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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

Summary

In this chapter, we learned the basics of exploring Spark data, using some Spark-specific commands that allowed us to filter, group, and summarize our Spark data.

We also learned about the ability to visualize data directly in Spark, along with learning how to run R functions such as ggplot against data.

We learned about some strategies for working with Spark data, such as performing intelligent filtering and sampling.

Finally, we demonstrated that often we need to extract some Spark data back into local R if we want the flexibility to use some of our usual tools that may not be supplied natively in the Spark environment.

In the next chapter, we will delve into the various predictive models that you can use that are specific to large datasets.

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