ML: Add AutoML classification example using PyCaret

topic/machine-learning/classification-automl/.gitignore

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+.env
+.env.local
+*.log
+catboost_info/
+mlruns/
+model/

topic/machine-learning/classification-automl/README.md

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+# AutoML with Pycaret and CrateDB
+
+This folder provides examples, tutorials and runnable code on how to use CrateDB
+with Pycaret to automatically create high-performing machine learning models.
+
+The tutorials and examples focus on being easy to understand and use. They
+should be a good starting point for your own projects.
+
+## About Pycaret
+
+[Pycaret] is a Python library that makes it easy to create and train machine
+learning models in python. The outstanding feature of Pycaret is its AutoML
+capabilities.
+
+Pycaret is a high-level interface on top of popular machine learning
+frameworks. Among them are scikit-learn, xgboost, ray, lightgbm and many more.
+
+Pycaret provides a simple low-code interface to utilize these libraries without
+needing to know the details of the underlying model architectures and
+parameters.
+
+The general concept of Pycaret - and for the matter of fact for AutoML in
+general - is rather simple: One takes the raw data, splits it into a training
+and a test set and then trains a number of different models on the training
+set. The models are then evaluated on the test set and the best performing
+model is selected. This process gets repeated for tuning the hyperparameters
+of the best models. Again, this process is highly empirical. The parameters are
+changed, the model is retrained and evaluated again. This process is repeated
+until the best performing parameters are found.
+
+Modern algorithms for executing all these experiments are - among other -
+GridSearch, RandomSearch and BayesianSearch. For a quick introduction into
+these methods, see this
+[Introduction to hyperparameter tuning][Introduction to hyperparameter tuning]
+
+In the past, all these try-and-error experiments had to be done manually -
+which is a tedious and time-consuming task. Pycaret automates this process
+and provides a simple interface to execute all these experiments in a
+straightforward way. This notebook shows how.
+
+## What's inside
+
+[![Made with Jupyter](https://img.shields.io/badge/Made%20with-Jupyter-orange?logo=Jupyter)](https://jupyter.org/try) [![Made with Markdown](https://img.shields.io/badge/Made%20with-Markdown-1f425f.svg?logo=Markdown)](https://commonmark.org)
+
+This folder provides guidelines and runnable code to get started with [Pycaret]
+and [CrateDB].
+
+- [readme.md](readme.md): The file you are currently reading contains a
+  walkthrough about how to get started with the Pycaret framework and CrateDB,
+  and guides you to corresponding example programs on how to train different
+  models.
+
+- [requirements.txt](requirements.txt): Pulls the required dependencies to
+  run the example programs.
+
+- `automl_classification_with_pycaret.ipynb` [![Open on GitHub](https://img.shields.io/badge/Open%20on-GitHub-lightgray?logo=GitHub)](automl_classification_with_pycaret.ipynb) [![Open in Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/crate/cratedb-examples/blob/feature%2Fpycaret_example/topic/machine-learning/classification-automl/automl_classification_with_pycaret.ipynb)
+
+  This notebook explores the Pycaret framework and shows how to use it to
+  train different classification models - using a user churn dataset as an
+  example. The notebook demonstrates the usage of Pycaret to automatically train
+  and benchmark a multitude of models and at the end select the best performing
+  model. The notebook also shows how to use CrateDB as storage for both the raw
+  data and the expirement tracking and model registry data.
+
+- Accompanied to the Jupyter Notebook files, there are also basic variants of
+  the above examples,
+  [automl_classification_with_pycaret.py](automl_classification_with_pycaret.py).
+
+[Pycaret]: https://github.com/pycaret/pycaret
+[CrateDB]: https://github.com/crate/crate
+[Introduction to hyperparameter tuning]: https://medium.com/analytics-vidhya/comparison-of-hyperparameter-tuning-algorithms-grid-search-random-search-bayesian-optimization-5326aaef1bd1