars using Multiple Linear Regression Model.," International Journal for Research in Applied Science and Engineering Technology, pp. 1801-1804, 2020.

[7] J. Nilsson, J. Carlsson Hauff and A. Carlander, " How consumers evaluate costs in complex services: the case of mutual fund fees.," European Journal of Marketing, vol. 58, no. 13, pp. 56-80, 2024.

[8] A. Goel and G. Sahil, " An analysis of policy prospective of taxi aggregators and consumers in digital eco-system.," International Journal of Intelligent Enterprise, vol. 11, no. 2, pp. 120-140., 2024.

[9] K. Noor and S. Jan, "Vehicle price prediction system using machine learning techniques.," International Journal of Computer Applications, vol. 167, no. 9, pp. 27-31, 2017.

[10] M. Méndez, M. G. Merayo and M. Núñez, "Machine learning algorithms to forecast air quality: a survey.," Artificial Intelligence Review, vol. 56, no. 9, pp. 10031-10066., 2023.

[11] J. Zhao, H. Ling, J. Liu, J. Wang, A. F. Burke and Y. Lian, "Machine learning for predicting battery capacity for electric vehicles.," ETransportation, vol. 15, p. 100214, 2023.

[12] M. A. Kutlugün and Y. Şirin, "Augmenting the training database with the method of gradual similarity ratios in the face recognition systems.," Digital Signal Processing, Vols. 135,, p. 103967, 2023.

[13] D. Kshirsagar and S. Kumar, "Towards an intrusion detection system for detecting web attacks based on an ensemble of filter feature selection techniques.," Cyber-Physical Systems, vol. 9, no. 3, pp. 244-259., 2023.

[14] F. E. Ayo, L. A. Ogundele, S. Olakunle, J. B. Awotunde and F. A. Kasali, " A hybrid correlation-based deep learning model for email spam classification using fuzzy inference system," Decision Analytics Journal, vol. 10, p. 100390, 2024.

[15] E. Gegic, B. Isakovic, D. Keco, Z. Masetic and J. Kevric, "Car price prediction using machine learning techniques.," TEM Journal, vol. 8, no. 1, p. 113, 2019.

[16] C. K. Puteri and L. N. Safitri, "Analysis of linear regression on used car sales in Indonesia," In Journal of Physics: Conference Series , vol. 1469, no. 1, pp. 012143, IOP Publishing., 2020.

[17] M. Hankar, M. Birjali and A. Beni-Hssane, "Used car price prediction using machine learning: A case study.," in In 2022 11th International symposium on signal, image, video and communications (ISIVC) (pp. 1-4), IEEE, 2022.

[18] A. Chandak, P. Ganorkar, S. Sharma, A. Bagmar and S. Tiwari, "Car price prediction using machine learning.," International Journal of Computer Sciences and Engineering, vol. 7, no. 5, pp. 444-450., 2019.

[19] M. Asghar, K. Mehmood, S. Yasin and Z. M. Khan, "Used cars price prediction using machine learning with optimal features.," Pakistan Journal of Engineering and Technology, vol. 4, no. 2, pp. 113-119., 2021.

[20] K. Samruddhi and R. A. Kumar, "Used car price prediction using K-nearest neighbor based model," Int. J. Innov. Res. Appl. Sci. Eng.(IJIRASE), vol. 4, no. 3, pp. 2020-686, 2020.

[21] A. S. Pillai, "A Deep Learning Approach for Used Car Price Prediction," Journal of Science & Technology, vol. 3, no. 3, pp. 31-50, 2022.

[22] S. Kumar, V. Singh and R. Goel, "Strategic Forecasting for Electric Vehicle Sales: A Cutting Edge Holistic Model Leveraging Key Factors and Machine Learning Technique," Transportation in Developing Economies, vol. 10, no. 2, p. 23, 2024.

[23] P. H. Putra, A. Azanuddin, B. Purba and Y. A. Dalimunthe, "Random forest and decision tree algorithms for car price prediction.," Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA), vol. 3, no. 2, pp. 81-89., 2023.

[24] M. Alwadi, G. Chetty and M. Yamin, "A Framework for Vehicle Quality Evaluation Based on Interpretable Machine Learning," International Journal of Information Technology, vol. 15, no. 1, pp. 129-136., 2023.

[25] M. A. Hall, "Correlation-based feature selection for machine learning," (Doctoral dissertation, The University of Waikato)., 1999.

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Published

2025-01-14

How to Cite

Taiwo, A., Ogundele, L., Ogundele, L., Ayo, F., & Ejidokun, A. (2025). Car Price Prediction Using Artificial Neural Networks: A Data-Driven Approach. Indonesian Journal on Computing (Indo-JC), 9(3). Retrieved from https://socjs.telkomuniversity.ac.id/ojs/index.php/indojc/article/view/975

Issue

Vol. 9 No. 3 (2024): December, 2024

Section

Computer Science

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