Intelligent Human Resource Management: Machine Learning Approaches for Employee Performance and Retention Optimization

This paper explores the application of machine learning (ML) techniques in human resource management (HRM) to optimize employee performance and retention. A hybrid ML framework combining logistic regression, random forest, and gradient boosting is proposed. The model is evaluated on real-world HR datasets, demonstrating significant improvements in predicting employee attrition, performance levels, and identifying high-potential talent. The approach provides HR managers with actionable insights for strategic workforce planning.

کلیدواژه ها:

Retention ، Predictive Analytics ، AI Human Resource Management ، Machine Learning ، Employee Performance ، Employee