Metadata-Version: 2.1
Name: scikit-learn-whiskers
Version: 0.2.0
Summary: Tools aimed to facilitate some datascience and machine learning tasks.
Author-email: Martin Carlos Araya <martinaraya@gmail.com>
License: MIT License
        
        Copyright (c) 2020 Martín Carlos Araya
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
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Project-URL: Homepage, https://github.com/ayaranitram/scikit-learn-whiskers
Project-URL: Bug Tracker, https://github.com/ayaranitram/unyts/scikit-learn-whiskers
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

# `scikit-learn-whiskers`
A collection (only one at this time) of tools aimed to help with some tasks of machine learning and datascience studies.  
These tools are intended to be compatible with scikit-learn utilities, and work properly inside a Pipeline.

## `WhiskerOutliers`
A class to mark as **outliers** the values that can visually be identified as outliers from a typical _box and whiskers_ plot.  
This class implements `.fit`, `transform` and `fit_transform`, as well as `get_params` and `set_params` methods as any standard scikit-learn implementation. 
  
## `StandardOutliers`
A class to mark as **outliers** the values outside the range _`threshold` * standard deviation_ around the _mean_.  
This class implements `.fit`, `transform` and `fit_transform`, as well as `get_params` and `set_params` methods as any standard scikit-learn implementation.

## Requisites:  
- `NumPy`
- `Pandas`
- `Scikit-Learn`

## Installation
To install it: `pip git+https://github.com/ayaranitram/scikit-learn-whiskers`
