International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 8 Issue 2
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E-Governance Service Delivery Analysis Using Machine Learning: A Study of Women & Child Development and School Education Services in Rural Regions

Author(s) Mr. Jogendra Kumar, Dr. Pramod Singh, Dr. Pinki Sharma
Country India
Abstract The aim of this research is to analyze the relevance of machine learning algorithms in understanding the challenges faced by e-governance service delivery in the Women & Child Development and School Education departments, particularly in the rural areas. Although there has been an increase in the number of digital governance projects aimed at enhancing accessibility, efficiency, and transparency in public services, the rural population is still faced with several challenges that impede the effective use of such systems. A major part of these challenges is related to Information and Communication Technology (ICT) issues that impede service delivery systems and overall system performance. In this scenario, supervised machine learning algorithms such as Decision Tree, Random Forest, and Logistic Regression were used to analyze and assess the effect of critical ICT-related challenges on service disruption. This research uses primary data collected from a structured survey of 200 respondents in various rural areas. Thirteen ICT-related parameters were taken into consideration, which included internet connectivity, electricity availability, infrastructure adequacy, digital literacy, awareness levels, content accessibility, and language proficiency. The findings have shown consistent results, which reveal that the lack of adequate internet connectivity and electricity availability are the most influential factors that negatively impact digital service delivery in both sectors. Moreover, digital literacy and citizen awareness were identified as significant moderating factors that shape system usability and adoption outcomes. Among the classification models compared, the Random Forest classifier proved to be the most accurate and robust model, which provided a reliable ranking of feature importance in both sectors. The findings of this research stress the need to improve ICT infrastructure, digital literacy, and awareness programs to ensure effective governance outcomes. This research also underscores the efficacy of machine learning techniques as analytical tools in facilitating evidence-based policy formulation and decision-making in a rural governance context
Keywords Machine Learning, E-Governance, Women & Child Development, School Education, ICT Challenges, Random Forest
Field Computer Applications
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-03-11
DOI https://doi.org/10.36948/ijfmr.2026.v08i02.70994
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E-ISSN 2582-2160
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