Metadata-Version: 2.1
Name: cABCanalysis
Version: 0.1.0
Summary: Nested computed ABC analysis (cABC): A method to reduce feature sets to their most relevant items. Implementation in Python.
Home-page: https://github.com/JornLotsch/ABCanalysis
Author: Jorn Lotsch
Author-email: j.loetsch@em.uni-frankfurt.de
License: GNU General Public License v3 (GPLv3)
Keywords: cABC analysis,ABC analysis,item categorization,feature selection,information reduction
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Requires-Python: >=3.5
Description-Content-Type: text/markdown

# ABCanalysis
Nested computed ABC analysis (cABC): A method to reduce feature sets to their most relevant items. Implementation in Python.

## Author
Jorn Lotsch  
Data Science | Clinical Pharmacology  
Goethe - University
Frankfurt am Main  
Germany

## Abstract
The **cABCanalysis** package is a Python package that provides a method for categorizing items or inventories, with the aim to reduce feature sets to the most important elements.

## Requirements
* numpy >= 1.19.2
* pandas >= 1.1.5
* seaborn >= 0.11.2
* scipy >= 1.7.3

## Reference
Publication in progress.




