#16 · Primary category： Classical Machine Learning Frameworks
feature_engine
Feature engineering and selection open-source Python library compatible with sklearn.
Project last updated：08/26/26
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2.3K
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370
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68
License
BSD-3-Clause
Why we included this project
Most preprocessing code ends up as one-off pandas snippets that are hard to reproduce, and this library packages the common operations as scikit-learn-compatible transformers instead. Data scientists working with tabular data get fit-and-transform objects for missing values, categorical encodings, outlier capping, discretization, and target transformations, all of which behave like the rest of an sklearn pipeline. The selection side includes recursive elimination and variance thresholds for pruning predictors before training. It is a practical base for teams that want preprocessing steps tracked and versioned alongside their models rather than scattered across separate scripts.
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