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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

Plotting outside of Spark

If you wish to use other tools to plot the data, you can first take a sample of the Spark data and plot it using another package such as ggplot. Note that some versions of Spark may now have ggplot integrated and available for use within Spark. However this example will show another example of extracting data which can be used by other packages.

Collecting a sample of the results

We will take a 2% sample of all of the predictions and then print some of the results. Note that the Spark sample function has a different syntax from the base R sample function we used earlier. You could also specify this as SparkR::sample to make sure you are invoking the correct function:

local = collect(sample(preds,...
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