Machine Learning-Based Employee Productivity Assessment Using Random Forest Classification

Abstract

Employee productivity assessment is a critical challenge in organizational management, yet traditional methods often lack objectivity and scalability. We propose a data-driven approach that employs machine learning to classify employee productivity into High, Medium, or Low categories based on performance metrics such as task completion, working hours, attendance, and efficiency. The proposed system uses a Random Forest classifier, which aggregates predictions from multiple decision trees trained on randomized subsets of data and features, thereby improving robustness and accuracy. The methodology involves preprocessing employee data, splitting it into training and test sets, and training the model to predict productivity levels. Experimental results demonstrate that the system provides actionable insights for decision-making, enabling organizations to identify and address productivity bottlenecks effectively. Moreover, the Random Forest approach outperforms conventional methods by handling non-linear relationships and reducing overfitting. The significance of this work lies in its potential to transform subjective productivity evaluations into an automated, data-centric process, fostering fairer and more efficient workforce management. This study contributes to the growing body of research on machine learning applications in human resource analytics, offering a practical solution for modern enterprises.