Welcome to the Diabetes Prediction System! This project utilizes machine learning techniques to predict the likelihood of a person having diabetes based on various health parameters. The prediction system is implemented using Django framework along with HTML and CSS for the front-end interface.

Prediction Model: Utilizes a machine learning model trained on historical health data to predict the probability of diabetes. User-friendly Interface: Provides a clean and intuitive web interface for users to input their health information and receive predictions. Accessibility: Accessible via a web browser, making it convenient for users to utilize the prediction system from anywhere. Technologies Used Django: Python web framework used for backend development and routing. HTML: Used for creating the structure and content of web pages. CSS: Utilized for styling and enhancing the visual appearance of the web interface. Machine Learning: Employed for building and deploying the predictive model for diabetes prediction. Setup Instructions To run the Diabetes Prediction System locally on your machine, follow these steps:

bash Copy code git clone https://github.com/aman-tech45/DiabetesPrediction.git Navigate to Project Directory:

bash Copy code cd DiabetesPrediction Install Dependencies:

bash Copy code pip install -r requirements.txt Run the Django Server:

bash Copy code python manage.py runserver Access the Prediction System: Open a web browser and go to http:https://localhost:8000 to access the Diabetes Prediction System.

Input Health Information: On the homepage, fill in the required health parameters such as glucose level, blood pressure, BMI, etc.

Submit Information: Once all the necessary information is entered, click on the "Predict" button to submit the data for prediction.

View Prediction: The prediction result will be displayed on the screen, indicating the likelihood of diabetes based on the provided health information.

Contributions to the Diabetes Prediction System are welcome! If you have any ideas for improvements or feature enhancements, feel free to open an issue or submit a pull request.

This project is licensed under the MIT License.