feature_engine and featuretools
These are complementary tools: featuretools automates the creation of new features from relational data, while feature_engine provides a comprehensive toolkit for selecting, engineering, and transforming features—they're typically used together in a feature engineering pipeline.
About feature_engine
feature-engine/feature_engine
Feature engineering and selection open-source Python library compatible with sklearn.
This tool helps data scientists and machine learning engineers prepare their raw datasets for building better predictive models. It takes your raw data, with its missing values, messy categories, and complex numeric features, and systematically transforms it. The output is a cleaned, structured dataset optimized for machine learning algorithms, making your models more accurate and robust.
About featuretools
alteryx/featuretools
An open source python library for automated feature engineering
This tool helps data professionals prepare their datasets for machine learning by automatically creating meaningful features from raw, structured data. You feed it multiple related tables, like customer details, transactions, and sessions, and it outputs a single, wide table of new features that can directly be used to train predictive models. It's designed for data scientists, analysts, and machine learning engineers who need to quickly generate rich features without extensive manual coding.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work