Machine Learning Models for Predicting Employee Attrition: A Data Science Perspective

Introduction: Employee attrition poses significant challenges for organizations, impacting productivity and profitability. This study explores attrition patterns using machine learning models, integrating predictive analytics with established human resource theories to identify key drivers of workforce turnover. Methods: The research analysed a dataset comprising demographic, job-related, and engagement factors. Logistic Regression was employed as the baseline model to interpret linear relationships, while Random Forest and Decision Trees captured non-linear interactions. Performance metrics such as accuracy, precision, recall, F1-score, and AUC-ROC were used to evaluate model effectiveness, alongside feature importance analysis for actionable insights. Results: Results revealed that job satisfaction, tenure, departmental dynamics, and engagement levels are critical predictors of attrition. Random Forest emerged as the most effective model, achieving an accuracy of 92% and an AUC-ROC of 94%, highlighting its capability to capture complex patterns. Decision Trees provided interpretable decision rules, offering practical thresholds for HR interventions. Logistic Regression complemented these models by offering insights into direct, linear relationships between predictors and attrition. Conclusion: The study finds that machine learning improves attrition analysis by identifying complex patterns and enabling proactive retention strategies. Predictive analytics strengthens traditional theories, providing a structured approach to reducing employee turnover. Organizations can use these insights to enhance workforce stability and performance. Future research could incorporate qualitative methods and longitudinal studies to refine strategies and assess long-term impacts.