Machine Learning and HRM: A Path to Efficient Workforce Management

Abstract

This is research on how machine learning algorithms can be used to achieve the best human resources management practices, in particular, those dealing with tasks that include, identification of employee performance, attrition analysis and workforce planning Performances of both Random Forest And Gradient Boosting algorithms were evaluated for Human Resources related activities by analyzing the real world Human Resources datasets during the experiments undertaken. Reported data showed the highest accuracy for the Gradient Boosting algorithm with vacancy rate prediction using MAE and RMSE performance metrics, which indicated the best results. Therefore, exactly like Gradient Boosting holds a superior measure of accuracy, precision, recall, and F1-score against Random Forest in attrition analysis. In workforce planning, Gradient Boosting showed only slightly less Mean Absolute Percentage Error (MAPE) and Root Mean Squared Percentage Error (RMSPE) values as compared to both approaches that were applied, which points to the superior performance of forecasting methods. The above results certainly show machine learning algorithms can be very successful in HRM tasks and that data-driven decision-making could improve performance while also strengthening it with a huge deal.