Risk Prediction Model of Human Resource Management Based on Data Mining

This paper aims to build a Human Resource Management (HRM) risk prediction model based on random forest algorithm to help enterprises identify and manage human resource risks more effectively. By collecting historical data related to human resource risks, preprocessing and feature extraction, a risk prediction model is trained by using random forest algorithm. The experimental results indicate that the model exhibits high prediction accuracy, approximately 0.96, and demonstrates exceptional predictive capability across various risk types, successfully identifying potential human resources risks. Comparative experiments with other prevalent machine learning algorithms further underscore the advantages of random forest in risk prediction. This study offers enterprises a practical risk management tool and introduces a novel perspective and methodology for applying data mining in HRM.