Feature-engine: A Feature Engineering for Machine Learning library


Feature-engine rocks!

Feature-engine is a Python library that contains several transformers to engineer features for use in machine learning models. Feature-engine preserves Scikit-learn functionality with fit() and transform() methods to learn parameters from and then transform data.

Feature-engine includes transformers for:

  • Missing value imputation
  • Categorical variable encoding
  • Outlier capping
  • Discretisation
  • Numerical variable transformation

Feature-engine allows to select which variables to engineer within each transformer.

Feature-engine’s transformers can be assembled within the Scikit-learn pipeline, therefore making it possible to save and deploy one single object (.pkl) with the entire machine learning pipeline.


Feature-engine is a Python 3 package and works well with 3.5 or later. Earlier versions have not been tested. The simplest way to install Feature-engine is from PyPI with pip, Python’s preferred package installer.

$ pip install feature-engine


Interested in contributing to Feature-engine? That is great news! Feature-engine is a welcoming and inclusive project and it would be great to have you onboard. We follow the Python Software Foundation Code of Conduct.

Regardless of your skill level you can help us. We appreciate bug reports, user testing, feature requests, bug fixes, addition of tests, product enhancements, and documentation improvements.

More details on how to contribute will come soon! Meanwhile, feel free to fork the Github repo and make pull requests, create an issue, or send feedback. More details on how to reach us in the Getting help section below.

Thank you for your contributions!

Feature-engine’s Transformers

Missing Data Imputation: Imputers

Categorical Variable Encoders: Encoders

Numerical Variable Transformation: Transformers

Variable Discretisation: Discretisers

Outlier Capping: Cappers

Getting Help

Can’t get someting to work? Here are places you can find help.

  1. The docs (you’re here!).
  2. Stack Overflow. If you ask a question, please tag it with “feature-engine”.
  3. If you are enrolled in the Feature Engineering for Machine Learning course in Udemy, post a question in a relevant section.

Find a Bug?

Check if there’s already an open issue on the topic. If needed, file an issue.