An open source python framework for automated feature engineering
The upcoming release of Featuretools 1.0.0 contains several breaking changes. For details on migrating to the new version, refer to Transitioning to Featuretools Version 1.0

Featuretools automatically creates features from

temporal and relational datasets

Deep Feature Synthesis

Featuretools uses DFS for automated feature engineering. You can combine your raw data with what you know about your data to build meaningful features for machine learning and predictive modeling.

Precise Handling of Time

Featuretools provides APIs to ensure only valid data is used for calculations, keeping your feature vectors safe from common label leakage problems. You can specify prediction times row-by-row.

Reusable Feature Primitives

Featuretools comes with a library of low-level functions which can be stacked to create features. You can build and share your own custom primitives to be reused on any dataset.

Why use Featuretools?

Improve your existing workflow

Featuretools works alongside tools you already use to build machine learning pipelines. You can load in pandas dataframes and automatically create meaningful features in a fraction of the time it would take to do manually.

Accessible Python API

With several demo applications, extensive documentation and community support on Stack Overflow, getting started with Featuretools is easier than ever.  Take a look at the Demos page to get started.

Alteryx Open Source Tools

Automated Machine Learning

EvalML is an AutoML library that builds, optimizes, and evaluates machine learning pipelines.

Prediction Engineering

Compose is a machine learning tool for automated prediction engineering. It allows you to structure prediction problems and generate labels for supervised learning.

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