Book Image

Learning Apache Spark 2

Book Image

Learning Apache Spark 2

Overview of this book

Apache Spark has seen an unprecedented growth in terms of its adoption over the last few years, mainly because of its speed, diversity and real-time data processing capabilities. It has quickly become the preferred choice of tool for many Big Data professionals looking to find quick insights from large chunks of data. This book introduces you to the Apache Spark framework, and familiarizes you with all the latest features and capabilities introduced in Spark 2. Starting with a detailed introduction to Spark’s architecture and the installation procedure, this book covers everything you need to know about the Spark framework in the most practical manner. You will learn how to perform the basic ETL activities using Spark, and work with different components of Spark such as Spark SQL, as well as the Dataset and DataFrame APIs for manipulating your data. Then, you will perform machine learning using Spark MLlib, as well as perform streaming analytics and graph processing using the Spark Streaming and GraphX modules respectively. The book also gives special emphasis on deploying your Spark models, and how they can be operated in a clustered mode. During the course of the book, you will come across implementations of different real-world use-cases and examples, giving you the hands-on knowledge you need to use Apache Spark in the best possible manner.
Table of Contents (18 chapters)
Learning Apache Spark 2
Credits
About the Author
About the Reviewers
www.packtpub.com
Customer Feedback
Preface

Chapter 10. Customer Churn Prediction

This is our last chapter for this book, and we have looked at the technology topics around Spark from architecture to the details of the APIs including RDDs, DataFrames, and machine learning and GraphX frameworks. In the last chapter, we covered a recommendation engine use case where we primarily looked at the Scala API. We've primarily used Scala, Python, or R-Shell. In this chapter, we will be using the Jupyter notebook with the Pyspark interpreter to look at the Churn prediction use case.

The chapter covers:

  • Overview of customer churn
  • Importance of churn prediction
  • Understanding the dataset
  • Exploring data
  • Building a machine learning pipeline
  • Predicting Churn

This chapter will hopefully give you a good introduction to churn prediction systems, which you can use as a baseline for other prediction activities.

Let's get started.