mljar-supervised VS studio

Web App is using two open-source packages that I've created:

- MLJAR AutoML - Python package for AutoML on tabular data https://github.com/mljar/mljar-supervised

- Mercury - framework for converting Jupyter Notebooks into Web App https://github.com/mljar/mercury

You can run Web App locally. What is more, you can adjust notebook's code for your needs. For example, you can set different validation strategies or evalutaion metrics or longer training times. The notebooks in the repo are good starting point for you to develop more advanced apps.

It's an Automated Machine Learning python package. It's open-source, you can see how it works on GitHub: https://github.com/mljar/mljar-supervised

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If you need a simple and fast solution, go with auto-sklearn Maybe a bit more complex, but very powerful was mljar-supervised

In my case, I had experience in DS and software engineering. It gives me ability to start a company that works on Data Science tools.

MLJAR AutoML is a Python package for Automated Machine Learning on tabular data with feature engineering, explanations, and automatic documentation.

I recommend trying my AutoML package. You can easily check many different algorithms. Waht is more, the baseline algorithms are checked (major class predictor for classification and mean predictor for regression). The advance of AutoML is that it is really quick. You dont need to write preprocessing code, just call fit method.

With my wife, we are working on visual interface for creating Python scripts in the notebook. We created desktop application MLJAR Studio. In our app, user has a list of predefined steps. Each step has a graphical interface with a form that after filling generate the Python code. The Python code is the source of the truth.

Currently we have a few steps for training Machine Learning model on tabular data. [Here you have few gifs with screenshots](https://mljar.com/docs/how-does-python-notebook-work/) how it looks like, and [example how to build ML model](https://mljar.com/docs/create-first-notebook/) on tabular data. The created notebook is compatible with Jupiter notebook.

In the near future, we are planning to add notebook scheduling and more steps (probably with some dynamic manager for steps loading). We see MLJAR Studio as an alternative to visual programming environments which are node based. Because the Python code is the source of truth, it offers a great flexibility to define new steps or to add custom Python code.

The app is desktop based (it is using electron framework). It automatically installs Python 3.9 with miniconda and required packages. The installation is local, without change to the environment path. You can see installation instructions [here](https://mljar.com/docs/install-notebook/). The application is only for Windows. If you are interested in MacOS or Linux versions, please fill the [form](https://docs.google.com/forms/d/e/1FAIpQLSeB5-hA326sBg9fg-pp...) and we will notify you when ready.

If you would like to try the app (currently Windows only), it can be downloaded from GitHub release page: https://github.com/mljar/studio/releases

If you would like to try the app (currently Windows only), it can be downloaded from GitHub release page: https://github.com/mljar/studio/releases

Instead, we started to work on desktop application that will allow to create python notebooks with no-code GUI (https://github.com/mljar/studio some screenshots on our website ).