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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

Health insurance coverage dataset


We will start by reading in a dataset which contains health care enrollment data over a period for several categories. This data has been sourced from Table HIB-2, health insurance coverage status and type of coverage all persons by age and sex: 1999 to 2012, and it is available from the CMS website at http://www.census.gov/data/tables/time-series/demo/health-insurance/historical-series/hib.html.

This table shows the number of people covered by government and private insurance, as well as the number of people not covered.

This table has several embedded time series across all the 14 years represented. 14 data points would not be considered an extremely long time series; however, we will use this data to demonstrate how we can comb through many time series at once. Since it is small, it will be easy enough to verify the results via visual inspection and printing subsets of the data. As you become familiar with the methodology, it will enable you to expand to...

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