Mental Health Prediction of Students Using Machine Learning Method

In India, student dropout rates are influenced by a myriad of economic, social, and institutional factors, highlighting the urgent need to address mental health among students. This study presents a novel approach to assessing students' emotional well-being by integrating questionnaire-based assessments with visual data analysis. Utilizing machine learning techniques, we identify trends in students' responses, providing feedback to promote mental health awareness and support. Our platform fosters a positive student community, bridging empathy with technology. The proposed methodology leverages a unique dataset to facilitate the early detection of mental health issues, enhancing the accuracy and reliability of predictions. This paper identifies thematic clusters in students' emotional states, underscoring its innovative contributions to the field. By implementing a robust data collection process and ensuring high-quality visualizations, this study aims to set a new standard for mental health evaluation among students, ultimately striving for improved mental health outcomes