Predicting Employee Turnover through Machine Learning and Data Analytics

Abstract

In today's rapidly evolving business environment, employee retention has become a critical concern for organizations striving to maintain operational efficiency and reduce turnover costs. This paper presents a data-driven analysis of employee engagement, job satisfaction, salary, training hours, and their impact on turnover risk. Using a simulated dataset of 1,000 employees, we explore the relationships between these key factors through statistical analysis and predictive modeling. A Random Forest model is employed to predict employee turnover and assess the relative importance of each variable. The results reveal that job satisfaction and salary are the strongest predictors of turnover, with engagement scores and training hours also playing significant but less influential roles. Our findings provide actionable insights for human resource professionals, enabling them to develop targeted retention strategies that address the most impactful factors driving employee attrition. The study contributes to the existing literature by integrating multiple metrics into a comprehensive predictive model, offering a holistic understanding of employee retention dynamics.