Predictive modeling of employee performance using workplace metrics

Abstract

Organizational achievement and productivity depend heavily on how well employees perform their work. However, authorities, especially Human Resources (HR) departments, experience major difficulties when trying to predict performance based on workplace metrics. Traditional human judgment-based performance evaluation methods often rely on subjective assessments, which can introduce biases and inconsistencies. To address this issue, our research focuses on developing a predictive model that leverages workplace data to estimate employee performance with greater accuracy and objectivity. Several factors can be used to accurately predict an employee’s performance. Attendance records, task completion rates, peer feedback, and project efficiency can be considered as key workplace metrics primarily. By using these metrics and machine learning techniques, such as regression and classification models, we seek to uncover patterns and correlations that traditional evaluation methods may overlook. The predictive model will be assessed using performance evaluation metrics such as R2, MAE, RMSE, and MAPE, ensuring its reliability and effectiveness. The main goal of this study is to develop an analytical instrument based on data that enables effective workforce management for both organizational leaders and HR departments. This model can help organizations proactively identify high performers, detect potential areas for employee development, and formulate strategies to improve overall engagement and productivity. Additionally, by integrating ethical AI practices, we ensure fairness and transparency in performance evaluations, reducing biases in decision-making. Our study contributes to the growing field of HR analytics and workforce optimization, paving the way for more efficient, scalable, and unbiased employee performance assessment methodologies.