A Streamlit-based ML app for predicting student academic outcomes (Graduate/Dropout/Enrolled) using a Random Forest model. Includes role-based authentication, modular UI, customizable presets, and human-readable field mappings.

Usage Instructions

1. Launch the Application

streamlit run app.py

The app will open in your browser. If model files are missing, follow the Model Training steps below.

2. Authentication & Roles

Login Required: Enter your username and password to access the app.
Default Admin: Username: admin, Password: admin123
Roles:
- Admin: Full access, can manage users and export data
- Educator: Can export predictions
- User: Can view predictions only

3. Input Forms & Presets

Sidebar Forms: Enter student details using dropdowns, sliders, and text fields.
Presets: Use the preset selector to auto-fill fields for typical student profiles (e.g., "Typical Graduate Student").
- Presets cover academic, family background, and financial/social fields.
Field Mappings: All dropdowns show human-readable names (e.g., country names, not codes), powered by the integrated data dictionary.

4. Prediction Workflow

Fill in Student Data: Complete all required fields. Use presets for convenience or enter custom values.
Submit: Click the Predict button.
View Results:
- Prediction outcome (Graduate/Dropout/Enrolled)
- Confidence score
- Personalized recommendations based on input and prediction
Validation: If input is invalid (e.g., age out of range), warnings will be shown with guidance.

5. Export & Role-Based Features

Export Predictions: Available to Educator/Admin roles. Export results for further analysis.
Session Management: Sessions persist for extended periods, but re-login may be required after browser refresh or timeout.

6. Model Training (If Needed)

If model files are missing or you want to retrain:

cd models
python train_and_save_model.py

This generates:

random_forest_model.pkl
preprocessing_info.pkl
feature_names.pkl

7. Customization & Advanced Usage

Feature Changes: Update src/config/mappings.py and retrain the model.
Presets: Edit PRESET_CONFIGURATIONS in mappings.py to add or modify student profiles.
Styling: Adjust theme in src/config/theme.py.
Authentication: Extend roles in src/utils/auth.py.

Project Structure

app.py: Main Streamlit app
src/components/: UI components (forms, prediction display)
src/config/: Mappings, theme, config
src/utils/: Auth, model utils
models/: ML model training, serialized files
data/: CSV dataset, data dictionary
notebook/: Data analysis notebook

Troubleshooting

Model files missing: Retrain using the steps above.
Authentication issues: Check users.json for correct credentials.
Invalid input: Review warnings and ensure all required fields are filled.

License

This project is licensed under the MIT License - see the LICENSE file for details.