# Kidney Disease Prediction Web Application A comprehensive Django-based web application for predicting kidney disease using machine learning. The application includes features like user authentication, a chatbot for assistance, diet planning, and detailed health metrics analysis. ## 🌟 Features - **Disease Prediction**: Advanced machine learning model for kidney disease prediction - **User Authentication**: Secure register and login system - **Interactive Chatbot**: AI-powered assistance for users - **Diet Planning**: Personalized diet recommendations - **Health Metrics Tracking**: Record and monitor various health parameters - **Blog Section**: Informative articles about kidney health - **User Dashboard**: Manage and view health records - **Responsive Design**: Works on all devices ## 🛠️ Tech Stack - **Backend**: Python 3.x, Django - **Database**: SQLite3 - **Machine Learning**: Scikit-learn - **Frontend**: HTML5, CSS3, JavaScript - **UI Framework**: Bootstrap - **Data Processing**: Pandas, NumPy ## 📁 Project Structure ``` kideny/ ├── app3/ # Main application directory │ ├── migrations/ # Database migrations │ ├── templates/ # HTML templates │ ├── forms.py # User and member forms │ ├── models.py # Database models │ ├── urls.py # URL configurations │ └── views.py # View functions ├── static/ # Static files (CSS, JS, Images) ├── templates/ # Global templates ├── manage.py # Django management script ├── model.joblinb # Trained ML model └── db.sqlite3 # SQLite database ``` ## 🚀 Installation 1. Clone the repository: ```bash git clone cd kideny ``` 2. Create and activate virtual environment: ```bash python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate ``` 3. Install dependencies: ```bash pip install django scikit-learn joblib pandas numpy ``` 4. Apply database migrations: ```bash python manage.py migrate ``` 5. Run the development server: ```bash python manage.py runserver ``` ## 💻 Usage ### Prediction System 1. Login to your account 2. Navigate to the prediction form 3. Enter health metrics: - Blood Pressure - Specific Gravity - Albumin - Sugar - Blood Glucose Random - Blood Urea - Serum Creatinine ### Additional Features - **Chatbot**: Access AI assistance through the chatbot interface - **Diet Plan**: Get personalized diet recommendations - **Blog**: Read informative articles about kidney health - **Health Records**: Track and manage your health metrics ## 🔒 Security Features - Secure user authentication - Protected health data - Session management - Form validation ## 🤝 Contributing 1. Fork the repository 2. Create your feature branch (`git checkout -b feature/AmazingFeature`) 3. Commit changes (`git commit -m 'Add AmazingFeature'`) 4. Push to branch (`git push origin feature/AmazingFeature`) 5. Open a Pull Request ## 📝 License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ## 📞 Contact Your Name - your.email@example.com Project Link: [https://github.com/yourusername/kideny](https://github.com/yourusername/kideny) ## 🙏 Acknowledgments - Medical data providers - Machine learning model contributors - Open source community - UI/UX designers --- ⭐️ Star this repository if you find it helpful! # diesese_diagonosis