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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

Spark SQL

Another way to explore data in Spark is by using Spark SQL. This allows analysts who may not be well-versed in language-specific APIs, such as SparkR, PySpark (for Python), and Scala, to explore Spark data.

I will describe two different ways of accessing Spark data via SQL:

  • Issuing SQL commands through the R interface:

This has the advantage of returning the results as an R dataframe, where it can be further manipulated

  • Issuing SQL queries via databricks SQL magic: directive

This method allows analysts to issue direct SQL commands, without regard to any specific language environment

Before processing an object as SQL, the object needs to be registered as a SQL table or view. Once it is registered, it can be accessed through the SQL interface of any language API.

Once registered, you can use the show tables SparkR command to get a list of registered tables for your...

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