Predictive Analysis of Student Stress Level using Machine Learning

Abstract

1. College students are suffering from many mental health problems including mental stress, somatization, obsessive, interpersonal sensitivity, depression, anxiety, hostility, fear, paranoia and psychosis, which can bring a lot of negative effects to them. 2. Obviously, the mental health problems of college students not only directly affect their own growth, but also affect the stability of the campus. Colleges increasingly prioritize monitoring and preventing students' psychological crises. 3. All Colleges simply analyze whether students have mental health problems or what kind of problems they have. It fails to uncover underlying relationships within psychological data. 4. We require a comprehensive system to address student mental health concerns, with a specific focus on predicting and managing student stress. 5. There are so many factors related to stress such as work load, assignments, family issues, friends issues, attendance, teaching etc... 6. Machine learning is a subject to predict future based on the past data. By leveraging machine learning techniques, we can accurately predict student stress levels. Furthermore, our proposed system will provide personalized suggestions based on these stress levels