A DATA-DRIVEN MACHINE LEARNING FRAMEWORK FOR EMPLOYEE PRODUCTIVITY CLASSIFICATION IN WORK-FROM-HOME SETTINGS

Abstract: – The rapid transition to a work-from-home (WFH) culture has significantly transformed the traditional office environment, introducing new challenges in monitoring and evaluating employee productivity. Unlike conventional workplaces, remote work settings rely heavily on digital interactions, task-oriented workflows, and self-managed time, making productivity assessment more complex. In this study, a machine learning-based approach is employed to classify employee productivity levels in a WFH environment using a publicly available dataset. Work-related behavioral features are analyzed using supervised learning algorithms such as Decision Tree, K-Nearest Neighbors (KNN), and Naïve Bayes. The experimental results demonstrate that these algorithms can effectively classify employee productivity into predefined categories with satisfactory accuracy.

Keywords: Work From Home, Employee Productivity, Machine Learning, Classification, Remote Work.