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Apache Spark for Data Science Cookbook

You're reading from   Apache Spark for Data Science Cookbook Solve real-world analytical problems

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Product type Paperback
Published in Dec 2016
Publisher
ISBN-13 9781785880100
Length 392 pages
Edition 1st Edition
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Authors (2):
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Padma Priya Chitturi Padma Priya Chitturi
Author Profile Icon Padma Priya Chitturi
Padma Priya Chitturi
Nagamallikarjuna Inelu Nagamallikarjuna Inelu
Author Profile Icon Nagamallikarjuna Inelu
Nagamallikarjuna Inelu
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Table of Contents (11) Chapters Close

Preface 1. Big Data Analytics with Spark 2. Tricky Statistics with Spark FREE CHAPTER 3. Data Analysis with Spark 4. Clustering, Classification, and Regression 5. Working with Spark MLlib 6. NLP with Spark 7. Working with Sparkling Water - H2O 8. Data Visualization with Spark 9. Deep Learning on Spark 10. Working with SparkR

Univariate analysis


Once the data is available, we have to spend lot of time and effort in data exploration, cleaning and preparation because the quality of the high input data decides the quality of calculating the output. Hence, once we identify the business questions, the first step of data exploration/analysis is univariate analysis, which explores the variables one by one. The methods of univariate analysis depend on whether the variable type is categorical or continuous.

Getting ready

To step through this recipe, you will need a running Spark cluster in any one of the modes, that is, local, standalone, YARN, or Mesos. For installing Spark on a standalone cluster, please refer to http://spark.apache.org/docs/latest/spark-standalone.html. Also, include the Spark MLlib package in the build.sbt file so that it downloads the related libraries and the API can be used. Install Hadoop (optionally), Scala, and Java.

How to do it…

  1. Let's take the example of Titanic dataset. On April 15, 1912, the...

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