Employee turnover remains a long-standing issue that can lead to significant economic losses and a decline in workplace productivity. Traditional methods for predicting natural attrition, such as surveys and rumour assessment, usually fail to capture the complexity and multi-factorial nature of natural attrition. To address this limitation, this paper proposes to develop an advanced prediction model using Machine Learning (ML) algorithms to enhance the accuracy and reliability of export prediction. These methods are applicable to a full set of data covering parameters such as demographics, workplace performance and survey scores. The objective of this model is to provide a data-driven model that can identify employees at risk of leaving the organization, thereby enabling intervention at the right time and place. The objectives in the paper are to identify employees who are more likely to leave due to work and social experience, second to understand the main variables that lead to employee turnover, and finally to make the model effective in reducing employee turnover and improving employee satisfaction. In this paper, a ML-based approach is introduced to enhance turnover prediction accuracy by integrating comprehensive features and addressing class imbalance demonstrating improved performance and interpretability as compared to previous studies. Through the application of these ML methods, this paper is beneficial to both academic research and practical practice in human resource management. The results of the paper emphasized the data analysis and ML opportunities, to help solve the problem of employee retention, and create a more close, more effective, more motivated staff. In this paper, the proposed ML model performs well in predicting employee turnover by effectively integrating key features as compared with traditional methods, this method has a higher accuracy rate.
Ling et al. (2026) studied this question.