Feature-engine is a Python library with multiple transformers to engineer and select features for use in machine learning models. Feature-engine's transformers follow scikit-learn's functionality with fit() and transform() methods to first learn the transforming parameters from data and then transform the data.
In this repo, you will find a lot of examples on how to use Feature-engine's transformers on various datasets. The notebooks are sorted in the following folders and include examples for the following transformers:
- MathematicalCombination
- CombineWithReferenceFeature
- CyclicalTransformer - notebook wanted, please contribute
- EqualFrequencyDiscretiser
- EqualFrequencyDiscretiser plus WoEEncoder
- EqualWidthDiscretiser
- EqualWidthDiscretiser plus OrdinalEncoder
- DecisionTreeDiscretiser
- ArbitraryDiscreriser
- ArbitraryDiscreriser plus MeanEncoder
- OneHotEncoder
- OrdinalEncoder
- CountFrequencyEncoder
- MeanEncoder
- WoEEncoder
- PRatioEncoder
- RareLabelEncoder
- DecisionTreeEncoder
- MeanMedianImputer
- RandomSampleImputer
- EndTailImputer
- AddMissingIndicator
- CategoricalImputer
- ArbitraryNumberImputer
- DropMissingData -- notebook wanted, please contribute
- Winsorizer
- ArbitraryOutlierCapper
- OutlierTrimmer
- create new features - wine data
- regression pipeline - house prices data
- more notebooks wanted, please constribute
- LogTransformer
- LogCpTransformer
- ReciprocalTransformer
- PowerTransformer
- BoxCoxTransformer
- YeoJohnsonTransformer
- SklearnTransformerWrapper plus Scikit-learn's OneHotEncoder
- SklearnTransformerWrapper plus Scikit-learn's feature selection classes
- SklearnTransformerWrapper plus Scikit-learn's KBinsDiscretizer
- SklearnTransformerWrapper plus Scikit-learn's Scalers
- SklearnTransformerWrapper plus Scikit-learn's SimpleImputer
- notebooks wanted, please contribute
We welcome notebooks from users of the package. If you want to create one of the missing notebooks, or want to add a notebook of your own, provided that the data set is free to share, make a pull request with the code.
How to contribute:
- Fork the repo
- Clone your fork into your local computer:
git clone https://github.com/<YOURUSERNAME>/feature-engine-examples.git - cd into the repo
cd feature-engine-examples - If you haven't done so yet, install feature-engine
pip install feature_engine - Create a feature branch with a meaningful name for your notebook:
git checkout -b mynotebookbranch - Develop your notebook
- Add the changes to your copy of the fork:
git add .,git commit -m "a meaningful commit message",git pull origin mynotebookbranch:mynotebookbranch - Go to your fork on Github and make a PR to this repo
- Done
Thank you!!