MACHINE LEARNING MODELS FOR PREDICTING EMPLOYEE RETENTION AND PERFORMANCE

This paper examines the usage of machine learning models in forecasting performance and retention among employees, important organizational performance elements. Both substandard performance and high turnover are expensive, and in turn, insights based on data are a requirement. The research applies a comprehensive literature review and examines existing literature and finds predictors such as satisfaction, length of service, compensation, and engagement. It establishes a predictive model-building process to efficiently forecast these outcomes. The research establishes such models allow firms to proactively choose, allocate resources in a productive way, and lower costs on turnover. Data privacy, interpretability, and bias are however among the implementation barriers. The paper concludes with a mention on machine learning’s potential in revolutionizing HR analytics, with a systematic process in utilizing insights ethically. It supports future research in ethically aligned AI and real-time predictions and makes a useful contribution in workforce strategy.