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

Chapter 4. Fraud Detection on Spark

In Chapter 1, Spark for Machine Learning, we discussed how to get the Apache Spark system ready, and in Chapter 2, Data Preparation for Spark ML, we listed detailed instructions for data preparation. Now, in chapters 4 to 6, we will move to a new stage of utilizing Apache Spark-based systems to turn data into insights for some specific projects, which is fraud detection for this chapter; risk modeling for Chapter 5, Risk Scoring on Spark; and churn prediction for Chapter 6, Churn Prediction on Spark.

Specifically, in this chapter, we will review machine learning methods and analytical processes for a fraud detection project, and also discuss how Apache Spark makes them easy and fast. At the same time, with a real-life fraud detection example, we will illustrate our step-by-step process of obtaining fraud insight from big data.

  • Spark for fraud detection

  • Methods of fraud detection

  • Feature preparation

  • Model estimation

  • Model evaluation

  • Result explanation

  • Deploying...