This project integrates a backend built with Flask and TensorFlow and a frontend developed using React. It allows admins to manage machine learning models and pipelines while offering predictions on student persistence based on input data.

• Admin Model Management: Add, view, and switch between multiple models and pipelines.
• Student Persistence Prediction: Provides predictions based on student data.
• Secure Access: Admin pages are protected and accessible only after login.

Follow the instructions below to set up and run the application.

• Python (>= 3.8)
• Node.js (>= 14.x)
• npm (comes with Node.js)

1. Navigate to the Backend Directory
   Open a terminal and move into the backend folder:

   cd backend

2. Create a Virtual Environment
   Create an isolated Python environment:

   python -m venv venv

3. Activate the Virtual Environment

   • On Windows:

     venv\Scripts\activate

   • On macOS/Linux:

     source venv/bin/activate

4. Install Dependencies
   Install the required Python packages:

   pip install -r requirements.txt

5. Add Models and Pipelines

   • Save .keras files for TensorFlow models in the backend/model/ folder.
   • Save .pkl files for pipelines in the backend/pipeline/ folder.

6. Start the Backend Server
   Run the Flask app:

   python app.py

   The backend will be available at http://127.0.0.1:5000.

1. Navigate to the Frontend Directory
   Open a terminal and move into the frontend folder:

   cd frontend

2. Install Dependencies
   Install the necessary Node.js packages:

   npm install

3. Start the Frontend Server
   Run the React development server:

   npm start

   The frontend will be available at http://localhost:3000.

1. Login
   Navigate to the homepage (/) and log in using the provided credentials.

2. Manage Models and Pipelines
   Access /admin to:

   • View available models and pipelines.
   • Switch between models and pipelines.
   • Upload new models (.keras) and pipelines (.pkl).

3. Add Models and Pipelines

   • Save new models to backend/model/.
   • Save new pipelines to backend/pipeline/.

1. Access the prediction form at /predict.
2. Fill in the required student data fields.
3. Submit the form to receive predictions on student persistence.

StudentPersistencePredictor/
├── backend/
│   ├── app.py              # Flask app entry point
│   ├── model/              # Folder for TensorFlow models (.keras)
│   ├── pipeline/           # Folder for pipelines (.pkl)
│   ├── requirements.txt    # Backend dependencies
│   └── ...
├── frontend/
│   ├── src/
│   │   ├── components/     # React components
│   │   ├── styles/         # CSS files
│   │   └── ...
│   ├── package.json        # Frontend dependencies
│   └── ...
└── README.md               # Project documentation

cd backend
python -m venv venv
venv\Scripts\activate        # Windows
source venv/bin/activate     # macOS/Linux
pip install -r requirements.txt
python app.py

cd frontend
npm install
npm start

• Flask
• TensorFlow
• Joblib
• Pandas
• Scikit-learn

• React
• Axios

Contributions are welcome! Feel free to open issues or submit pull requests.

This project is licensed under the MIT License.