Metadata-Version: 2.1
Name: feature_engine
Version: 0.3.1
Summary: Feature engineering package that follows sklearn functionality
Home-page: http://github.com/solegalli/feature_engine
Author: Soledad Galli
Author-email: solegalli1@gmail.com
License: BSD 3 clause
Description: # Feature Engine
        
        Feature-engine is a Python library that contains several transformers to engineer features for use in machine learning models. Feature-engine's transformers follow Scikit-learn like functionality with fit() and transform() methods to first learn the transforming paramenters from data and then transform the data.
        Current Feature-engine's transformers include functionality for:
        
        * Missing data imputation
        * Categorical variable encoding
        * Outlier removal
        * Discretisation
        * Numerical Variable Transformation
        
        ## Important Links
        
        * Documentation: http://feature-engine.readthedocs.io
        * Home page: https://www.trainindata.com/feature-engine
        
        ### Imputing Methods
        
        * MeanMedianImputer
        * RandomSampleImputer
        * EndTailImputer
        * AddNaNBinaryImputer
        * CategoricalVariableImputer
        * FrequentCategoryImputer
        * ArbitraryNumberImputer
        
        ### Encoding Methods
        * CountFrequencyCategoricalEncoder
        * OrdinalCategoricalEncoder 
        * MeanCategoricalEncoder
        * WoERatioCategoricalEncoder
        * OneHotCategoricalEncoder
        * RareLabelCategoricalEncoder
        
        ### Outlier Handling methods
        * Winsorizer
        * ArbitraryOutlierCapper
        
        ### Discretisation methods
        * EqualFrequencyDiscretiser
        * EqualWidthDiscretiser
        * DecisionTreeDiscretiser
        
        ### Variable Transformation methods
        * LogTransformer
        * ReciprocalTransformer
        * PowerTransformer
        * BoxCoxTransformer
        * YeoJohnsonTransformer
        
        ### Installing
        
        ```
        pip install feature_engine
        ```
        or
        
        ```
        git clone https://github.com/solegalli/feature_engine.git
        ```
        
        ### Usage
        
        ```
        from feature_engine.categorical_encoders import RareLabelEncoder
        
        rare_encoder = RareLabelEncoder(tol = 0.05, n_categories=5)
        rare_encoder.fit(data, variables = ['Cabin', 'Age'])
        data_encoded = rare_encoder.transform(data)
        ```
        
        See more usage examples in the jupyter notebooks in the example folder of this repository, or in the documentation: http://feature-engine.readthedocs.io
        
        ### License
        
        BSD 3-Clause
        
        ### Authors
        
        * **Soledad Galli** - *Initial work* - [Feature Engineering Online Course](https://www.udemy.com/feature-engineering-for-machine-learning/?couponCode=FEATENGREPO).
        
        
        ### References
        
        Many of the engineering and encoding functionality is inspired by this [series of articles from the 2009 KDD competition](http://www.mtome.com/Publications/CiML/CiML-v3-book.pdf).
        
        To learn more about the rationale, functionality, pros and cos of each imputer, encoder and transformer, refer to the [Feature Engineering Online Course](https://www.udemy.com/feature-engineering-for-machine-learning/?couponCode=FEATENGREPO)
        
        For a summary of the methods check this [presentation](https://speakerdeck.com/solegalli/engineering-and-selecting-features-for-machine-learning) and this [article](https://www.trainindata.com/post/feature-engineering-comprehensive-overview)
        
        To stay alert of latest releases, sign up at [trainindata](https://www.trainindata.com)
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
