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Prediction of Diabetes using Machine Learning Algorithms with Django Framework

This project aims to predict the likelihood of an individual having diabetes using machine learning algorithms implemented within a Django framework. The application provides a user-friendly interface where users can input relevant medical information, and the system generates a prediction based on the provided data.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Technologies Used
• Contributing
• License

Introduction

The Prediction of Diabetes project combines the power of machine learning algorithms and the Django web framework to create a predictive model for diabetes. By utilizing a dataset containing various medical attributes, the system can predict the likelihood of a person having diabetes based on the input provided.

Features

• User-friendly interface for inputting medical information.
• Integration of machine learning algorithms for diabetes prediction.
• Display of prediction results to users.
• Data visualization to provide insights into the prediction model.
• Secure user authentication and authorization.
• User profile management.

Installation

To run this project locally, follow these steps:

1. Clone the repository:

git clone https://github.com/kabuur/Prediction-of-diabetes-using-machine-learning-algorith-whit-Django-framework.git

2. Change into the project directory:

cd Prediction-of-diabetes-using-machine-learning-algorith-whit-Django-framework

3. Create a virtual environment:

python -m venv venv

4. Activate the virtual environment:

• On macOS and Linux:

source venv/bin/activate

• On Windows:

venv\Scripts\activate

5. Install the project dependencies:

pip install -r requirements.txt

6. Run database migrations:

python manage.py migrate

7. Start the development server:

python manage.py runserver

8. Open your web browser and navigate to http://localhost:8000 to access the application.

Usage

1. Create an account or log in if you already have one.
2. Fill in the required medical information in the provided form.
3. Click the "Predict" button to obtain the prediction result.
4. The prediction result will be displayed on the screen.
5. Explore the different sections of the application to gain insights into the prediction model and manage your user profile.

Technologies Used

• Python
• Django
• Machine Learning (Scikit-learn, TensorFlow, or any other relevant libraries)
• HTML/CSS
• JavaScript
• Bootstrap (or any other CSS framework)
• SQLite (or any other database of your choice)

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvement, please open an issue or submit a pull request. Make sure to follow the existing code formatting and style conventions.

License

This project is licensed under the MIT License.

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