Data Science for Marketing Analytics

7. Supervised Learning: Predicting Customer Churn

Overview

In this chapter, you will perform classification tasks using logistic regression and implement the most widely used data science pipeline – Obtain, Scrub, Explore, Model, and iNterpret (OSEMN). You will interpret the relationship between the target and explanatory variables by performing data exploration. This will in turn help in selecting features for building predictive models. You will use these concepts to train your churn model. You will also perform logistic regression as a baseline model to predict customer churn.

Introduction

The success of a company is highly dependent on its ability to attract new customers while holding on to the existing ones. Churn refers to the situation ...