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] 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
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
Built Distribution
Hashes for dirty_cat-0.2.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 24a1110457c004d9d670aa5662edbed720ef731199dc389c7fec24bcda1409f6 |
|
MD5 | 9bfd2a83a693a56d4df6a8bb7d8c8721 |
|
BLAKE2b-256 | 6c9ed8e44c8d1ee31ac026f675ca47d2cb21da958387a618eb658263fee4814d |