Computational Analysis of Continuity and Change: Machine Learning Insights into India's 18th Lok Sabha Formation

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Pavan Kumar Goyal
Dr. Prashant Sen
Dr. Anil Pimplapure

Abstract

This paper examines the formation and early dynamics of India's 18th Lok Sabha following the 2024 general election, which resulted in an unprecedented third consecutive term for Prime Minister Narendra Modi and the BJP-led National Democratic Alliance (NDA). It analyzes the electoral outcomes, coalition dynamics, policy priorities, and potential implications for India's democratic institutions through computational methods and machine learning techniques. The research draws on electoral data, policy announcements, and preliminary parliamentary proceedings to offer insights into the trajectory of Modi's third administration. The findings suggest a complex interplay between continuity in leadership and governance approach, alongside evolving coalition dynamics and policy adaptations in response to emerging economic and geopolitical challenges.

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How to Cite
Pavan Kumar Goyal, Dr. Prashant Sen, & Dr. Anil Pimplapure. (2024). Computational Analysis of Continuity and Change: Machine Learning Insights into India’s 18th Lok Sabha Formation. Educational Administration: Theory and Practice, 30(1), 6774–6781. https://doi.org/10.53555/kuey.v30i1.10026
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Author Biographies

Pavan Kumar Goyal

Research Scholar, Department of Computer Science Engineering, School of Engineering, Eklavya University, Damoh M.P., India.

Dr. Prashant Sen

Head of Department, Department of Computer Science Engineering, School of Engineering, Eklavya University, Damoh M.P., India.

Dr. Anil Pimplapure

Dean, School of Engineering, Eklavya University, Damoh M.P., India