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 service forecasting


In this section, we will describe a real use case of predicting service requests in detail and then describe how to prepare Apache Spark computing for this real-life project.

The use case

In the United States, and worldwide, more and more cities have made their collected data open to the public. As a result, city governments and many other organizations have performed machine learning on these open datasets with good insight into improving decision making and a lot of positive impact, for example, in New York and Chicago. Using large amount of open data is becoming a trend now. For example, using big data to measure cities is becoming a research trend, as we can note from http://files.meetup.com/11744342/CITY_RANKING_Oct7.pdf.

Using data analytics for cities has a wide impact as more than half of us live in urban centers now, and the percentage is still increasing. Therefore, what you will learn in this chapter will enable data scientists to create a huge positive...