Beyond the Grind: Leveraging Data Analysis and Machine Learning for the Quantification and Enhancement of Work-Life Balance

Authors

  • Deepshika Vijayanand Vellore Institute of Technology, Chennai, Tamil Nadu, India
  • Subbulakshmi P Vellore Institute of Technology, Chennai, Tamil Nadu, India

DOI:

https://doi.org/10.15379/ijmst.v10i1.2634

Keywords:

Work-Life Balance, Machine Learning, Regression Analysis, Exploratory Data Analysis, Predictive Modelling, Mean Squared Error (MSE), R-Squared (R²).

Abstract

This research aims to comprehensively investigate the dynamics of work-life balance and to develop predictive models using machine learning techniques to assess and predict the factors influencing work-life equilibrium. The study leverages a dataset containing 15,973 responses obtained from the global work-life survey conducted by Authentic-Happiness.com. The survey comprises 23 questions, providing a multifaceted view of how individuals manage their personal and professional lives. Initial Exploratory Data Analysis (EDA) uncovers five key dimensions: "Healthy Body," "Healthy Mind," "Expertise," "Connection," and "Meaning." These dimensions are explored to gain insights into their significance in relation to work-life balance. Subsequently, an extensive set of machine learning regression models, including Linear Regression, Decision Tree Regression, Random Forest Regression, Gradient Boosting Regressor, XGBoost, LightGBM, CatBoost, Support Vector Machine, K Nearest Neighbors, K-Means Regression, Ridge and Lasso Regression, Principal Component Analysis, RANSAC, Quartile Regression, GAM, Huber Regression, RBF Kernel Regression, and SGD Regression, are employed to predict work-life balance scores. Performance evaluation is based on metrics such as Mean Squared Error (MSE) and R-squared (R²). The research uncovers a holistic understanding of work-life balance and identifies significant predictors. The comparative analysis of machine learning models reveals their effectiveness in predicting work-life balance, highlighting the models that perform optimally. This research contributes valuable insights into the intricate factors that underlie work-life balance, offering a data-driven perspective that can inform personal choices, organizational strategies, and policy decisions. The application of machine learning techniques underscores the potential for addressing contemporary challenges associated with achieving a harmonious work-life equilibrium.

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Published

2023-10-11

How to Cite

[1]
D. . Vijayanand and S. . P, “Beyond the Grind: Leveraging Data Analysis and Machine Learning for the Quantification and Enhancement of Work-Life Balance”, ijmst, vol. 10, no. 1, pp. 718-734, Oct. 2023.