Predictive Analytics for Employee Retention: Leveraging Machine Learning in HR Management

Abstract

Abstract: Employee retention is a critical challenge for organizations seeking to maintain a stable and experienced workforce. This study explores the application of predictive analytics and machine learning techniques to anticipate employee attrition and enhance HR management practices. By analyzing historical employee data, including demographics, performance metrics, and engagement levels, we develop predictive models that identify potential attrition risks. These models enable HR departments to proactively implement retention strategies, thereby reducing turnover rates and associated costs. The research highlights the effectiveness of various machine learning algorithms, including decision trees, random forests, and logistic regression, in predicting employee attrition. Furthermore, the study discusses the ethical considerations and data privacy concerns in deploying predictive analytics in HR. The findings underscore the potential of machine learning to transform HR management by providing actionable insights and fostering a more engaged and loyal workforce.