Understanding Work and Sleep Through A Machine Learning Approach

The present study will utilize machine learning methods to find the individual factors (e.g, demographic, lifestyle, contextual, work-related) best predict sleep. I will examine (1) whether machine learning techniques better predict sleep over linear prediction methods and (2) what factors are most important when predicting sleep. Using government-funded data from the Bureau of Labor Statistics (BLS), a variety of prediction methods will be utilized to evaluate and compare predictive performance across 2018, 2019, and 2020. Results found will have a variety of theoretical and practical implications for scientists and practitioners.