AI-Powered Workforce Analytics Predicting Employee Turnover Probability

This paper will examine how the random Forest, which is a powerful machine learning technique, can be used to predict employee turnover probability. The retention of employees is a very critical issue to any organization because excessive turnover may result in high expenditure and a loss of productivity. Through random forest, this research would establish a correct predictive model based on different features on employees such as demographics, performance variables, job satisfaction variables and remuneration variables. The ensemble method of random forest which involves the combination of many decision trees guarantees the high accuracy of the method since it helps in reducing the overfitting and complex correlations of the data. This is because the model can deal with both categorical and continuous variables, and hence it is highly applicable in workforce analytics. Findings of the present research point to some of the most significant aspects of employee turnover and give organizational stakeholders meaningful information on what can be done proactively in retention strategies. The tool can be used to assist the HR departments in recognizing the at-risk employees and implementing specific interventions to increase workforce stability.