Mental Health Risk Prediction System for Employees in the Tech Industry

Mental health problems of employees in the technology sector over the recent years have led to a great demand for systems that would monitor these issues proactively and intervene early. This is a project on a Machine Learningbased Mental Health Risk Prediction System to predict and categorize employees' mental health risk as High, Moderate, or Low. The system uses the demographic, workplace, and behavioral attributes of the person, e.g., age, work interference, benefits, family history, and organizational support factors. To get the correct classification, an Extra Trees Classifier model is trained on curated mental health datasets. The trained model is combined with a Django-based web application, which has a userfriendly interface made with HTML, CSS, JavaScript, and Bootstrap. The platform lets employees enter their personal and workplace details, gives the prediction of their mental health risk, and the result is stored securely in an SQLite database. A dedicated admin dashboard comparing employee wellbeing metrics across companies visualizes the data with the help of Chart.js and Plotly. Later on, there can be an AI-powered chatbot to spread mental health awareness and detect emotions by analyzing the face to extend the organizational well-being monitoring. The proposed system is committed to supporting technology companies in identifying those who are at risk early and creating a culture of mental wellness in the workplace.