doi: 10.7763/IJCTE.2026.V18.1388
Harnessing Explainable AI and Machine Learning for Dual Predictive Modeling of Car Goodwill and Crop MSP in Price and Policy Forecasting
Meghna Chaudhary1
,
Mohammad Afshar Alam1 ,
Sherin Zafar1,* ,
Kiliyanal Muhammedkunju Abubeker2
- 1Department of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi, India
- 2Department of Electronics and Communication Engineering, Amal Jyothi College of Engineering (Autonomous), Kanjirappally, Kerala, India
- Manuscript receivedFebruary 12, 2025
- revisedApril 15, 2025
- acceptedNovember 25, 2025
- publishedMarch 17, 2026
Abstract
Accurate yet interpretable forecasting remains a major challenge in data-driven price and policy decision-making, as many machine learning models prioritize predictive performance while offering limited transparency. To address this gap, this study proposes an integrated Explainable Artificial Intelligence (XAI)—enabled machine learning framework for dual predictive modelling across two economically significant domains: automotive valuation and agricultural price policy. The framework simultaneously predicts car goodwill values and crop Minimum Support Prices (MSP), enabling cross-domain analysis while maintaining model interpretability. Comparative experiments are conducted using multiple machine learning techniques, including linear and ensemble-based models, applied to automotive and agricultural datasets. Ensemble methods demonstrate superior predictive capability in both domains. To enhance transparency and stakeholder trust, XAI techniques are incorporated to explain model behaviour and identify key influencing factors. The analysis shows that depreciation and brand-related attributes play a dominant role in car goodwill valuation, whereas climatic and cost-related factors significantly influence MSP predictions. The results confirm that integrating XAI with machine learning improves both predictive reliability and interpretability, transforming black-box models into actionable decision-support systems. The proposed dual predictive framework offers a scalable and transparent approach for market optimization and policy evaluation, highlighting the practical value of explainable AI in strategic economic planning and data-driven governance.
Keywords
- Explainable Artificial Intelligence (XAI)
- goodwill car values
- crop Minimum Support Price (MSP)
- linear regression
- random forest
- decision tree
- gradient boosting
- XGBoost and Marketing Mix Modelling (MMM)
How to Cite
Meghna Chaudhary, Mohammad Afshar Alam, Sherin Zafar, and Kiliyanal Muhammedkunju Abubeker, "Harnessing Explainable AI and Machine Learning for Dual Predictive Modeling of Car Goodwill and Crop MSP in Price and Policy Forecasting," International Journal of Computer Theory and Engineering, vol. 18, no. 1, pp. 48-67, 2026. https://doi.org/10.7763/IJCTE.2026.V18.1388
Copyright & License
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).