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by Heba El-Shimy, Scott D'Angelo | Published November 7, 2018
Artificial intelligenceData scienceDeep learningMachine learningNode.jsObject Storage
This code pattern walks you through the full cycle of a data science project. You begin by understanding the business perspective of the problem – here we used customer churn. Then, you use the available data set to gain insights and build a predictive model for use with future data. You’ll deploy the model into production and use it to score data collected from a user interface.
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:
Find the detailed instructions in the readme file. These instructions show you how to:
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