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Data Science for Marketing Analytics by Pranshu Bhatnagar, Debasish Behera, Tommy Blanchard

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Chapter 7

Supervised Learning: Predicting Customer Churn

Learning Objectives

By the end of this chapter, you will be able to:

  • Perform classification tasks using logistic regression
  • Implement the most widely used data science pipeline (OSEMN)
  • Perform data exploration to understand the relationship between the target and explanatory variables.
  • Select the important features for building your churn model.
  • Perform logistic regression as a baseline model to predict customer churn.

This chapter covers classification algorithms such as logistic regression and explains how to implement the OSEMN pipeline.

Introduction

Churn prediction is one of the most common use cases of machine learning. Churn can be anything—employee churn from a company, ...

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