Enhancing Management Strategies: Machine Learning and Creative Performance Insights in Employee Attrition Analysis and Prediction
Keywords:
Attrition, Prediction, Machine Learning, SMOTEAbstract
Employee attrition and excessive turnover are major difficulties in today's competitive employment market, affecting many industries. To overcome these difficulties, firms are increasingly relying on artificial intelligence (AI) to forecast staff loss and devise effective retention strategies. This study investigates famous machine learning (ML) models to forecast employee turnover and deliver data-driven solutions. The first section of the study compares various ML models on an imbalanced dataset. The second section introduces the Synthetic Minority Oversampling Technique (SMOTE) for data oversampling and applies ML models to the enlarged dataset. ML can predict employee turnover by examining historical data, employee behavior, and external factors. This early detection enables organizations to respond proactively with targeted retention strategies. The study concludes that the Random Forest model is the best model when combined with SMOTE, achieving performance scores of 0.96 out of 1.
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