Interactive streamlit application to use the python library mloptimizer

Before you start, make sure you have Docker and, of course, your favorite IDE installed.

1. Clone this repo to your local machine
2. Go to mloptimizer-app and build a Docker image from the Dockerfile provided:

cd ./mloptimizer-app/
docker build -t mloptimizer-img .

3. Run a new container and start using the app

docker run -dp 127.0.0.1:8000:8501 --name mloptimizer-app mloptimizer-img

Take into account that Streamlit app uses port 8501 of your new container, and it is mapped to your localhost 8000 port. You can edit command above to use a different port of your local machine. 4. You can now view the MLOptimizer App in your browser. Open your favorite browser and go to http://localhost:8000/

Forget about huge python scripts, maintenance of libraries on your machine and difficult commands of a CLI. With this GUI, you can easily upload your CSV dataset and search for the best hyper-parameters for different cases. You'll be able, for example, to choose between different algorithms, specify amount of generations and individuals, set ranges of values for the hyper-paramaters or even decide which ones you want to keep fixed.