International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024
p-ISSN: 2395-0072
www.irjet.net
Stress Prediction in Working Employees Using Machine Learning Dr. H K Chethan1, Ms. Chaithrashree S2, Mr. Likith Rao A3, Ms. Pavithra S4, Ms. Syeda Aalia Zareen5 1 Professor, Dept. of Computer Science and Engineering, Maharaja Institute of Technology, Thandavapura
2,3,4,5Students, Dept of Computer Science and Engineering, Maharaja Institute of Technology, Thandavapura
---------------------------------------------------------------------***--------------------------------------------------------------------real-time through a browser-based application. By Abstract - In today's IT landscape, employee stress is a very significant concern, impacting mental health and workplace productivity. In this project, a real-time application that uses profiles to forecast stress levels in working professionals is introduced. Stress is difficult for the present manual procedures to identify, hence an automated solution is required. Our suggested approach uses the norms of data science classification to divide employees into two groups: Stressed and Stress-Free. By proactively managing employee stress, the main objective is to improve decision-making procedures and, eventually, corporate the final results related to stress. Visual Studio and SQL Server were used to create the system, w hich is a browser-based program that can be accessed by many users and locations. This project supports the larger goal of emphasizing employee well-being inside the organization in addition to addressing the urgent need for stress prediction. Key Words: Real-time application, Profiles, Forecasting, Stress levels, IT professionals, Automated solution, Data science, Classification, Norms, Proactive management.
1.INTRODUCTION In the ever-evolving landscape of the Information Technology (IT) industry, the wellbeing of employees is a crucial aspect. IT professionals often face mental health issues namely stress, depression, and interpersonal sensitivity. Despite efforts by industries to address these issues, manual identification and intervention remain the norm. Our project seeks to revolutionize this approach by introducing an automated Stress Prediction System for IT employees.
Impact Factor value: 8.226
The system is a real-time program designed to forecast an employee's stress level while they are at work. The working employee is categorized by the model as either Stress or Stress free. Better decision-making and business improvisation are included in the scope. 1.2 MOTIVATION TO TAKE UP THE PROBLEM The inspiration for resolving the issue of articulation of stress expectations among working representatives in IT organizations lies in the critical effect that pressure has. Worker stress is a typical issue in the high-speed IT business of today, yet it is much of the time neglected in light of the fact that manual methods are utilized to recognize it. The criticalness and significance of this examination are featured by the current condition of pressure expectation, which needs robotization. We get the opportunity to foster a framework that proactively recognizes and oversees pressure in IT experts, advancing a more certain and useful workplace, by using the force of innovation and information science.
The trouble of really perceiving pressure pointers and evaluating different worker profiles makes it challenging to expect representative pressure in IT associations utilizing AI calculations. Right now, the absence of a computerized framework worsens the issue, contingent just upon manual techniques that every now and again find it challenging to recognize little signs of pressure. Conquering hindrances such as information assortment and quality, including choice, model preparation, and interpretability, is important to foster areas of strength for a Moreover, the framework should be adaptable and functional for use in genuine work settings. To propel worker prosperity endeavors and amplify hierarchical execution, these issues should be settled.
This project's scope extends to the business sector, specifically targeting stress prediction within the IT industry. The proposed system is an automation solution accessible in
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1.1 OBJECTIVE
1.3 CHALLENGES TO BE ADDRESSED
The current manual system struggles to promptly identify stress among employees, leading to a lack of timely intervention. Our solution aims to fill this gap with a realtime application using data science techniques to predict the stress levels based on working employee profiles. By leveraging "classification rules," we aim to provide a userfriendly tool for categorizing employees into stress and stress-free groups.
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addressing the pressing issue of employee stress, our project strives to create a positive impact on the well-being and productivity of IT professionals, fostering a healthier work environment.
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