A high-fidelity, industrial skeuomorphic web application for monitoring and predicting student academic risk. This system transforms raw academic data into tactical diagnostic readouts using a tactile control panel interface.
The UI is designed as an Industrial Control Panel, featuring:
- Tactile Tactility: Physical bevels, recessed wells, and brushed metal textures.
- Dynamic Telemetry: LED status indicators (Nominal vs. Alert) and glowing technical readouts.
- Interactive Analytics: Enlargable high-resolution charts for variance visualization.
Active System Terminal for data input.
- Predictive Engine: Analyzes Attendance, Midterm Scores, and CGPA to determine unit risk probability.
- Unit Diagnostic Panels: Individual high-fidelity readouts for every student, featuring "System Tech Notes" and personalized monikers.
- Name-Enabled Pipeline: Full support for student names throughout the data stream and UI.
- Data Export/Import: Integrated demo data generator for system calibration and testing.
Tactical Monitoring Log and real-time status telemetry.
Detailed variance visualization with percentage readouts.
- Python 3.8+
- Git
-
Clone/Setup Repository:
git clone <repository-url> cd EWS
-
Configure Environment:
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate pip install -r requirements.txt
-
Calibrate Demo Data:
python scripts/create_demo.py
-
Launch Analysis Stream:
python app.py
Access the terminal at: http://127.0.0.1:5000
app.py: Main Analytics Engine & Flask Server.templates/: Tactical UI layouts (index.html,dashboard.html).static/css/style.css: Skeuomorphic design system & color tokens.models/: Trained logistic regression and decision tree pickels.scripts/: System utility tools (Demo generation).data/: Protected data stream storage (Uploads/Static).
The project is initialized with Git. Sensitive binaries and temporary caches are filtered via .gitignore.