ig-pusdekar-sahil/Mental-Health-Score

# Mental Health Score Mansik-Santulan-Score is a full-stack web application and machine learning project that predicts a student's mental health score based on various lifestyle and social media usage factors. ## Overview The project uses a trained machine learning model to analyze student data such as: - Age and Gender - Academic Level & Country - Social media platform preferences and usage hours - Study, physical activity, and sleep hours - Self-reported stress levels It provides an intuitive web interface for users to enter their habits and instantly receive an estimated mental health score out of 10, complete with a gauge visualization and feedback bands (strained, balanced, strong). ## Project Structure - **Frontend (Static UI)**: HTML, CSS, and Vanilla JavaScript (`index.html`, `style.css`, `script.js`). The UI features interactive elements and real-time form validation. - **Backend (API)**: A fast, lightweight Python server using FastAPI (`main.py`) and Pydantic for data validation. - **Machine Learning**: - `Mental_Health_Model.pkl`: The trained predictive model (built using scikit-learn). - `ML_Project.ipynb`: Jupyter notebook containing the exploratory data analysis and model training pipeline. - `Student Social Media And Mental Health Impact.csv`: The dataset used to train the model. ## Running Locally ### Prerequisites - Python 3.8+ - A modern web browser ### 1. Start the Backend API 1. Open a terminal in the project directory. 2. Install the required Python dependencies: ```bash pip install -r requirements.txt ``` 3. Run the FastAPI server using Uvicorn: ```bash uvicorn main:app --reload ``` *The API will start running at `127.0.0.1`.* ### 2. Start the Frontend 1. Open the `script.js` file. 2. For local testing, ensure the `API_BASE` points to your local server: ```javascript const API_BASE = "127.0.0.1"; ``` 3. Simply open `index.html` in your web browser. Alternatively, you can serve it with a local HTTP server: ```b …