Machine Learning-Based Employee Performance Rating System

Abstract

Employee performance evaluation is a critical component of organisational management, directly influencing productivity, promotion decisions, and workforce optimisation. Conventional appraisal systems are often subjective, inconsistent, and limited in their ability to process multidimensional data. This study proposes a machine learning-driven framework for predicting and classifying employee performance based on historical organisational datasets.

The proposed system integrates structured data sources, including attendance records, task completion metrics, behavioural assessments, and project contributions, to develop predictive models capable of generating accurate performance ratings. Advanced classification algorithms, specifically Random Forest and Logistic Regression, are employed and evaluated using standard performance metrics, including accuracy, precision, recall, and F1-score.

To ensure practical applicability, the predictive model is deployed within a scalable web-based architecture, enabling real-time performance evaluation through an intuitive user interface. Experimental results demonstrate that the Random Forest model achieves superior performance with an accuracy of up to 88%, outperforming baseline methods.

The proposed approach enhances objectivity, reduces human bias, and enables data-driven decision-making in human resource management. This research highlights the potential of machine learning techniques in transforming traditional performance appraisal systems into intelligent and automated evaluation frameworks.