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 (>= 3.8)
NumPy (>= 1.17.3)
SciPy (>= 1.4.0)
scikit-learn (>= 0.23.0)
pandas (>= 1.2.0)
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-0.3.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b47227f538a36e9b0107c1ba985b4ffdac7e29fc7aee47b1af0859389ad88264 |
|
MD5 | 2c0f65fb7d071a31873f3ac309608530 |
|
BLAKE2b-256 | 2d638b5f63d1296c04c6c79090a7747edd2178a15ab5590f78d7bbfe0e967726 |