Implementing machine learning for predictive analytics: An empirical study of employee turnover

Employee turnover presents a critical challenge for organizations, affecting operational efficiency, morale, and long-term performance. This paper investigates the application of machine learning models for predicting employee attrition using the IBM HR Analytics dataset. Several algorithms were tested, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Support Vector Classifier (SVC). Models were evaluated using accuracy, precision, recall, F1-score, ROC AUC, and cross-validation. Among them, Support Vector Classifier (SVC) model demonstrated superior performance, with an AUC of 0.8129 and cross-validated AUC of 0.8291, indicating strong discriminative capability. Feature importance analysis revealed that overtime, job satisfaction, and employee involvement are key predictors of turnover. The findings highlight machine learning’s value in transitioning HR practices from reactive to proactive workforce management, with potential future applications including deployment in HR dashboards, ethical compliance, and dynamic data integration.