Machine Learning Applications in Human Resource Management: Predicting Employee Turnover and Performance

Abstract

Employee turnover is a significant concern for organizations as it impacts productivity, increases hiring costs, and disrupts operations. Predicting turnover is essential for businesses to develop effective retention strategies. Traditional methods, such as HR surveys and statistical analysis, have limitations in accuracy, whereas machine learning (ML) provides a more efficient approach by analyzing employee data. This study compares Decision Tree and Random Forest models to predict employee turnover based on factors like annual salary, monthly salary, and job satisfaction. While Decision Tree models offer interpretability, they may lead to errors, whereas Random Forest enhances accuracy by combining multiple decision paths. The results of this research will assist organizations in identifying at-risk employees, taking proactive measures, and improving workforce stability. Despite its advantages, machine learning in HR analytics faces challenges such as data bias, privacy concerns, and evolving workforce dynamics. The future of turnover prediction lies in real-time data tracking and advanced AI models, enabling businesses to make informed HR decisions and strengthen employee retention efforts. Even with its benefits, machine learning used in HR analytics has pitfalls like data bias, privacy issues, and changing workforce dynamics. The future of predicting turnover comes with real- time data monitoring and more sophisticated AI models, allowing companies to make data- driven HR decisions and enhance employee retention policies.