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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

Running the code

After loading the notebook, a series of code 'chunks' will appear. This particular notebook is an R type notebook, that is, by default, code lines can be R commands, SparkR commands, or special Databricks commands (such as display).

We will see later that we can have different cells for SQL commands (notebooks starting with %sql) or even intermix Python (%python) or scala code (%scala).

to run the contents of any particular code chunk you can use the folowingkeyboard sequence:

Alternatively, use can use the run cell icon (the first triangle icon):

Now that we now how to run individual code chunks, we will begin by running the initialization code which simply tests the Databricks system to see if it is operational and also sets some options.

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