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🦟 Denguard

Denguard is an AI-powered Django web application that predicts dengue outbreak risks and recommends smart municipal actions using machine learning. It leverages real-time weather data, historical case reports, and medical inputs to help both citizens and doctors stay alert and respond proactively.


πŸš€ Features

  • πŸ“ Location-Based Risk Prediction using SVM
  • 🩺 Doctor Dashboard to log real dengue cases and issue medical advice
  • 🌧️ Live Weather Data Integration via OpenWeatherMap API
  • 🧠 Action Recommendation System using Markov Decision Process (MDP)
  • πŸ—ΊοΈ Heatmap Visualization of dengue outbreak zones by area
  • πŸ” Two User Roles: Normal User & Doctor
  • πŸ–₯️ Responsive Web Interface with clean, informative design

πŸ›  Tech Stack

  • Backend: Django, Scikit-learn, Pandas, NumPy
  • Machine Learning:
    • Support Vector Machine (SVM) for outbreak classification
    • Markov Decision Process (MDP) for decision policy
  • Frontend: HTML, CSS, JavaScript, Bootstrap/Tailwind
  • Data Handling: Real-life Dengue Cases + Weather Forecast APIs
  • Visualization: Heatmaps, tables, and dynamic recommendations

πŸ“ Data Sources

  • DGHS Bangladesh β€” Real dengue case reports
  • OpenWeatherMap API β€” Forecasted rainfall, temperature, humidity
  • Remote Sensing / Public Reports β€” Waterlogging and flood-prone areas
  • Manual Doctor Inputs β€” Hospital-reported confirmed dengue cases

🀝 Contribution

Contributions are welcome! Feel free to:

  • Submit a pull request
  • Open an issue
  • Suggest improvements or features

Whether you're a public health expert, developer, or designerβ€”your help is appreciated!


πŸ“œ License

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


πŸ“ž Contact

Author: Maruf Hossain
Department: Computer Science & Engineering
University: Green University of Bangladesh
πŸ“§ Email: maruf.bshs@gmail.com, 221902318@student.green.edu.bd

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Denguard is an AI-powered Django web application that predicts dengue outbreak risks and recommends smart municipal actions using machine learning. It leverages real-time weather data, historical case reports, and medical inputs to help both citizens and doctors stay alert and respond proactively.

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