Predicting Employee Turnover Using Machine Learning Techniques

Background: Employee turnover is a persistent issue in human resource management, leading to significant costs for organizations. This study aims to identifythe most effective machine learning model for predicting employee attrition, thereby providing organizations with a reliable tool to anticipate turnover and implement proactive retention strategies. Objective: This study aims to address the challenge of employee attrition by applying machine learning techniques to provide predictive insights that can improve retention strategies. Methods: Nine machine learning algorithms are applied to a dataset of 1,470 employee records. After data preprocessing and splitting into training and test sets, the models are evaluated on metrics including accuracy, precision, recall, F1 score and AUC. Model performance is optimized through hyperparameter tuning, using grid search with cross-validation. Results: Logistic regression achieves the highest accuracy and precision, making it the top-performing model overall. Random forest provides a balanced performance with strong AUC, offering a robust alternative. Conclusion: Human resources managers and directors should consider using logistic regression or random forest for predictive modelling of employee turnover, as these models have shown strong performance. Future research should employ causal analysis for deeper insights. Real-time monitoring and adaptive prediction could also enhance models, offering a dynamic approach to attrition management.