:description: A list of libraries, use cases / demos, and tutorials that leverage Featuretools =============================== Featuretools External Ecosystem =============================== New projects are regularly being built on top of Featuretools, highlighting the importance of automated feature engineering. On this page, we have a list of libraries, use cases / demos, and tutorials that leverage Featuretools. If you would like to add a project, please contact us or submit a pull request on `GitHub`_. .. _`GitHub`: https://github.com/alteryx/featuretools .. note:: We are proud and excited to share the work of people using Featuretools, but we cannot endorse or provide support for the tools on this page. --------- Libraries --------- `MLBlocks`_ =========== - 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. .. _`MLBlocks`: https://github.com/HDI-Project/MLBlocks `Cardea`_ ========= - Cardea is a machine learning library built on top of the FHIR data schema. It uses a number of **automl** tools, including Featuretools. .. _`Cardea`: https://github.com/D3-AI/Cardea ----------------- 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 customer lifetime value`: https://towardsdatascience.com/automating-interpretable-feature-engineering-for-predicting-clv-87ece7da9b36 `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 NHL playoff matches`: https://towardsdatascience.com/automated-feature-engineering-for-predictive-modeling-d8c9fa4e478b .. _`Kaggle`: https://www.kaggle.com/ `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. .. _`Predict poverty of households in Costa Rica`: https://www.kaggle.com/willkoehrsen/featuretools-for-good `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. .. _`Predicting Functional Threshold Power (FTP)`: https://github.com/jrkinley/ftp_proba .. note:: For more demos written by `Feature Labs `_, see `featuretools.com/demos `_ --------- Tutorials --------- `Automated Feature Engineering in Python`_ ========================================== - This article provides a walk-through of how to use a retail dataset with DFS. .. _`Automated Feature Engineering in Python`: https://towardsdatascience.com/automated-feature-engineering-in-python-99baf11cc219 `A Hands-On Guide to Automated Feature Engineering`_ ==================================================== - A **in-depth** tutorial that works through using Featuretools to predict future product sales at "BigMart". .. _`A Hands-On Guide to Automated Feature Engineering`: https://www.analyticsvidhya.com/blog/2018/08/guide-automated-feature-engineering-featuretools-python/ `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. .. _`Introduction to Automated Feature Engineering Using DFS`: https://heartbeat.fritz.ai/introduction-to-automated-feature-engineering-using-deep-feature-synthesis-dfs-3feb69a7c00b `Automated Feature Engineering Workshop`_ ========================================= - An automated feature engineering workshop using Featuretools hosted at the 2017 Data Summer Conference. .. _`Automated Feature Engineering Workshop`: https://github.com/fred-navruzov/featuretools-workshop `Tutorial in Japanese`_ ======================= - A tutorial of Featuretools that demonstrates integrating with the feature selection library `Boruta`_ and the hyper parameter tuning library `Optuna`_. .. _`Tutorial in Japanese`: https://dev.classmethod.jp/machine-learning/yoshim-featuretools-boruta-optuna/ .. _`Optuna`: https://github.com/pfnet/optuna .. _`Boruta`: https://github.com/scikit-learn-contrib/boruta_py `Building a Churn Prediction Model using Featuretools`_ ======================================================= - A video tutorial that shows how to build a churn prediction model using Featuretools along with `Spark`_, `XGBoost`_, and `Google Cloud Platform`_. .. _`Building a Churn Prediction Model using Featuretools`: https://youtu.be/ZwwneZ6iU3Y .. _`Spark`: https://spark.apache.org/ .. _`XGBoost`: https://github.com/dmlc/xgboost .. _`Google Cloud Platform`: https://cloud.google.com/ `Automated Feature Engineering Workshop in Russian`_ ==================================================== - A video tutorial that shows how to predict if an applicant is capable of repaying a loan using Featuretools. .. _`Automated Feature Engineering Workshop in Russian`: https://youtu.be/R0-mnamKxqY