Employee Retention and Layoff Prediction Using Machine Learning: A Comparative Study of XGBoost and KMeans

Employee retention and layoff predictions are now one of the big challenges that organizations in different sectors face, especially in highly turning-over industries or those whose labor fluctuation varies according to seasonal trends. The present paper applies the study of machine learning to predict retention and layoff likelihood among the employees by focusing on the performance of two models: XGBoost, a classification model, and KMeans, a clustering model. Using employee tenure, performance ratings, and engagement scores variables, we compare each of the models in their accuracy, interpretability, and forecasting capability at predicting whether an employee is likely to remain or face the threat of layoff. XGBoost highly excels with regard to binary prediction while KMeans is very revealing about what characterization different groups of employees attract. It was able to compare these approaches and then concluded that XGBoost is an enhanced model regarding accurate layoff prediction where it directly gives simple “yes” or “no” outcomes that help in practical HR planning. These results strengthen the potential of machine learning as a material for predictive workforce management, yielding actionable insights for human resource departments and re- asserting strategic workforce planning.