The Student Progress Tracker is an intelligent system designed to track, analyze, and visualize student performance efficiently. Using AI-powered insights, this platform helps educators and students assess academic growth, identify weak areas, and improve learning outcomes.

📌 Live Demo: Click Here

✅ AI-Based Performance Analysis – Get AI-driven insights into student progress based on past performance. ✅ Personalized Student Dashboard – Each student gets a dedicated profile with progress charts, reports, and performance insights. ✅ Attendance Tracking – Automatically track attendance and generate insightful statistics. ✅ Automated Report Generation – Generate PDF reports for student progress, attendance, and overall performance. ✅ Role-Based Access – Separate dashboards for students, teachers, and administrators. ✅ Data Visualization – Interactive charts and graphs for better performance analysis. ✅ Secure Authentication – Uses email-based login and Django authentication to protect user data.

The project is well-structured and modular for scalability and maintenance:

📂 students/ → Manages student profiles, attendance, and performance reports. 📂 teachers/ → Handles teacher accounts, student evaluations, and class management. 📂 classroom/ → Contains AI models, progress-tracking logic, and result analytics. 📂 templates/ → HTML templates for dashboards, student profiles, and analytics. 📂 static/ → CSS, JavaScript, and images for UI enhancements.

💻 Backend: Django (Python) – Robust and scalable framework for handling student data. 🎨 Frontend: HTML, CSS (Bootstrap), JavaScript – Clean UI with responsive design. 📊 Database: SQLite (default), but supports PostgreSQL/MySQL for production. 🧠 AI Integration: Machine Learning models for performance prediction (future enhancement) currently OpenAi api used. 🚀 Hosting: Deployed on pythonanyhere.

✔ Python 3.7+ installed ✔ Virtual Environment (recommended for dependency isolation)

1️⃣ Clone the Repository

2️⃣ Create a Virtual Environment

3️⃣ Install Dependencies

4️⃣ Run Database Migrations

5️⃣ Create a Superuser (For Admin Access)

6️⃣ Start the Development Server

🔗 Open in your browser: http://127.0.0.1:8000

✅ Add students and assign them to classes. ✅ Track attendance and generate automated reports. ✅ Analyze student performance and identify weak areas.

✅ View personal dashboards and performance analytics. ✅ Download detailed reports for self-improvement. ✅ Get AI-powered insights on study focus areas.

📌 Student Dashboard

📌 Teacher Performance Overview Track student attendance, evaluate progress, and manage classes efficiently.

📌 AI-Generated Insights Understand students’ strengths and weaknesses through AI-powered suggestions.

To deploy this project on a live server:

🔹 Set DEBUG=False in settings.py for security. 🔹 Configure a production database (PostgreSQL/MySQL recommended). 🔹 Run collectstatic to gather static files:

🔹 Deploy on PythonAnywhere, AWS, or Heroku.

Contributions are welcome! 🎉

1️⃣ Fork the repository 2️⃣ Create a feature branch: git checkout -b feature/your-feature-name 3️⃣ Commit your changes: git commit -m 'Added new feature' 4️⃣ Push to the branch: git push origin feature/your-feature-name 5️⃣ Open a Pull Request!

📝 This project is licensed under the MIT License. See the LICENSE file for more details.

💡 Project Lead: Siddharth Nama

💙 If you find this project helpful, don't forget to ⭐ the repository!