Online Subsidy Management System Using Machine Learning (Algorithm- Logistic Regression, Random Forest, Decision Tree)
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2026
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| _version_ | 1866901726195351552 |
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| author | Aniket Bhandare, Soundrya Biradar, Nikhil Lonari, Vishwaraj Pawar, Pallavee Bavane-Patil |
| author_facet | Aniket Bhandare, Soundrya Biradar, Nikhil Lonari, Vishwaraj Pawar, Pallavee Bavane-Patil |
| contents | <p class="MsoNormal"><span>Government subsidy programs play a crucial role in socio economic development by supporting vulnerable populations in sectors such as agriculture, education, healthcare, energy, and food security. However, traditional subsidy management systems are often plagued by inefficiencies, fraud, leakage, lack of transparency, and poor targeting. The advent of digital governance and data driven technologies has opened new avenues for reforming subsidy allocation and monitoring mechanisms. Machine learning (ML), in particular, offers powerful tools for automating eligibility assessment, predicting beneficiary behavior, detecting anomalies, and optimizing policy outcomes. This review paper presents a comprehensive analysis of online subsidy management systems integrated with machine learning techniques, with a specific focus on Logistic Regression, Decision Tree, and Random Forest algorithms. The paper discusses system architecture, data sources, preprocessing methods, algorithmic frameworks, evaluation metrics, real world use cases, challenges, ethical considerations, and future research directions. The review aims to serve as a ready reference for researchers, policymakers, and system designers working toward intelligent, transparent, and efficient subsidy management platforms.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19899201 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Online Subsidy Management System Using Machine Learning (Algorithm- Logistic Regression, Random Forest, Decision Tree) Aniket Bhandare, Soundrya Biradar, Nikhil Lonari, Vishwaraj Pawar, Pallavee Bavane-Patil Online Subsidy Management System, Machine Learning, Logistic Regression, Decision Tree, Random Forest, E Governance, Fraud Detection, Welfare Distribution <p class="MsoNormal"><span>Government subsidy programs play a crucial role in socio economic development by supporting vulnerable populations in sectors such as agriculture, education, healthcare, energy, and food security. However, traditional subsidy management systems are often plagued by inefficiencies, fraud, leakage, lack of transparency, and poor targeting. The advent of digital governance and data driven technologies has opened new avenues for reforming subsidy allocation and monitoring mechanisms. Machine learning (ML), in particular, offers powerful tools for automating eligibility assessment, predicting beneficiary behavior, detecting anomalies, and optimizing policy outcomes. This review paper presents a comprehensive analysis of online subsidy management systems integrated with machine learning techniques, with a specific focus on Logistic Regression, Decision Tree, and Random Forest algorithms. The paper discusses system architecture, data sources, preprocessing methods, algorithmic frameworks, evaluation metrics, real world use cases, challenges, ethical considerations, and future research directions. The review aims to serve as a ready reference for researchers, policymakers, and system designers working toward intelligent, transparent, and efficient subsidy management platforms.</span></p> |
| title | Online Subsidy Management System Using Machine Learning (Algorithm- Logistic Regression, Random Forest, Decision Tree) |
| topic | Online Subsidy Management System, Machine Learning, Logistic Regression, Decision Tree, Random Forest, E Governance, Fraud Detection, Welfare Distribution |
| url | https://doi.org/10.5281/zenodo.19899201 |