Introduction

Churn prediction is one of the most common use cases of machine learning. Churn can be anything—employee churn from a company, customer churn from a mobile subscription, and so on. Predicting customer churn is important for an organization because acquiring new customers is easy, but retaining them is more difficult. Similarly, high employee churn can also affect a company, since it spends a huge sum of money on grooming talent. Also, organizations that have high retention rates benefit from consistent growth, which can also lead to high referrals from existing customers.

Most of the use cases for churn prediction involve supervised classification tasks. We saw what supervised learning is in the previous chapters, and covered regression in detail. In this chapter, we will first begin by learning about classification problems, then we will implement logistic regression and understand the intuition behind the algorithm. Next, we will see how to organize data to build a churn model...