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Practical Predictive Analytics

You're reading from   Practical Predictive Analytics Analyse current and historical data to predict future trends using R, Spark, and more

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781785886188
Length 576 pages
Edition 1st Edition
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Author (1):
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Ralph Winters Ralph Winters
Author Profile Icon Ralph Winters
Ralph Winters
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Predictive Analytics FREE CHAPTER 2. The Modeling Process 3. Inputting and Exploring Data 4. Introduction to Regression Algorithms 5. Introduction to Decision Trees, Clustering, and SVM 6. Using Survival Analysis to Predict and Analyze Customer Churn 7. Using Market Basket Analysis as a Recommender Engine 8. Exploring Health Care Enrollment Data as a Time Series 9. Introduction to Spark Using R 10. Exploring Large Datasets Using Spark 11. Spark Machine Learning - Regression and Cluster Models 12. 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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