Exploring Holistic Well-Being: EDA and aComparative ML Approach to Work-LifeBalance

This research focuses on lifestyle and work-life balance, examining how different dynamics can influence holistic wellbeing, which focuses on the current literature to develop a framework that extends beyond standard wellbeing concepts.The work aims to enrich the discourse on holistic well-being by synthesizing existing knowledge and exploring new knowledge, providing valuable views for individuals and age groups involved in professionals striving to develop a more balanced and meaningful life.The study utilizes AI-driven methods and machine learning algorithms to analyse work-life indices to understand patterns and correlations that contribute or hinder work-life harmony.The findings highlight the importance of lifestyle choices, social connections, and personal fulfilment in achieving holistic wellness.The research provides evidence-based insights and practical recommendations to develop a healthy lifestyle and work-life balance.The study also examines the ethical implications of AI and highlights the need for a comprehensive approach to work-life balance.The study utilizes supervised learning algorithms and a comparative analysis of the accuracy scores of various algorithms, revealing significant differences in classification and more accuracy for the work-life balance score.The study aims to uncover insights beyond the typical contrasts and illuminate the interrelationship between numerous factors affecting an individual's well-being.