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Apache Spark Machine Learning Blueprints

You're reading from   Apache Spark Machine Learning Blueprints Develop a range of cutting-edge machine learning projects with Apache Spark using this actionable guide

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Product type Paperback
Published in May 2016
Publisher Packt
ISBN-13 9781785880391
Length 252 pages
Edition 1st Edition
Languages
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Author (1):
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Alex Liu Alex Liu
Author Profile Icon Alex Liu
Alex Liu
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Table of Contents (13) Chapters Close

Preface 1. Spark for Machine Learning FREE CHAPTER 2. Data Preparation for Spark ML 3. A Holistic View on Spark 4. Fraud Detection on Spark 5. Risk Scoring on Spark 6. Churn Prediction on Spark 7. Recommendations on Spark 8. Learning Analytics on Spark 9. City Analytics on Spark 10. Learning Telco Data on Spark 11. Modeling Open Data on Spark Index

Model estimation


In this section, we will describe the methods and procedures for utilizing R notebooks within the DataScientistWorkbench to complete our model estimation.

The DataScientistWorkbench for R notebooks

As soon as we get our data ready by using OpenRefine, we should develop an R notebook within the which applies the codes prepared in section, Methods for risk scoring and the features prepared in section, Data and feature preparation to the data.

As seen in the following screenshot, the DataScientistWorkbench allows us to create an interactive R notebook, run it, and share it as well.

R studio, a favorite with R users, is also integrated with the DataScientistWorkbench:

To start a notebook, you can click on Build Analytics, and then on Notebook, or you can directly click on the Notebook blue button as seen in the following screenshot:

Once an R notebook is developed, it can be seen under Recent Notebooks, and you can run it to obtain results as in other environments.

R notebooks implementation...

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