Predicting Employee Performance using Machine Learning to Enhance Workforce Efficiency

The aim of this study is to develop a machine learning model that utilizes historical employee data to predict future performance within organizational contexts. To achieve this, employees are categorized into three distinct groups: high performers, moderate performers, and low performers. The objective is to use this data to enhance talent management and decision-making in organizations. Building the machine learning model involves addressing challenges such as data quality, bias, and interpretability. Compared to current methods, anticipated outcomes involve achieving higher levels of accuracy, computational complexity, and adaptability. However, ethical considerations, including fairness and transparency, remain essential to this study. Hence, the aim of this study is to offer a flexible and forward-looking approach that adapts to changing organizational dynamics, meeting companies' needs for innovative approaches to workforce management.