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:
- 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.pyfile.
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. Here are step by step commands you can run in your windows machine
python -m venv venv
.\venv\Scripts\activate
pip install -r requirements.txt
streamlit run app2.py #for streamlit app
python app.py #for flask app