Optimizing Workforce Stability: Machine Learning Techniques for Predicting Employee Attrition

Abstract

Abstract: Employee attrition poses significant challenges to organizations, impacting operational efficiency and increasing recruitment and training costs. This study explores the application of machine learning techniques to predict employee attrition, providing HR management with actionable insights to enhance retention strategies. We utilized logistic regression, decision trees, random forests, and gradient boosting to develop predictive models based on a comprehensive dataset of employee records. Our results indicate that the gradient boosting model achieved the highest accuracy (87%) and AUC (0.94), followed by the random forest model with an accuracy of 85% and AUC of 0.92. These models demonstrated robust performance in identifying at-risk employees, enabling proactive interventions to mitigate turnover. The study underscores the potential of machine learning in transforming HR analytics, offering a data-driven approach to improving workforce stability. Future research should focus on integrating external factors and ensuring ethical considerations in the deployment of predictive analytics in HR practices.