feature-engine/feature_engine

Health v2 · maintenance only

Adoption not part of the score

2272 stars · 368 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Feature-engine is a Python library for feature engineering and feature selection with transformers that follow scikit-learn's fit/transform API. It provides a wide range of transformers for missing data imputation, encoding, discretisation, outlier handling, and variable selection, compatible with sklearn pipelines.

Use cases

• engineer features for machine learning models in python
• select the most relevant features before training a model
• impute missing values in a dataframe within an sklearn pipeline
• encode categorical variables for scikit-learn estimators
• detect and cap outliers in my dataset
• wrap feature engineering steps in a cross-validation-safe pipeline

When to choose

• you need sklearn-compatible feature engineering transformers that fit into Pipelines
• you want to avoid data leakage by learning parameters only from the training set
• you need a broad toolkit for imputation, encoding, discretisation, outliers, and selection in one library

When to avoid

• you need deep learning or GPU-accelerated feature processing
• you work outside the Python/pandas ecosystem
• you only need one-off ad-hoc transformations without pipeline integration

Facets

library · maturity stable

machine-learning data-science machine-learning data-science python feature-engineering feature-selection scikit-learn-compatible transformers data-preprocessing

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markdown · JSON · MCP: product_card(name="feature-engine/feature_engine")