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.

Predict Remaining Useful Life
Repository | Notebook | Advanced Notebook
In this example, we demonstrate rapidly building a predictive model for the Remaining Useful Life (RUL) of an engine. Using time-series data, we perform automated feature engineering on data from running engines. This example can be used as an end-to-end workflow to automatically generate features for a common time series prediction problem.

Predict Taxi Trip Duration
Repository | Baseline Featuretools | Custom Primitives
Over four 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.

Predict Appointment No-Show
Repository | Notebook
We use Featuretools to predict whether or not a patient will show up to a doctor’s appointment. In this end-to-end demonstration we show how to automatically create valid features which use label information. By providing a little bit of human knowledge about the time relationships between columns, we can use historical missed appointment information without leaking labels. 

Predict Olympic Medals
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.

Predict Correct Answer
Repository | Notebook
Using public data from CMU Datashop we predict whether or not a student will succesfully answer a question on a given attempt. We show how an Entity Set is useful for understanding the data and how it can be used to automatically generate features. We also demonstrate how automatically generated primitives can be useful beyond improving machine learning scores: they enhance our understanding of the problem itself.

 

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