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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

Product type Paperback

Published in Jun 2017

Publisher Packt

ISBN-13 9781785886188

Length 576 pages

Edition 1st Edition

Languages

R

Tools

Splunk

Concepts

Predictive Analytics

Author (1):

Ralph Winters

View More author details

Table of Contents (13) Chapters

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

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...

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Authors (1)

Winters

Winters

Ralph Winters started his career as a database researcher for a music performing rights organization (he composed as well!), and then branched out into healthcare survey research, finally landing in the Analytics and Information technology world. He has provided his statistical and analytics expertise to many large fortune 500 companies in the financial, direct marketing, insurance, healthcare, and pharmaceutical industries. He has worked on many diverse types of predictive analytics projects involving customerretention, anti-money laundering, voice of the customer text mining analytics, and health care risk and customer choice models. He is currently data architect for a healthcare services company working in the data and advanced analytics group. He enjoys working collaboratively with a smart team of business analysts, technologists, actuaries as well as with other data scientists. Ralph considered himself a practical person. In addition to authoring Practical Predictive Analytics for Packt Publishing, he has also contributed two tutorials illustrating the use of predictive analytics in Medicine and Healthcare in Practical Predictive Analytics and Decisioning Systems for Medicine: Miner et al., Elsevier September, 2014, and also presented Practical Text Mining with SQL using Relational Databases, at the 2013 11th Annual Text and Social Analytics Summit in Cambridge, MA. Ralph resides in New Jersey with his loving wife Katherine, amazing daughters Claire and Anna, and his four-legged friends, Bubba and Phoebe, who can be unpredictable. Ralph's web site can be found at ralphwinters.com

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