Enhancing Employee Retention: Predicting Attrition Using Machine Learning Models
Keywords:
business, economics, attrition, employee retention, machine learning, predictionAbstract
Employee attrition poses challenges for organizations, affecting productivity and costs. This study applies machine learning models to predict attrition using the IBM Employee Attrition dataset. To address class imbalance, we analyze ten supervised models, with Random Forest outperforming others, especially with SMOTE. Key predictors include overtime, stock options, job satisfaction, job level, and tenure with a manager. Causal inference techniques quantify their impact, providing understanding for retention strategies. These findings provide actionable insights for organizations to implement targeted retention strategies, reduce turnover, and enhance employee engagement. Future research should explore real-time analytics and ethical AI frameworks for workforce management.
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