In today’s data-rich organisational landscape, predictive analytics is rapidly redefining how human resource professionals confront the persistent challenge of employee turnover. This study explores the efficacy of machine learning models in forecasting employee attrition by applying robust algorithms such as Random Forest and XGBoost. Drawing upon a structured primary dataset from a mid-sized service firm, we employed ten comprehensive statistical techniques—including correlation analysis, logistic regression, decision trees, and ROC-AUC evaluation—to uncover the most influential factors affecting employee exits. The results demonstrate that attributes like job satisfaction, performance rating, monthly income, and promotion frequency are key predictors of attrition. The Random Forest model, with an AUC score of 0.89, outperformed others in both accuracy and interpretability, further validated through feature importance analysis. The study also highlights the interpretive value of SHAP and permutation importance in demystifying black-box models and enhancing managerial trust in predictive systems.

This research offers significant implications for HR practitioners, urging a shift from reactive to proactive retention strategies driven by data. While the models show high predictive accuracy, limitations regarding sample generalisability and feature engineering remain. Nonetheless, the findings lay a strong foundation for future investigations in diverse sectors, with a call to embed such analytics into strategic HR decision-making. By integrating advanced machine learning with grounded human insight, organisations can navigate turnover risks with far greater foresight and precision.