IJMECS Vol. 16, No. 6, 8 Dec. 2024
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Exploratory Data Analysis, Holistic Well-being, Supervised Learning, Work-life balance, Work-life Harmony
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 well-being 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.
Bala Dhandayuthapani V., "Exploring Holistic Well-Being: EDA and a Comparative ML Approach to Work-Life Balance", International Journal of Modern Education and Computer Science(IJMECS), Vol.16, No.6, pp. 1-19, 2024. DOI:10.5815/ijmecs.2024.06.01
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