Build a customer churn predictor using Watson Studio - Chris Tyler of IBM

Customer churn, when a customer ends their relationship with a business, is one of the most basic factors in determining the revenue of a business. You need to know which of your customers are loyal and which are at risk of churning, and you need to know the factors that affect these decisions from a customer perspective. This code pattern explains how to build a machine learning model and use it to predict whether a customer is at risk of churning. This is a full data science project, and you can use your model findings for prescriptive analysis later or for targeted marketing.

When you have completed this code pattern, you’ll understand how to:

Use Jupyter Notebooks to load, visualize, and analyze data
Run Notebooks in IBM Watson Studio
Load data from IBM Cloud Object Storage
Build, test, and compare different machine learning models using scikit-learn
Deploy a selected machine learning model to production using Watson Studio
Create a front-end application to interface with the client and start consuming your deployed model