Predictive Model for Reducing Employee Turnover Using Machine Learning Techniques

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.