ML Model Deployment in Flask API

This project is a simple classification machine learning project aimed at predicting whether a person suffers from diabetes based on parameters such as blood sugar level, BMI, and blood pressure. However, the primary goal of this project is to create a Flask REST API, allowing access to the machine learning algorithm from any backend system.

Table of Contents

• Use Case
• Usage
  • Setup
  • Running the Program
  • Accessing the API
• Note
• API Link
• License
• Author

Use Case

This project's use case includes:

• Integration with other backend systems: The Flask API allows seamless integration with various backend systems, enabling the prediction of diabetes status.
• Healthcare applications: Healthcare providers can use the API to incorporate diabetes prediction into their systems, aiding in early diagnosis and treatment.
• Research purposes: Researchers can access the API to study diabetes prediction algorithms and develop new insights into the disease.

Usage

Setup

1. Clone the repository to your local machine.
2. Install the necessary dependencies by running:

   pip install -r requirements.txt

Run the program using Gunicorn:

  gunicorn app:app

Accessing the API

To access the API:

• Use Postman or any other API testing tool.
• Send data in the correct format as specified in the app.py file.
• Refer to the API link provided to interact with the deployed project directly.

Note

• This is an API project with no graphical user interface (UI).
• The program can be run locally or accessed via the deployed API link.
• Ensure data is sent in the correct format for accurate predictions.

API Link

The deployed project is accessible via the following API link: API Link

License

This project is licensed under the MIT License. See the LICENSE file for details.