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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

Sending output to an external file

One benefit of assigning plots to a plot object is that you can later send the plots to an external file, such as a PDF, view it externally, and even view it in your browser directly from R. For example, for the Lattice graphs example, you can use trellis.device and specify the output parameters, and then print the object. As we illustrated in an earlier chapter, you can use browseURL to open the PDF in your browser:

# send to pdf
setwd("C:/PracticalPredictiveAnalytics/Outputs")
trellis.device(pdf, file = "x3.pdf")
print(p)
dev.off()
#NOT run
#browseURL('file://c://PracticalPredictiveAnalytics//Outputs//x3.pdf')
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