Included in this module are 5 libraries that will help you during your data science adventures and help you save some of that valuable time you would rather spend on modelling than on data cleaning.
Project description
iembdfa
Included in this module are 5 libraries that will help you during your data science adventures and help you save some of that valuable time you would rather spend on modelling rather than on data cleaning.
DataCleaning - A.1) Automated Data Cleaning; identify invalid values and/or rows and automatically solve the problem- NAN, missing, outliers, unreliable values, out of the range, automated data input.
AutoInterpolation - A.4.3) Automated Interpolation transformation.
GeneticFeature - A.7) Characteristics/Feature selection - Stepwise and Genetic Algorithm
DateCatVar - H.2) Human assisted Data preprocessing and transformation for modelling - Text processing and Dates processing into variables that can be used in modelling.
RatioVar - H.4) Human assisted variables and ratios creation. Create a list of possible actions that could be taken and create an user interface for a human to decide what to do.
Free software: MIT license
Documentation: https://iembdfa.readthedocs.io.
Features
TODO
Credits
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.
History
0.9 (2016-06-18)
First release on PyPI.
Project details
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