Preface

As data scientists and machine learning professionals, our jobs are to build models for detecting frauds, predicting customer churns, or turning data into insights in a broad sense; for this, we sometimes need to process huge amounts of data and handle complicated computations. Therefore, we are always excited to see new computing tools, such as Spark, and spend a lot of time learning about them. To learn about these new tools, a lot of learning materials are available, but they are from a more computing perspective, and often written by computer scientists.

We, the data scientists and machine learning professionals, as users of Spark, are more concerned about how the new systems can help us build models with more predictive accuracy and how these systems can make data processing and coding easy for us. This is the main reason why this book has been developed and why this book has been written by a data scientist.

At the same time, we, as data scientists and machine learning professionals, have already developed our frameworks and processes as well as used some good model building tools, such as R and SPSS. We understand that some of the new tools, such as MLlib of Spark, may replace certain old tools, but not all of them. Therefore, using Spark together with our existing tools is essential to us as users of Spark and becomes one of the main focuses for this book, which is also one of the critical elements, making this book different from other Spark books.

Overall, this is a Spark book written by a data scientist for data scientists and machine learning professionals to make machine learning easy for us with Spark.

What this book covers

Chapter 1, Spark for Machine Learning, introduces Apache Spark from a machine learning perspective. We will discuss Spark dataframes and R, Spark pipelines, RM4Es data science framework, as well as the Spark notebook and implementation models.

Chapter 2, Data Preparation for Spark ML, focuses on data preparation for machine learning on Apache Spark with tools such as Spark SQL. We will discuss data cleaning, identity matching, data merging, and feature development.

Chapter 3, A Holistic View on Spark, clearly explains the RM4E machine learning framework and processes with a real-life example and also demonstrates the benefits of obtaining holistic views for businesses easily with Spark.

Chapter 4, Fraud Detection on Spark, discusses how Spark makes machine learning for fraud detection easy and fast. At the same time, we will illustrate a step-by-step process of obtaining fraud insights from big data.

Chapter 5, Risk Scoring on Spark, reviews machine learning methods and processes for a risk scoring project and implements them using R notebooks on Apache Spark in a special DataScientistWorkbench environment. Our focus for this chapter is the notebook.

Chapter 6, Churn Prediction on Spark, further illustrates our special step-by-step machine learning process on Spark with a focus on using MLlib to develop customer churn predictions to improve customer retention.

Chapter 7, Recommendations on Spark, describes how to develop recommendations with big data on Spark by utilizing SPSS on the Spark system.

Chapter 8, Learning Analytics on Spark, extends our application to serve learning organizations like universities and training institutions, for which we will apply machine learning to improve learning analytics for a real case of predicting student attrition.

Chapter 9, City Analytics on Spark, helps the readers to gain a better understanding about how Apache Spark could be utilized not only for commercial use, but also for public use as to serve cities with a real use case of predicting service requests on Spark.

Chapter 10, Learning Telco Data on Spark, further extends what was studied in the previous chapters and allows readers to combine what was learned for a dynamic machine learning with a huge amount of Telco Data on Spark.

Chapter 11, Modeling Open Data on Spark, presents dynamic machine learning with open data on Spark from which users can take a data-driven approach and utilize all the technologies available for optimal results. This chapter is an extension of Chapter 9, City Analytics on Spark, and Chapter 10, Learning Telco Data on Spark, as well as a good review of all the previous chapters with a real-life project.

What you need for this book

Throughout this book, we assume that you have some basic experience of programming, either in Scala or Python; some basic experience with modeling tools, such as R or SPSS; and some basic knowledge of machine learning and data science.

Who this book is for

This book is written for analysts, data scientists, researchers, and machine learning professionals who need to process Big Data but who are not necessarily familiar with Spark.

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "In R, the forecast package has an accuracy function that can be used to calculate forecasting accuracy."

A block of code is set as follows:

df1 = sqlContext.read \
. format("json") \ data format is json
. option("samplingRatio", "0.01") \ set sampling ratio as 1%
. load("/home/alex/data1,json") \ specify data name and location

Any command-line input or output is written as follows:

sqlContext <- sparkRSQL.init(sc)

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Users can click on Create new note, which is the first line under Notebook on the first left-hand side column."

Note

Warnings or important notes appear in a box like this.

Tip

Tips and tricks appear like this.

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Questions

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