Predictive HR Analytics: Using Machine Learning to Forecast Workforce Trends and Needs

The present research paper aims to analyze the use of predictive HR analytics integrated with machine learning algorithms for the anticipation of workforce demand and requirements. Thus, based on Kaggle’s IBM HR Analytics and Employee Satisfaction Index datasets, the study uses Decision Trees and Random Forest approaches to develop the model and predict essential HR parameters, including attrition rates, job satisfaction, and performance levels. This paper illustrates that with the use of machine learning, it is possible to extract valuable information from the data concerning employees and apply them to the field of Human Resource Management to improve the efficiency of decision-making. The results identify the future opportunities of predictive analytics by illustrating its application for HR functions to reduce the efforts toward proactive management, enhance the methods for recruitment, and enhance the approaches to retention of employees. Thus, the findings of this study enrich the body of knowledge regarding the application of machine learning in improving the field of human resource management, specifically in identifying the best approaches for connecting HR efforts with the strategic objectives of organizations.