🎓 Student Depression Prediction Web App

This is a web-based application that predicts the likelihood of depression among students based on their inputs. It uses a trained Neural Network model and provides SHAP-based visual explanations for transparency.

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Features

• Predicts depression level using a trained ML model
• SHAP visualizations to explain prediction outcomes
• Simple and interactive web interface using Flask

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Built With

• Backend: Python (Flask, NumPy, Pandas, TensorFlow, SHAP)
• Frontend: HTML/CSS (Jinja templating)
• Visualization: Matplotlib, Seaborn

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Installation

1. Clone the Repository

git clone https://github.com/Periyzat/Student-Depression-Prediction-Web-App.git
cd Student-Depression-Prediction-Web-App

3. Create & Activate a Virtual Environment

python -m venv venv
source venv/bin/activate  # for Linux/macOS
venv\Scripts\activate     # for Windows

5. Install Dependencies

pip install -r requirements.txt

7. Run the Application

python app.py

Usage

1. Open your browser and navigate to: http://127.0.0.1:5000/
2. Fill in the form with relevant personal details.
3. Submit the form to receive a prediction along with a SHAP explanation chart.

Model Details

• Model: Deep Neural Network (my_model.keras)
• Training Dataset: Depression Student Dataset on Kaggle
• Explainability: SHAP values are used to interpret model decisions.

Project Structure

Student-Depression-Prediction-Web-App/
├── app.py                   # Flask app
├── model/
│   ├── model.py             # Model loading and prediction functions
│   └── my_model.keras       # Trained deep learning model
├── dataset/
│   └── Depression Student Dataset.csv
├── templates/
│   ├── index.html           # Home page form
│   └── result.html          # Prediction + SHAP result
├── static/
│   └── depression_plot.png  # Visualization asset
├── requirements.txt         # Project dependencies
└── README.md                # Project overview

Contributing

Contributions are welcome! Please open issues or submit a pull request for improvements or bug fixes.

License

This project is licensed under the MIT License.