In this code pattern, we will create and deploy a customer churn prediction model using IBM Cloud Private for Data. First, we will load customer demographics and trading activity data into Db2 Warehouse. Next, we'll use a Jupyter notebook to visualize the data, build hypotheses for prediction, and then build, test, and save a prediction model. Finally, we will enable a web service and use the model from an app. The use case describes a stock trader company that can use churn prediction to target offers for at-risk customers. Once deployed, the model can be used for inference from an application using the REST API. A simple app is provided to demonstrate using the model from a Python app.