Beyond the Grind: Leveraging Data Analysis and Machine Learning for the Quantification and Enhancement of Work-Life Balance - Vijayanand, Deepshika; P, Subbulakshmi (International Journal of Membrane Science and Technology, 2023)
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.