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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

Joining data


If you need to bring together different data sources, SQL is one method for bringing data together. As mentioned, SQL syntax is common to a lot of environments, so if you learn SQL syntax in R, you have started to learn how data is processed in other environments. But do not restrict yourself to just SQL. Other options exist for joining data, such as using the merge statement. Merge is a native function that accomplishes the same objective. And some other packages handle data integration fairly well. I will also be using the dplyr package to perform some of the same tasks as could be done in SQL.

The sqldf package is a standard R package that uses standard SQL syntax to merge, or join, two tables together. For relational data, this is accomplished by associating a variable on one table (primary key) with a similar variable on another associated table. Note that I am using the term table in the context of a relational database environment. In the R environment, an SQL table is...

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