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Research Article

Predictive Modeling of Automobile Prices Using Feature Engineering and Regression Techniques

  • ICCAIS-2026
  • Volume 5, Issue 1
  • Pages 120-126
  • Published Online: 2026
Open Access Peer Reviewed Crossref DOI

Authors

  • Suvam Nahak1
  • Pedina Bavishya2
  • Ayush Kumar Behera3
  • Soumya Ranjan Mishra4
  • Sachikanta Dash5
  • Prahallad Kumar Sahu6

1 2 3 4 6 Department of CSA, GIET University Gunupur, Odisha, India. 5 Madanapalle Institute of Technology and Science University, Andhra Pradesh, India.

Article Information

Organised By
Madanapalle Institute of Technology & Science (MITS)
Conference Date
9 & 10 March 2026
Pages
120-126
Article Type
Research Article
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References

1. D. Van Thai, L. N. Son, P. V. Tien, N. N. Anh, and N. T. N. Anh, “Prediction car prices using quantify qualitative data and knowledge- based system,” in 2019 11th International Conference on Knowledge and Systems Engineering (KSE), 2019, pp.1-5.
2. N. Pal, P. Arora, P. Kohli, D. Sundararaman, and S. S. Palakurthy, “How much is my car worth? A methodology for predicting used cars’ prices using random forest,” in Future of Information and Communication Conference, 2018, pp. 413–422.
3. Mishra, S. R., Dash, S., Padhy, S., Kumar, N., & Dash, Y. (2024, September). Integrating Multi-Omics Data for Advanced Diabetes Prediction and Understanding. In 2024 7th International Conference on Contemporary Computing and Informatics (IC3I) (Vol. 7, pp. 1447-1453). IEEE.
4. Dash, S., Mishra, S. R., & Baboo, A. (2025, January). Enhancing Diabetes Prediction using Hybrid Ensemble Approach. In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN) (pp. 1-6). IEEE.
5. A. Camero, J. Toutouh, D. H. Stolfi, and E. Alba, “Evolutionary deep learning for car park occupancy prediction in smart cities,” in International Conference on Learning and Intelligent Optimization, 2018, pp. 386–401.
6. Dash, S., Mishra, S. R., & Baboo, A. (2025, January). Efficient Prediction of Diabetes Mellitus Through Hybrid Ensemble Machine Learning Model Using IoT. In 2025 1st International Conference on AIML-Applications for Engineering & Technology (ICAET) (pp. 1- 6). IEEE.
7. Mishra, S. R., & Dash, S. (2024, December). Machine Learning Based Diabetes Prediction Using the PIMA Indian Dataset. In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (pp. 1-6). IEEE.
8. D. Štifanić, J. Musulin, A. Miočević, S. Barresi Šegota, R. Subic, and Z. Car, “Impact of COVID-19 on forecasting stock prices: an integration of stationary wavelet transforms and bidirectional long short-term memory,” Complexity, vol. 2020, 2020.
9. L. Bukvić, J. Pašagić Škrinjar, T. Fratrović, and B. Abramović, “Price Predic-tion and Classification of Used-Vehicles Using Supervised Machine Learning,”Sustainability, vol. 14, no. 24. MDPI AG, p. 17034, Dec. 19, 2022.
10. Rath, L., Mishra, S. R., Dash, S., Pradhan, P. C., & Baboo, A. (2025). Predicting diabetic patients coronary artery calcium score, deep learning using retinal images. In Intelligent Computing Techniques and Applications (pp. 113-118). CRC Press.
11. Dash, S., Padhy, S., Kumar, N., & Nayyar, A. (2026). Medisecure: a hybrid approach for enhancing multimedia data protection in healthcare. Cluster Computing, 29(1), 75.
12. Dash, S., Padhy, S., Suman, P., Mal, S., Malviya, L., Suman, A., & Kishore, J. (2025). Privacy-preserving diabetes and heart disease prediction via federated learning and WCO. International Journal of Computational Intelligence Systems, 18(1), 217.
13. Mishra, S. R., Dash, S., Padhy, S., & Das, R. K. (2024, September). Diabetic Foot Ulcer Diagnosis Through Deep Learning Model. In 2024 International Conference on Artificial Intelligence and Emerging Technology (Global AI Summit) (pp. 1194-1199). IEEE.
14. E. Gegic, B. Isakovic, D. Keco, Z. Masetic, and J. Kevric, “Car Price Prediction using Machine Learning Techniques,” vol. 8, no. 1, p. 7, 2021.
15. Mishra, S. R., & Dash, S. (2024). Iterative Model Design for Diabetes Analysis Using FedOmics Causal Network and Federated Multi- Omics Variational Autoencoder. International Journal of Emerging Technologies and Innovative Research, 11(6).
16. M. Cocca, D. Giordano, M. Melia, and L. Vassio, “Free floating electric car sharing: A data-driven approach for system design,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 12,
17. pp. 4691–4703, 2019.
