Predict Customer Churn

This project shows an application of Featuretools to a common use case for subscription businesses: increasing active subscribers by decreasing the number of churned customers. In a set of Jupyter Notebooks and accompanying articles, we cover the concepts and implementation of the prediction engineering, feature engineering, modeling approach to solving problems with machine learning. The end outcome is a relevant solution to the customer churn problem as well as a general-purpose framework you can apply to problems across industries. Additionally, this project demonstrates using Spark with PySpark to scale feature engineering to large datasets.


Get the latest tutorials, releases, and demos!