PREDICTIVE WORKFORCE ANALYTICS FOR EMPLOYEE RETENTION - Rajeev KR; Bhullar, Gagandeep; Mishra, Shyam K; Malathy, Manju; Kalidindi, Sri Ranga Lakshmi; Patankar, Tejashree Prashant; Babu, S. B. G. Tilak (ShodhKosh: Journal of Visual and Performing Arts, 2026)
Predictive Workforce Analytics to Retention has become one of the strategic moves to solve the increasingly becoming problem of employee attrition in the organization. Traditional approaches tend to be retrospective, and they are not able to proactively detect at-risk employees. To address the limitations associated with the current research, the given work offers to use machine learning-based predictive modeling, namely a Random Forest algorithm. The model is also able to predict possible turnover more accurately by incorporating various sources of data including employee demographics, performance indicators, engagement rates and organizational indicators. The developed approach will improve the decision-making process by offering practical information to HR managers to apply specific retention approaches. This model is much better than traditional statistical methods in both predicting better and processing the complex, non-linear relationships in the data. The results indicate that predictive analytics may contribute to a significant decrease in the attrition, enhance labor stability, and promote sustainable organizational development with the help of the data-driven human resource management.