18. Mishra, S. R., & Dash, S. (2024). Predictive Analysis On Diabetes Detection Using Pima Indian Diabetes Dataset. IJRAR-International Journal of Research and Analytical Reviews (IJRAR), 11(2).
19. A. Barnett et al., “Enacting ‘more-than-human’ care: Clients’ and counsellors’ views on the multiple affordances of chatbots in alcohol and other drug counselling,” International Journal of Drug Policy, p. 102910, Oct. 2020
20. Baboo, A., Mishra, S. R., & Dash, S. (2024, November). An Improvised Diabetes Prediction System Using Hybrid Ensemble Approach. In 2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) (pp. 1-6). IEEE..
21. A. U. Guler, K. Misra, and V. Singh, “Heterogeneous price effects of consolidation: Evidence from the car rental industry,” Marketing Science, vol. 39, no. 1, pp. 52–70, 2020.
22. Mishra, S. R., & Dash, S. (2026). AI ‐ Driven Remote Health Monitoring for Predicting Diabetes and Heart Diseases Using ULMCSO and PGND Models. Hyper ‐ Intelligent Networks: Exploring the Future of Connectivity for Society 5.0, 219-247.
23. M. Hankar, M. Birjali, and A. Beni-Hssane, “Used Car Price Prediction usingMachine Learning: A Case Study,” 2022 11th International Symposium onSignal, Image, Video and Communications (ISIVC). IEEE, May 18, 2022.
24. Mishra, S. R., Dash, S., Padhy, S., & Samuel, P. (2026). Legal Aspects of Operating IoMT Applications in the Fog Computing. In Integrating Cloud, Fog, and Edge Computing in Healthcare: Federated Learning and Blockchain Approaches: Harnessing Distributed Technologies for Enhanced Healthcare Delivery (pp. 211- 224). Cham: Springer Nature Switzerland.
25. A. Fathalla, A. Salah, K. Li, K. Li, and P. Francesco, “Deep end-to- end learning for price prediction of second-hand items,” Knowledge and Information Systems, vol. 62, no. 12, pp. 4541–4568, 2020.
26. Pattnayak, A. P., Mishra, S. R., Dash, S., & Baboo, A. (2025). Utilization of deep learning and machine learning models to approach high glucose and low glucose prediction with type 1 diabetes mellitus in adult patients. In Intelligent Computing Techniques and Applications (pp. 102-107). CRC Press.
27. Dora, N., Dash, S., Baboo, A., & Mishra, S. R. (2025, August). Efficient Nail Disease Diagnosis Using Deep Neural Networks for Predicting Abnormalities. In 2025 International Conference on Next Generation of Green Information and Emerging Technologies (GIET) (pp. 1-5). IEEE.
28. Mishra, S. R., Dash, S., & Rath, L. (2024, November). Effective Diabetes Mellitus Prediction Using a Hybrid Ensemble Machine Learning Model with Iot. In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS) (pp. 1- 8). IEEE.
29. Khuntuli, B., Dash, S., Pradhan, P. C., & Mishra, S. R. Combating food insecurity through remote sensing and machine learning for enhanced crop yield prediction. In Intelligent Computing Techniques and Applications (pp. 135-140). CRC Press.
30. Sahu, P. K., Biswal, B. B., Mishra, S. R., Padhy, J., & Kumar, D. (2025, March). Demand-Based Secured Data Transmission in WSN. In International Conference on Next Generation Computing and Communication Applications (pp. 37-44). Cham: Springer Nature Switzerland.
31. Baboo, A., Dash, S., Padhy, S., & Samuel, P. (2026). Testing Perspectives of Fog-Based IoMT Applications with Federated Learning. In Integrating Cloud, Fog, and Edge Computing in Healthcare: Federated Learning and Blockchain Approaches: Harnessing Distributed Technologies for Enhanced Healthcare Delivery (pp. 199-210). Cham: Springer Nature Switzerland.
32. C. Jin, ”Price Prediction of Used Cars Using Machine Learning,” 2021 IEEE Interna-tional Conference on Emergency Science and Information Technology (ICESIT),Chongqing, China, 2021, pp. 223- 230.
33. Baboo, A., Patro, S. P., & Dash, S. (2024, December). A Deep Learning Approach for Enhancing Cardiovascular Disease Prediction Using ECG Data. In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (pp. 1-5). IEEE.
34. Dash, A. B., Dash, S., Padhy, S., Kumar, N., Pati, G. K., & Uthansingh, K. (2025). Leveraging inception-v3 CNN model for efficient image classification. In Intelligent Computing and Communication Techniques (pp. 341-348). CRC Press.
35. Dash, A. B., Dash, S., Padhy, S., Mishra, B., & Paikaray, B. K. (2025). Streamlining colorectal cancer diagnosis: leveraging MobileNet-V3 for efficient image classification. Int. J. Internet Manufacturing and Services, 11(4), 317.
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