Here’s an improved and detailed README for your Diabetes Prediction System:

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Diabetes Prediction System

Overview

The Diabetes Prediction System is a machine learning-based web tool designed to predict diabetes risk using patient data such as age, BMI, glucose, and insulin levels. It aims to assist in early detection, enabling proactive healthcare.

Features

• High Accuracy: Achieves over 85% accuracy using patient health data.
• User-Friendly Interface: Simple input form and fast predictions.
• Quick Processing: Provides results in less than 1 second.

Technologies Used

• Machine Learning: Python, Scikit-learn, Pandas, NumPy
• Web Framework: Flask
• Frontend: HTML, CSS, Bootstrap
• Database: SQLite (or MySQL if needed)
• Deployment: Local or cloud-based

Project Structure

├── app/
│   ├── static/               # CSS and assets
│   ├── templates/            # HTML templates
│   ├── model/                # Trained machine learning model
│   ├── routes.py             # Flask routes
├── data/                     # Dataset (medical records)
├── requirements.txt          # Python dependencies
├── train.py                  # Model training script
├── app.py                    # Main Flask application
└── README.md                 # Project documentation

Setup Instructions

1. Clone the repository:

   git clone https://github.com/prathameshatkare/diabestese-prediction.git
   cd diabestese-prediction

2. Install dependencies:

   pip install -r requirements.txt

3. Download the dataset:

   • Get the diabetes dataset from [link to dataset] and place it in the data/ directory.

4. Train the model:

   python train.py

5. Run the application:

   python app.py

6. Access the web interface:

   • Open your browser and navigate to http://localhost:5000.

Usage

1. Enter medical data (age, BMI, glucose, etc.) in the web form.
2. Receive a diabetes risk prediction along with a confidence score.

Future Enhancements

• Include additional features (family history, lifestyle).
• Deploy to cloud platforms for broader accessibility.
• Integrate with healthcare systems to track patient data.

Contributing

We welcome contributions! Feel free to open issues or submit pull requests for improvements.

License

This project is licensed under the MIT License.

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