Predictive HR Analytics to Optimize Decision-Making Processes and Enhance Workforce Performance

Abstract

This paper explores the application of predictive analytics in Human Resource Management (HRM) to optimize decision-making processes and enhance workforce performance. The primary objective is to develop a predictive model that identifies key factors influencing employee retention within our organization. Utilizing historical HR data, machine learning algorithms are employed to analyze patterns and forecast potential attrition risks. The methodology involves data pre-processing, feature selection, and model training using a comprehensive dataset spanning employee demographics, performance metrics, and engagement indicators. The model's predictive accuracy is assessed through cross-validation, and the final model is validated using a separate test dataset. Results indicate a significant improvement in the accuracy of attrition predictions compared to traditional methods. Identified risk factors include job satisfaction, career development opportunities, and team dynamics. The project concludes with actionable insights for HR practitioners to proactively address potential retention challenges. This paper demonstrates the transformative potential of data-driven decision-making in HRM. By leveraging predictive analytics, organizations can strategically allocate resources, implement targeted interventions, and foster a more engaged and satisfied workforce