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Practical Predictive Analytics

You're reading from  Practical Predictive Analytics

Product type Book
Published in Jun 2017
Publisher Packt
ISBN-13 9781785886188
Pages 576 pages
Edition 1st Edition
Languages
Author (1):
Ralph Winters Ralph Winters
Profile icon Ralph Winters

Table of Contents (19) Chapters

Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Getting Started with Predictive Analytics The Modeling Process Inputting and Exploring Data Introduction to Regression Algorithms Introduction to Decision Trees, Clustering, and SVM Using Survival Analysis to Predict and Analyze Customer Churn Using Market Basket Analysis as a Recommender Engine Exploring Health Care Enrollment Data as a Time Series Introduction to Spark Using R Exploring Large Datasets Using Spark Spark Machine Learning - Regression and Cluster Models Spark Models – Rule-Based Learning

Setting the stage by creating survival objects


Coding survival analysis in R usually starts with creating what is known as a survival object using the Surv() function. A survival object contains more information than a regular dataframe. The purpose of the survival object is to keep track of the time and the event status (0 or 1) for each observation. It is also to designate what the response (dependent) variable is.

At a minimum, you need to supply a single time variable and an event when defining a survival object. In our case, we will use the tenure time (Xtenure2) as the time variable, and a formula that designates the defining event. In our case, this will be Churn == 1, since that means that the customer churned in that month:

install.packages("survival")
library(survival)
ChurnStudy$SurvObj <- with(ChurnStudy, Surv(Xtenure2, Churn == 1))

As I mentioned in earlier chapters, I always like to issue a str() command after I create a new dataframe, just to make sure the results are as expected...

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