Predict Next Purchase
Repository | Notebook
In this demonstration, we use a multi-table dataset of 3 million online grocery orders from Instacart to predict what a customer will buy next. We show how to generate features with automated feature engineering and build an accurate machine learning pipeline using Featuretools, which can be reused for multiple prediction problems. For more advanced users, we show how to scale that pipeline to a large dataset using Dask.

NYC Taxi
Repository | Baseline Featuretools | Custom Primitives
Over five workbooks we go into depth in several aspects of Featuretools functionality while building a model which predicts how long a New York City taxi trip will take from the pickup location. We show how to augment a basic machine learning data science pipeline quickly with Featuretools and demonstrate how to write your own custom primitives.

Olympic Games
Repository | Notebook
We show how Featuretools makes it easy to incorporate automated feature engineering into your workflow using historical Olympic Games data. This demonstration shows how Featuretools simplifies data science-related code and enables us to ask innovative questions, all while automatically generating hundreds of features, improving accuracy and avoiding classic label-leakage problems.