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/
For a detailed description of the problem of encoding dirty categorical data, see Similarity encoding for learning with dirty categorical variables [1].
Installation
Dependencies
dirty_cat requires:
Python (>= 3.5)
NumPy (>= 1.8.2)
SciPy (>= 1.0.1)
scikit-learn (>= 0.19.0)
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
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
dirty_cat-0.0.3.tar.gz
(80.0 kB
view hashes)
Built Distribution
Close
Hashes for dirty_cat-0.0.3-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 157e7aa4fb6dd63b644aa094099b29bc96461492bcb83d6474d8b8153943712a |
|
MD5 | 270fd736b7e5c7da8a4ff25cffb3ead6 |
|
BLAKE2b-256 | 69d05de16e3c105d571fc464b71beb60d03651862fe4c7dbfe31761681c11b65 |