PythonStdioGames VS mljar-supervised

Side note: My latest book, The Big Book of Small Python Projects, is out. It's a collection of short but complete games, animations, simulations, and other programming projects. They're more than code snippets, but also simple enough for beginners/intermediates to read the source code of to figure out how they work. The book is released under a Creative Commons license, so it's free to read online. (I'll be uploading it this week when I get the time.) The projects come from this git repo.

I prefer doing several small projects. I have a list of games/puzzles/art/simulation programs in Python here that are under 300 lines: https://github.com/asweigart/pythonstdiogames

So far I found only https://github.com/asweigart/PythonStdioGames/tree/main/src/gamesbyexample

You can also look at the code in this GitHub repo.

Side note: My latest book, The Big Book of Small Python Projects, is out. It's a collection of short but complete games, animations, simulations, and other programming projects. They're more than code snippets, but also simple enough for beginners/intermediates to read the source code of to figure out how they work. The book is released under a Creative Commons license, so it's free to read online. (I'll be uploading it this week when I get the time.) The projects come from this git repo.

You should do whatever the person in the video does. You can make a Tic Tac Toe game with just text. (I have such a program here in Python).

The src/gamesbyexample folder in this git repo has examples of several simple games made in Python. These are short programs designed for beginners; projects like Tic Tac Toe, Guess the Number, and other simple games. Go ahead and play around with them, and then try to recreate them from scratch yourself: https://github.com/asweigart/pythonstdiogames

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.