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AI-powered automation and learning project built for URAAN Tech AI Techathon 2025. Modular, efficient, and open source.

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🌟 TaleemAI — Empowering Education with Artificial Intelligence

Project Lead & Core Developer: Syed Mushahid Ali Kazmi
Mushahid led the entire development lifecycle of TaleemAI — from initial idea, data acquisition, and model design to implementation, testing, and deployment. His dedication shaped the project into a unified AI-powered educational platform.

Collaborative Support & Auxiliary Contributions: Muhammad Abdullah
Muhammad Abdullah supported the initiative through feedback during early development, dataset validation, and assisting with report structuring. His involvement added collaborative depth to the project.

Python License GitHub Workflow Status Platform AI


📊 Datasets & Resources

Source Type Description
Kaggle – Students Performance in Exams CSV Core dataset used for training the classification and forecasting models
Google Colab Cloud Platform Primary environment for model training, testing, and automation
GitHub Actions CI/CD Used for automatic retraining, evaluation, and reporting workflows
Matplotlib / Seaborn Visualization Used to generate plots, figures, and performance visualizations

All dataset references and licenses are documented in resources.md.


📈 Model & Report Workflow

The end-to-end workflow is designed for full automation and reproducibility:

  1. Fetch Dataset → Download from Kaggle via API or manual upload.
  2. Preprocessing → Data cleaning, encoding categorical variables, and feature scaling.
  3. Training → Classification using algorithms like Random Forest and Logistic Regression.
  4. Evaluation → Metrics include confusion matrix and classification report.
  5. Visualization → Performance graphs saved in the /reports/ directory.
  6. PDF Generation → Automated report creation containing all results and visuals.
  7. Automation → GitHub Actions triggers retraining and reporting on every push.

📄 Auto-Generated Report Example

Each generated PDF report (stored in the /reports/ folder) includes:

  • ✅ Dataset summary and structure
  • 📊 Model accuracy, precision, recall, and F1-score
  • 🧠 Confusion matrix visualization
  • 🌿 Feature importance and interpretation
  • 🔮 Forecasting insights for future student performance trends

🤝 Contributors

Name Role Contribution
Syed Mushahid Ali Kazmi Lead Developer System Architecture, AI Modeling, Automation, Documentation
Muhammad Abdullah Technical Support Dataset Validation, Report Design Support

📜 License

This project is licensed under the MIT License — you’re free to use, modify, and distribute it with proper credit.
See the LICENSE file for full details.


⭐ Acknowledgments

A special thanks to the amazing open-source ecosystem and communities:

  • 📚 Kaggle Community for providing publicly available datasets.
  • ☁️ Google Colab for free cloud computing resources.
  • 🔄 GitHub Actions for seamless CI/CD integration.
  • 🐍 Python Community for the incredible libraries that made this possible.

💬 Feedback

Found a bug, have ideas, or want to contribute?
👉 Open an issue or reach out via email — we’d love to hear from you!

AI can’t replace teachers — but it can empower them.


📘 TaleemAI — By Students, For Students.

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AI-powered automation and learning project built for URAAN Tech AI Techathon 2025. Modular, efficient, and open source.

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