IDENTIFICATION AND MANAGEMENT OF EMPLOYEES STRESS LEVELS IN IT SECTOR
Keywords:
Data Science, Machine Learning, Stress, IT ProfessionAbstract
The contemporary world is fraught with conflict, leading to widespread stress for various reasons. Numerous factors influence human stress levels, with IT sector employees being particularly susceptible due to work pressure, overload, and higher employee dominance. Changing lifestyles and work environments have further increased stress risks, despite corporate efforts to improve mental health support. This study explores the stress patterns among working adults and uncovers significant factors affecting stress levels, employing machine learning methods. While substantial research on stress prediction exists, many studies lack practical implementation. Data science methods effectively process training datasets and predict stress efficiently. The suggested framework evaluates parameters such as gender, age, family history, health issues, working hours, tech role, and leave acquisition to assess employee stress levels. Using Visual Studio and SQL Server, this real-time application is developed to help IT companies monitor and manage employee stress more effectively. By offering a holistic approach, this system seeks to improve workplace well-being and productivity.
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