Develop A Model/Software Which Will Help Students to Assess Mental Health of Students, Build Methods to Find Out and Provide Solution for The Improvement

Mental health issues among children and adolescents have become a global public health concern, exacerbated by factors like academic pressure, social comparison, and the post-pandemic impact. This project aims to develop a software-based mental health surveillance platform to assess and track the psychological well-being of students, offering early detection and personalized intervention recommendations. Built using Python, HTML, CSS, JavaScript, and Flask, the system leverages AI and machine learning, including XGBoost for classification and KMeans Clustering for grouping students based on mental health data such as academic performance, attendance, behavior, and mood. The platform predicts risk levels (“Low Risk,” “Moderate Risk,” “High Risk”) and suggests timely interventions for counselors, educators, and parents. The system is accessible via a secure web portal and does not require high-end infrastructure, making it suitable for schools with varying economic backgrounds. Data privacy and ethical AI standards are prioritized to ensure secure, anonymized data storage. The ultimate goal is to create a scalable solution for early intervention, contributing to emotionally resilient youth in line with India’s National Education Policy (NEP) and digital health initiatives.