Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Practical Predictive Analytics

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

Arrow left icon
Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781785886188
Length 576 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Ralph Winters Ralph Winters
Author Profile Icon Ralph Winters
Ralph Winters
Arrow right icon
View More author details
Toc

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

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...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image