Improving Workforce Management with Machine Learning: A Novel Approach to Employee Classification

This study proposes a novel machine learning-based employee classification system to address the critical challenge of employee attrition, offering significant advancements in workforce management. Leveraging a dataset from XYZ Corporation, the system employs advanced machine learning techniques to predict employee turnover with high accuracy and interpretability. The XGBoost model emerged as the best-performing algorithm, achieving an accuracy of 92.3%, a precision of 89.7%, and an F1-score of 90.6%. Key predictors of attrition, such as job satisfaction, work-life balance, and compensation, were identified using SHAP (SHapley Additive Explanations) values, enabling HR professionals to understand and address the underlying drivers of turnover. A key innovation of this research is the integration of fairness-aware techniques, such as re-weighting and adversarial debiasing, to mitigate bias in employee classification. This ensures equitable predictions across demographic groups, promoting ethical and transparent workforce analytics. Additionally, a decision support system was developed to provide HR teams with real-time insights and personalized retention strategies, enabling proactive interventions for high-risk employees. The findings demonstrate the superiority of machine learning over traditional methods, such as logistic regression, which achieved only 85.2% accuracy. By combining predictive accuracy, explainability, and fairness, this research advances the field of AI-driven workforce management. The proposed system has significant implications for improving employee retention, reducing turnover costs, and fostering data-driven HR decision-making. Future work could explore scalability across industries and the long-term impact of personalized retention strategies on organizational performance.