Machine learning with dirty categories.
Project description
dirty_cat is a Python module for machine-learning on dirty categorical variables.
Website: https://dirty-cat.github.io/
dirty_cat’s SuperVectorizer automatically turns pandas data frames into numerical arrays suitable for learning.
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
Dependencies
dirty_cat requires:
Python (>= 3.6)
NumPy (>= 1.16)
SciPy (>= 1.2)
scikit-learn (>= 0.21.0)
pandas (>= 1.1.5)
Optional dependency:
python-Levenshtein for faster edit distances (not used for the n-gram distance)
User installation
If you already have a working installation of NumPy and SciPy, the easiest way to install dirty_cat is using pip
pip install -U --user dirty_cat
Other implementations
References
Project details
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