Diabetes Prediction System

Description

The Diabetes Prediction System is a machine learning project designed to predict the likelihood of developing diabetes based on various health metrics. The system uses the Pima Indian Diabetes dataset to train a logistic regression model, providing a user-friendly interface for making predictions.

Installation

1. Clone the Repository

   git clone <repository-url>
   cd DiabetesPrediction
   

2. Install Dependencies

   Ensure you have Python 3.x installed. Install the required packages using:

   pip install -r requirements.txt
   

3. Setup Dataset

   Download the Pima Indian Diabetes dataset from Kaggle:

   Pima Indian Diabetes Dataset

   Place the dataset in the DiabetesPrediction directory. Make sure the dataset file is named diabetes.csv or update the path in views.py accordingly.

4. Run the Project

   Start the Django development server:

   python manage.py runserver
   

   Navigate to http://127.0.0.1:8000/ in your browser to access the application.

Usage

1. Home Page

   Visit the home page to get an overview of the Diabetes Prediction System. Click on "Let's go" to proceed to the prediction page.

2. Prediction Page

   On the prediction page, enter the required health metrics and submit the form. A popup will display the prediction result.

3. Dataset Details

   The dataset used for training and testing the model includes:

   • Pregnancies: Number of pregnancies
   • Glucose: Plasma glucose concentration
   • Blood Pressure: Diastolic blood pressure
   • Skin Thickness: Triceps skin fold thickness
   • Insulin: 2-Hour serum insulin
   • BMI: Body mass index
   • Diabetes Pedigree Function: Diabetes pedigree function
   • Age: Age in years
   • Outcome: Binary outcome variable indicating diabetes presence (1) or absence (0)

Contributing

If you’d like to contribute to this project:

1. Fork the Repository

   Click the "Fork" button on GitHub to create your own copy of the repository.

2. Create a New Branch

   git checkout -b <feature-branch>
   

3. Make Changes

   Implement your feature or bug fix.

4. Commit Your Changes

   git add .
   git commit -m "Description of changes"
   

5. Push to Your Fork

   git push origin <feature-branch>
   

6. Submit a Pull Request

   Go to the repository on GitHub and submit a pull request detailing your changes.

License

This project is licensed under the MIT License.

Credits

Developed by Yoon Thiri Ko. Special thanks to YouTube tutorials and Kaggle for the dataset.