Management and Economics
| Open Access |
DOI: Machine Learning–Driven Strategic Decision-Making: An Empirical Analysis of Employee Attrition Prediction Using Ensemble Models
Md Arif , Department of Management Science and Quantitative Methods, Gannon University, USA Md Abu Sufian Mozumder , College of Business, Westcliff University, Irvine, California, USA Ashadujjaman Sajal , Department of Management Science and Quantitative Methods, Gannon University, USA Kazi Abu Jahed , Master of Science in Business Intelligence and Analytics, Saint Joseph's University (SJU), USA Mohammad Kawsur Sharif , Department of Business Administration and Management, Washington University of Virginia, USA Asaduzzaman Anik , Master of Business Administration (MBA) in management, Stanton University, Los Angeles, California Mousumi Ahmed , Master’s in Public Administration, University of Dhaka, Dhaka, Bangladesh.Abstract
This study presents an empirical evaluation of machine learning models in supporting strategic decision-making within modern organizations. The analysis is conducted using a publicly available dataset from the Kaggle, focusing on employee-related variables to predict organizational outcomes, specifically employee attrition. Multiple machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting, are applied and compared using standard evaluation metrics such as accuracy, precision, recall, F1-score, and Area Under the Curve.The results demonstrate that Gradient Boosting outperforms all other models, achieving the highest accuracy of 90%, precision of 87%, recall of 85%, F1-score of 86%, and AUC score of 0.92. Random Forest also exhibits strong performance with an accuracy of 88% and an AUC of 0.90, indicating robust predictive capability. In contrast, Decision Tree and Logistic Regression models show comparatively lower performance, with accuracies of 82% and 79% respectively, reflecting their limited ability to capture complex, non-linear relationships within the dataset.The findings further reveal that variables such as job satisfaction, overtime, monthly income, and years at the company are the most influential predictors of attrition across all models. Feature importance analysis confirms that employee engagement and workload-related factors significantly impact organizational outcomes. Additionally, ensemble methods demonstrate greater stability and predictive reliability compared to single-model approaches.
Keywords
Machine Learning, Data Analytics, Strategic Decision-Making, Gradient Boosting, Random Forest, Predictive Modeling, Employee Attrition
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Copyright (c) 2026 Md Arif, Md Abu Sufian Mozumder, Ashadujjaman Sajal, Kazi Abu Jahed, Mohammad Kawsur Sharif, Asaduzzaman Anik, Mousumi Ahmed
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