Featuretools Ecosystem

New projects are regularly being built on top of Featuretools. These projects not only validate the importance of feature engineering, but also provide additional functionality outside the scope of the Featuretools library.

On this page, we have a list of libraries, use cases / demos, and tutorials that leverage Featuretools. It is far from an exhaustive list. If you would like to add a project, please contact us or submit a pull request on GitHub.


Featuretools for R

  • An R interface to the Python module Featuretools.

Featuretools for Spark

  • Featuretools4S is a Python library written to scale Featuretools with Spark, making it capable of generating features for billions of rows of data.


For more information on using Featuretools with Spark, see this tutorial.


  • MLBlocks is a simple framework for composing end-to-end tunable Machine Learning Pipelines by seamlessly combining tools from any python library with a simple, common and uniform interface. MLBlocks contains a primitive that uses Featuretools.


  • Cardea is a machine learning library built on top of the FHIR data schema. It uses a number of automl tools, including Featuretools.

Demos & Use Cases

Predict customer lifetime value

  • A common use case for machine learning is to predict customer lifetime value. This article walks through the importance of this prediction problem using Featuretools in the process.

Predict NHL playoff matches

  • Many users of Kaggle are eager to use Featuretools to improve their model performance. In this blog post, a Kaggle user takes a dataset of plays from National Hockey League games and creates a model to predict if a game is a playoff match.

Predict poverty of households in Costa Rica

  • Social programs have a difficult time determining the right people to give aid. Using a dataset of Costa Rican household characteristics, this Kaggle kernel predicts the poverty of households.

Predicting Functional Threshold Power (FTP)

  • This notebook and accompanying report evaluates the use of machine learning for predicting a cyclist’s FTP using data collected from previous training sessions. Featuretools is used to generate a set of independent variables that capture changes in performance over time.


For more demos written by Feature Labs, see featuretools.com/demos


Automated Feature Engineering in Python

  • This article provides a walk-through of how to use a retail dataset with DFS.

A Hands-On Guide to Automated Feature Engineering

  • A in-depth tutorial that works through using Featuretools to predict future product sales at “BigMart”.

Simple Automatic Feature Engineering

  • A walk-through that applies Featuretools to a sample dataset and creates a classifier to predict clients who make large orders.

Introduction to Automated Feature Engineering Using DFS

  • This article demonstrates using Featuretools helps automate the manual process of feature engineering on a dataset of home loans.

Automated Feature Engineering Workshop

  • An automated feature engineering workshop using Featuretools hosted at the 2017 Data Summer Conference.

Tutorial in Japanese

  • A tutorial of Featuretools that demonstrates integrating with the feature selection library Boruta and the hyper parameter tuning library Optuna.