Book Image

Practical Predictive Analytics

By : Ralph Winters
Book Image

Practical Predictive Analytics

By: Ralph Winters

Overview of this book

This is the go-to book for anyone interested in the steps needed to develop predictive analytics solutions with examples from the world of marketing, healthcare, and retail. We'll get started with a brief history of predictive analytics and learn about different roles and functions people play within a predictive analytics project. Then, we will learn about various ways of installing R along with their pros and cons, combined with a step-by-step installation of RStudio, and a description of the best practices for organizing your projects. On completing the installation, we will begin to acquire the skills necessary to input, clean, and prepare your data for modeling. We will learn the six specific steps needed to implement and successfully deploy a predictive model starting from asking the right questions through model development and ending with deploying your predictive model into production. We will learn why collaboration is important and how agile iterative modeling cycles can increase your chances of developing and deploying the best successful model. We will continue your journey in the cloud by extending your skill set by learning about Databricks and SparkR, which allow you to develop predictive models on vast gigabytes of data.
Table of Contents (19 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback

Examining survival curves

Kaplan Meir survival curves are usually a good place to start when examining the effect of different single factors upon the survival rate, since they are easy to construct and visualize. Later on, we will example cox regression, which can examine multiple factors.

Kaplan Meir (KM) curves are actually step functions in which the survival object, or hazard rate, is estimated at each discrete time point. This survival rate is computed by calculating the number of customers who have survived (are still active), divided by the number of customers at risk. The number of customers at risk (which is the denominator) excludes all customers who have already churned, or haven't achieved the tenure specified at any particular time point.

To illustrate, if we table ChurnStudy by the number of months active (Xtenure2), we can see that for month 1, there were 44 members whose survival rate is calculated as (1984 -19) (Number left after end of month 1 / 1984):