Template-Type: ReDIF-Article 1.0
Author-Name: Khadiga ABDELKARIM
Author-Name-First: Khadiga
Author-Name-Last: ABDELKARIM
Author-Email: khadiga.hamid@student.aiu.edu.my
Author-Workplace-Name: Albukhary International University
Author-Name: Mahgoub ABDELRAHIM
Author-Name-First: Mahgoub
Author-Name-Last: ABDELRAHIM
Author-Email: mahgoubosman2020@gmail.com
Author-Workplace-Name: Albukhary International University
Author-Name: Baraa ELTAYEB
Author-Name-First: Baraa
Author-Name-Last: ELTAYEB
Author-Email: baroaltayeb@gmail.com
Author-Workplace-Name: Albukhary International University
Title: Predictive Model for Reducing Employee Turnover Using Machine Learning Techniques
Abstract: Purpose- The problem of employee turnover is a chronic disruption in the stability of organizations, them functioning, and their long-term development. This paper will fill the gap of proactive and data-driven instruments that would measure the employees at risk of leaving without resignation. Aim- The objective of the study is to construct and test a predictive employee turnover model based on interpretable machine learning and apply it to employee retention purposes and offer a generalizable model to HR professionals. Methodology- The analysis utilizes the CatBoost Classifier gradient-boosting model optimized over a categorical variable and analyzes a publicly available HR analytics dataset of 59,598 records of employees at Kaggle. The data processing, model training, and performance evaluation (in terms of Accuracy, F1- score, and ROC-AUC) are part of the research pipeline along with the analysis of feature-importance to determine what predictors, have the strongest effect on attrition? Findings- The model scored high (0.8546) in ROC-AUC which means that the model has strong discriminative ability with regard to the ability to differentiate between employees who leave and those who remain. The feature-importance analysis demonstrates that organizational and behavioral factors include job level, marital status, remote work status, work-life balance, promotions, and dependents which have significantly larger predictive power compared to such demographic factors as age and gender. Limitations- The conclusions are made using one big company dataset and will not take into consideration contextual aspects of the labour-market or company-specific cultural variables. The lack of qualitative and team level indicators (leadership style, psychological safety, engagement) can also restrict the extension of the model, especially in an academic and a public-sector environment. Practical Implications- The model offers a clear, evidence-based instrument to HR professionals to recognize high-risk workers and shape up specific interventions on career advancement, work-related flexibility, recognition, and work-life interface. When organizations are paying attention to the most significant predictors, they can focus on retention programs and dedicate resources toward better use. Originality/value- This work is useful to HR analytics because it combines a highly performing machine learning algorithm with explainable results, as opposed to accuracy-only methods and adoptable, practitioner- oriented models. It provides a flexible baseline framework that can be scaled up or down by organizations, such as academic and public ones, to enable strategic planning of the workforce base and evidence-based HR decision-making.
Classification-JEL: I2, O3
Keywords: Employee Turnover, Predictive Analytics, CatBoost Classifier, Machine Learning, HR Analytics, Explainable AI, Data Science
Journal: Journal of Human Resource Management
Pages: 192-209
Volume: 28
Issue: 2
Year: 2025
File-URL: https://www.jhrm.eu/192-predictive-model-for-reducing-employee-turnover-using-machine-learning-techniques/
File-Format: Application/pdf
Handle: RePEc:cub:journl:v:28:y:2025:i:2:p:192-209