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

Spark for learning from open data


In this section, we will describe our real-life use case of learning from open data, and then describe how to prepare Apache Spark computing for our real-life projects.

The use case

As discussed in Chapter 9, City Analytics on Spark, in the United States and worldwide, more and more governments at various levels have made their collected data openly available to the public. As a result of expanding analytics of open data, many governmental and social organizations have used these open datasets to improve service to citizens, with a lot of good results recorded, such as in https://www.socrata.com/video/added-value-open-datas-internal-use-case/. Using data analytics for cities has a huge impact as more than half of us live in urban centers now, and this urban residence percentage is higher and higher every year.

Especially, using big data to measure communities is also favored by researchers and practitioners, as we can see at http://files.meetup.com/11744342...