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

What is survival analysis?

Survival analysis covers a broad range of topics. Here is the list of topics that we will cover in this chapter:

  • Survival analysis
  • Time-based variables and regression
  • R survival objects
  • Customer attrition or churn
  • Survival curves
  • Cox regression
  • Plotting methods
  • Variable selection
  • Model concordance

Often, predictive analytic problems deal with various situations concerning the tracking of important events along a customer's journey, and predicting when these events will occur. Survival analysis is a form of analysis that is based upon the concept of time to event. The time to event is simply the number of units of time that have elapsed until something happens. The event can be just about anything; a car crash, a stock market crash, or a devastating phenomenon.

Survival analysis originated in the studying of patients who developed terminal diseases, such as cancer, hence the term survival. However, conceptually, it can even be applied to marketing applications in which you...