Advancing Employee Attrition Models: A Systematic Review of Machine Learning Techniques and Emerging Research Opportunities

Employee attrition continues to pose significant challenges for organizations worldwide, impacting operational efficiency and incurring substantial costs related to the recruitment and training of replacements. This article proposes an integrated approach that utilizes advanced machine learning techniques and comprehensive data analytics to predict and understand employee turnover more effectively. By combining employee demographics, job characteristics, performance metrics, and organizational factors into a unified analytical framework, this study aims to provide an overview of the factors influencing employee attrition and performance. The research objectives include developing a robust framework for data integration and preprocessing, investigating the role of organizational culture and management practices on employee turnover, and establishing ethical guidelines and privacy-preserving methodologies for data handling. The proposed methodology considers existing datasets, such as the IBM HR Analytics dataset, supplemented by employee surveys and exit interviews, to analyze the impact of various factors on attrition rates. Advanced techniques, including CatBoost and deep learning models, are employed to enhance the predictive accuracy and interpretability of the attrition models. This research addresses critical gaps in current HR analytics practices by integrating technological advancements with a strong emphasis on ethical considerations and data privacy. The findings are expected to provide significant advancements in the fields of HR analytics and data privacy, offering organizations valuable insights and tools to enhance employee satisfaction and retention, thereby promoting organizational success and employee well-being.