Metadata-Version: 2.1
Name: preprocmachine
Version: 0.0.2
Summary: A tool to automate preprocessing phase.
Home-page: https://github.com/yashm28sjsu/preprocmachine
Author: William Su, Yash Modi, Niranjan Reddy Masapeta
Author-email: modiyash3393@gmail.com
License: MIT license
Keywords: preprocmachine
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
License-File: LICENSE

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preprocmachine
==============


.. image:: https://img.shields.io/pypi/v/preprocmachine.svg
        :target: https://pypi.python.org/pypi/preprocmachine

.. image:: https://img.shields.io/travis/yashm28sjsu/preprocmachine.svg
        :target: https://travis-ci.com/yashm28sjsu/preprocmachine

.. image:: https://readthedocs.org/projects/preprocmachine/badge/?version=latest
        :target: https://preprocmachine.readthedocs.io/en/latest/?version=latest
        :alt: Documentation Status




A tool to automate preprocessing phase.


* Free software: MIT license
* Documentation: https://preprocmachine.readthedocs.io.


Features
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* Takes a pandas dataframe as input along with target attribute
* Expandable library of self-contained modules containing preprocessing techniques available to use
        * Normalization
        * Imputation
        * Feature Selection
        * Outlier Detection
        * Duplicate Detection
* Provides flexibility to include a user defined preprocessing method if required
* Algorithm is flexible enough to work with any user defined ML algorithm
* Utilizes Epsilon greedy algorithm to determine the preprocessing steps
* Once algorithm execution is complete, it will return following:
        * Processed Dataset
        * metric to evaluate the performance  with comparison to performance before preprocessing
        * Series of Preprocessing Steps involved to reach this performance


Credits
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* Inspiration was taken from a published paper and open source project Learn2Clean: https://github.com/LaureBerti/Learn2Clean

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History
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0.0.1 (2021-12-01)
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* First release on PyPI.


