Prepping tables for machine learning
Project description
skrub (formerly dirty_cat) is a Python library that facilitates prepping your tables for machine learning.
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What can skrub do?
skrub provides data assembling tools (TableVectorizer, fuzzy_join…) and encoders (GapEncoder, MinHashEncoder…) for morphological similarities, for which we usually identify three common cases: similarities, typos and variations
See our examples.
What skrub cannot do
Semantic similarities are currently not supported. For example, the similarity between car and automobile is outside the reach of the methods implemented here.
This kind of problem is tackled by Natural Language Processing methods.
skrub can still help with handling typos and variations in this kind of setting.
For a detailed description of the problem of encoding dirty categorical data, see Similarity encoding for learning with dirty categorical variables [1] and Encoding high-cardinality string categorical variables [2].
Installation (WIP)
There are currently no PiPy releases. You can still install the package from the GitHub repository with:
pip install git+https://github.com/skrub-data/skrub.gitDependencies
Dependencies and minimal versions are listed in the setup file.
Contributing
The best way to support the development of skrub is to spread the word!
Also, if you already are a skrub user, we would love to hear about your use cases and challenges in the Discussions section.
To report a bug or suggest enhancements, please open an issue and/or submit a pull request.
Additional resources
References
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