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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

SparkR

Spark itself is written in a programming language called Scala and runs in a Java environment. However, you are not restricted to using Scala. Spark has several interfaces which are exposed through an API, which allows Spark programs to be written in these other languages:

  • R
  • Scala
  • Java
  • Python
  • Clojure

We will be demonstrating some of the examples in this chapter using SparkR. SparkR is an R package that provides a frontend to use Apache Spark from R. This allows SparkR to allow data scientists to interactively run jobs from R on a cluster. One big advantage of using SparkR, for the traditional R programmer, is that it uses some of the techniques that they already know such as the concept of dataframes is also available within SparkR.

Dataframes

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