Developed an AI-driven tool to predict and assess mental health status, specifically targeting depression among students. This project leverages machine learning to identify depression likelihood based on diverse input features, offering personalized insights and actionable recommendations. It provides an accessible, transparent, and proactive solution for mental health management, benefiting students, counselors, and educational institutions. Key Accomplishments: Early Detection: Built a neural network model using TensorFlow, trained on a dataset of 27,000 student records, to accurately predict depression risks before symptoms escalate. Personalized Insights: Designed a system to analyze factors like sleep duration, academic pressure, and dietary habits, delivering tailored recommendations for individual users. Model Interpretability: Integrated LIME to provide transparent feature importance analysis, enabling users to understand prediction reasoning. User-Friendly Interface: Created an intuitive Streamlit-based web application for seamless data input and result visualization, offering both quick and detailed analysis modes. Proactive Support: Developed a system to provide actionable next steps, including self-care strategies and professional consultation prompts, fostering preventive mental health care. Data Preprocessing: Implemented robust preprocessing steps, including handling missing values, feature encoding, and data filtering, to ensure high-quality input data. Technologies Used: Python, TensorFlow, Streamlit, Google Generative AI, Matplotlib, LIME, scikit-learn, pandas, NumPy