Enhancing Management Strategies: Machine Learningand Creative Performance Insights in Employee Attrition Analysisand Prediction

Neelam Urooj ()
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Neelam Urooj: Institute of Business Management and Administrative Sciences (IBM & AS), The Islamia University of Bahawalpur, Bahawalpur, Pakistan

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 3, 1211-1227

Abstract: 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)modelstoforecastemployee turnover and deliverdata-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 MLmodels 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 modelis the best modelwhen combined with SMOTE, achieving performance scores of 0.96out of 1.

Keywords: Attrition; Prediction; Machine Learning; SMOTE (search for similar items in EconPapers)
Date: 2024
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