Feature engineering package with Scikit-learn's fit transform functionality
Feature-engine is a Python library with multiple transformers to engineer features for use in machine learning models. Feature-engine's transformers follow Scikit-learn functionality with fit() and transform() methods to first learn the transforming paramenters from data and then transform the data.
Feature-engine features in the following resources:
- Documentation: http://feature-engine.readthedocs.io
- Home page: https://www.trainindata.com/feature-engine
Current Feature-engine's transformers include functionality for:
- Missing data imputation
- Categorical variable encoding
- Outlier removal
- Numerical Variable Transformation
Outlier Handling methods
Variable Transformation methods
pip install feature_engine
git clone https://github.com/solegalli/feature_engine.git
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
Local Setup Steps
- Clone the repo and cd into it
pip install tox
toxif the tests pass, your local setup is complete
Opening Pull Requests
PR's are welcome! Please make sure the CI tests pass on your branch.
- Soledad Galli - Initial work - Feature Engineering for Machine Learning, Online Course.
Many of the engineering and encoding functionality is inspired by this series of articles from the 2009 KDD competition.
To learn more about the rationale, functionality, pros and cos of each imputer, encoder and transformer, refer to the Feature Engineering for Machine Learning, Online Course
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