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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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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

Running the initialization code

The initialization code merely sets some options for later use and has no real output. We will just print "Hello World" to make sure that it is working:

options(digits=3) 
options(repr.plot.width = 1000, repr.plot.height = 500, repr.plot.res = 144, repr.plot.pointsize = 5)
rep_times=1000
cat("Hello World")

The output cell prints Hello World and also gives you the time it took to run, the username, time and time, as well as the cluster it ran on. Pay close attention to the length of time that each code chunk ran. It will be useful for benchmarking code at a later date.

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