Diabetes Prediction Using SciKit-Learn

This project aims to predict the onset of diabetes based on various diagnostic measures. It leverages the power of machine learning, specifically a Random Forest Classifier from the SciKit-Learn library, to make these predictions.

The project includes two distinct implementations:

1. Flask Application: A web-based application built with the Flask framework. This application provides a user-friendly interface to input the diagnostic measures and receive a prediction. The Flask application is implemented in the app.py file.

2. Streamlit Application: Another web-based application, this time built with the Streamlit framework. Streamlit allows for rapid prototyping and interactive data exploration, making it a great tool for this project. The Streamlit application is implemented in the app2.py file.