Smart Employee Monitoring System: Machine Learning-Driven Workforce Analytics and Risk Prediction

Abstract

Employee attrition, burnout, and low engagement can affect workforce stability and organizational performance. This paper presents a Smart Employee Monitoring System (SEMS) that combines machine learning, explainable AI, and natural language processing to support employee-related analysis. The system evaluates employee information to predict attrition risk and identify possible burnout conditions using multiple factors such as work patterns, job satisfaction, and stress indicators. Six machine learning algorithms Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, Naive Bayes, and KNearest Neighbors are used for attrition prediction, with Random Forest achieving approximately 92% accuracy in the project evaluation. The system also uses SHAP-based explanations to show the factors influencing predictions and TextBlob-based sentiment analysis to analyze employee feedback. A role-based architecture provides separate interfaces for Admin, HR, and Employee users. The application was developed using a Flask backend, React frontend, and database-supported employee management modules. Testing covered authentication, APIs, frontend functions, database operations, and machine learning components, with 28 test cases successfully completed. The proposed system provides a unified approach for analyzing workforce risks and supporting data-informed HR decisions.