Enhancing Management Strategies: Machine Learning and Creative Performance Insights in Employee Attrition Analysis and Prediction

Neelam  Urooj; Tasawar Javed; Muhammad Usman; Muhammad Tahir Mushtaq; Ayesha Raza Toor

Authors

Neelam Urooj Institute of Business Management and Administrative Sciences (IBM & AS), The Islamia University of Bahawalpur, Bahawalpur, Pakistan
Tasawar Javed Review of Automaton Learning Algorithms with Polynomial Complexity--Completely Solved Examples
Muhammad Usman Department of Computer Sciences, Quaid e Azam University, Islamabad
Muhammad Tahir Mushtaq School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
Ayesha Raza Toor School of Systems and Technology, University of Management and Technology, Lahore, Pakistan

Keywords:

Attrition, Prediction, Machine Learning, SMOTE

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) 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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