Detection and Analysis of Stress in IT Professionals by Using 5ML Techniques

Abstract

Abstract: Our project's primary objective is to use ML techniques to detect stress in IT employees. Our approach is an advancement over prior stress recognition methods that lacked personal counseling, but it now includes an analysis of employees and the recognition of occupational stress in them, as well asproviding them with appropriate stressmanagement remedies via a survey form that is sent out on a regular basis. Our system is primarily focused on stress management and creating a healthy and creative job atmosphere in order to get the most out of them through work time. This research focuses on the construction of an intelligent system that uses ML to determine if a person is stressed or not stressed. The data for this study was collected from more than 600 maleand female volunteers between the ages of 18 & 50. The acquired data consists of five (5) distinguishing traits (i.e. systolic blood pressure, diastolic blood pressure, glucose and gender). Employing Python IDE and sci-kit learn ML libraries, an autonomous system was constructed using ML techniques for categorization such like Linear Regression (LR), K-Nearest Neighbor (KNN), Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM). To find the ideal settings for each algorithm, Jupyter Notebook was used to improvements in service delivery using Grid search. The most important features connected to a person's stress condition are identified using feature selection technique. With an optimised training-testing average accuracy of 95.00 percent - 96.67 percent, the results we predict if one individual is stressed or not stressed after optimization.