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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

Read the data in


Next, we will read in a few rows of the file (using the nrow parameters), and then run a str() function on the input to see which variables are contained within the file. There are several metrics in the file related to medicare enrollment. We will just concentrate on the total enrollment metrics, and not utilize some of the other sub-segments (such as military and private insurance) for this chapter:

x <- read.csv("x <- read.csv("hihist2bedit.csv", nrow = 10)"
 str(x) 
 > 'data.frame': 10 obs. of  13 variables:
 >  $ Year        : Factor w/ 10 levels "2003","2004 (4)",..: 10 9 8 7 6 5 
 4 3 2 1
 >  $ Year.1      : int  2012 2011 2010 2009 2008 2007 2006 2005 2004 2003
 >  $ Total.People: num  311116 308827 306553 304280 301483 ...
 >  $ Total       : num  263165 260214 256603 255295 256702 ...
 >  $ pritotal    : num  198812 197323 196147 196245 202626 ...
 >  $ priemp      : num  170877 170102 169372 170762 177543 ...
 >  $ pridirect   : num...
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