AI-Based Employee Stress Detection

Employee well-being significantly
affects productivity, job satisfaction, and the
success of organizations. Many companies
struggle to monitor mental health and
engagement effectively. Traditional methods like
manual surveys and HR interviews often miss the
mark on accuracy, efficiency, and scalability. To
tackle this problem, this project introduces a webbased Employee Well-Being Prediction System.
It combines structured psychometric surveys with
machine learning to deliver automated, datadriven insights into workplace well-being. The
system uses the Python Flask framework and an
SQLite database for secure user management. It
also stores employee responses in Excel. Features
include secure login and registration for
employees and administrators, structured survey
modules, automated score calculation, and
predictive modeling of employee well-being.
Survey responses are gathered across six areas:
recognition, workplace environment, team
support, career growth, work-life balance, and
emotional state. At the heart of the system is the
Decision Tree algorithm, chosen for its simplicity
and effectiveness in classification tasks. The
model analyzes section scores and creates clear
decision rules to classify employees' well-being
status, ensuring transparency and accuracy in
predictions. In addition to making predictions, the
system improves usability through visual outputs
like pie charts to show score distributions. An
integrated admin dashboard lets HR professionals
monitor employee responses, track well-being
trends, and spot potential risks such as
disengagement or burnout. By merging
psychometric assessment with Decision Tree
modeling, the project offers organizations a
practical tool to improve workplace conditions,
enhance employee support, and make informed
HR choices. This method shows how web
technologies and machine learning can work
together to create smart solutions for employee
engagement, offering immediate insights and
laying the groundwork for future improvements
like interactive dashboards and advanced AI
models.