The Featuretools Python library supports automated feature engineering and is published as open source.
Open source
24 alternativesFeature Engine
Feature Engine provides Python tools for feature engineering and selection that are compatible with scikit-learn.
About Feature Engine
Feature-engine is an open-source Python library with transformers to engineer and select features for machine learning models. Its transformers use scikit-learn style fit() and transform() methods to learn parameters from data and then transform it.
It includes transformers for missing data imputation, categorical encoding, discretisation, outlier handling, variable transformation and selection, plus datetime, text and time series features.
Key features
- Missing data imputation with mean, median, arbitrary and other methods
- Categorical encoding including one-hot, ordinal, count and target-based encoders
- Discretisation via equal frequency, equal width and decision tree methods
- Outlier handling through capping, winsorising and trimming transformers
- Datetime, text and time series feature creation including lag features
- Feature selection by dropping constant, duplicate and correlated variables
Best for: Python users building machine learning models who need scikit-learn-compatible feature engineering transformers.
Good for
- Compatible with scikit-learn fit() and transform() workflow
- Covers imputation, encoding, transformation, creation and selection transformers
- Installable via pip and conda
Feature Engine at a glance
- Category
- AI & Machine Learning › AI Data & Analytics
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Feature Engine FAQ
How do I install Feature-engine?
From PyPI with pip install feature_engine, from Anaconda with conda install -c conda-forge feature_engine, or by cloning the repository.
How do Feature-engine transformers work?
They follow Scikit-learn functionality with fit() and transform() methods to learn transforming parameters from the data and then transform it.
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