Metadata-Version: 2.1
Name: imbdata
Version: 0.0.1
Summary: Creacion de la libreria para balancear datos
Project-URL: Homepage, https://github.com/unaigarciag/balanceo_library
Project-URL: Bug Tracker, https://github.com/unaigarciag/balanceo_library/issues
Author-email: ikerg <iker.gabirondo@alumni.mondragon.edu>
License: MIT License
        
        Copyright (c) 2023 ikergabirondo
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
License-File: LICENSE
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.9
Description-Content-Type: text/markdown

# Balance_library

Esta es una libreria de python que proporciona herramientas para el preprocesamiento y equilibrio de datos desequilibrados.

Las caracteristicas que tiene esta libreria son las siguientes:
- **Preprocesamiento de datos**: Limpieza de datos eliminando filas con valores faltantes (NAs).
- **Equilibrio de datos**: Ofrece métodos para equilibrar conjuntos de datos, incluyendo undersampling, oversampling y una combinación de ambas estrategias.
- **Generación de datos sintéticos**: Permite la generación de datos sintéticos para abordar problemas de oversampling utilizando técnicas como CTGANSynthesizer.
