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Author ORCID Identifier

https://orcid.org/0000-0001-9426-7643

Abstract

Pursuing Sustainable Development Goals (SDGs) necessitates aligning business and management practices on a global scale. This paper delves into the intricate dynamics between Gross State Domestic Product (GSDP) and SDGs across diverse states in India, offering nuanced insights to policymakers, businesses, and stakeholders. This paper explores the dynamic relationship between Gross State Domestic Product (GSDP) and the Sustainable Development Goals (SDGs) in the context of India's diverse states by applying modern machine learning techniques such as XG boost, Decision trees, and K mean clustering. The study delves into how economic growth influences the progress towards SDGs. The research integrates complex systems methodologies, combining exploratory data analysis, correlation analysis, and clustering to offer actionable insights for policymakers and businesses. The paper emphasizes the need for tailored strategies that consider the economic development stages of states to achieve sustainable development goals more effectively. Through this multidimensional approach, the study provides a comprehensive understanding of how GSDP can guide the pursuit of SDGs and proposes innovative, data-driven solutions for fostering sustainable growth across India.

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