Streamlit Healthcare Machine Learning Data App

Objective

1. How easy is it to create a Web App using Streamlit
2. Integrating multiple #machinelearning technologies in the same app
3. Code reusability
4. Streamlit functions & feature usage

of-course Dockerize!

Running the App

1. Checkout the code

git checkout

2. Build the docker image

docker build --tag streamlit-healthcare:1.0 .

3. Run the docker image

docker run -it -p 8501:8501 streamlit-healthcare:1.0

4. Browse the url

Features

• Load Healthcare data from Kaggle https://www.kaggle.com/sulianova/cardiovascular-disease-dataset
• Use scikit-learn ML lib to run classification.
• Provide Tuning param options in the UI
• Provide Switch to enable PySpark
• Provide Pyspark MLlib options over the same data, technically one can compare the result between 2 seperate libraries
• Plotting using Seaborn chart

Conclusion

Streamlit is essentially a very straightforward easy library to create python based Webapp. I am Convinced 👏👏👏