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Apache Spark Machine Learning Blueprints

You're reading from   Apache Spark Machine Learning Blueprints Develop a range of cutting-edge machine learning projects with Apache Spark using this actionable guide

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Product type Paperback
Published in May 2016
Publisher Packt
ISBN-13 9781785880391
Length 252 pages
Edition 1st Edition
Languages
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Author (1):
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Alex Liu Alex Liu
Author Profile Icon Alex Liu
Alex Liu
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Toc

Table of Contents (13) Chapters Close

Preface 1. Spark for Machine Learning FREE CHAPTER 2. Data Preparation for Spark ML 3. A Holistic View on Spark 4. Fraud Detection on Spark 5. Risk Scoring on Spark 6. Churn Prediction on Spark 7. Recommendations on Spark 8. Learning Analytics on Spark 9. City Analytics on Spark 10. Learning Telco Data on Spark 11. Modeling Open Data on Spark Index

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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