Book Image

Apache Spark Machine Learning Blueprints

By : Alex Liu
Book Image

Apache Spark Machine Learning Blueprints

By: Alex Liu

Overview of this book

There's a reason why Apache Spark has become one of the most popular tools in Machine Learning – its ability to handle huge datasets at an impressive speed means you can be much more responsive to the data at your disposal. This book shows you Spark at its very best, demonstrating how to connect it with R and unlock maximum value not only from the tool but also from your data. Packed with a range of project "blueprints" that demonstrate some of the most interesting challenges that Spark can help you tackle, you'll find out how to use Spark notebooks and access, clean, and join different datasets before putting your knowledge into practice with some real-world projects, in which you will see how Spark Machine Learning can help you with everything from fraud detection to analyzing customer attrition. You'll also find out how to build a recommendation engine using Spark's parallel computing powers.
Table of Contents (18 chapters)
Apache Spark Machine Learning Blueprints
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
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...