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PySpark Cookbook

You're reading from   PySpark Cookbook Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788835367
Length 330 pages
Edition 1st Edition
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Concepts
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Authors (2):
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Tomasz Drabas Tomasz Drabas
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Tomasz Drabas
Denny Lee Denny Lee
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Denny Lee
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Toc

Table of Contents (9) Chapters Close

Preface 1. Installing and Configuring Spark 2. Abstracting Data with RDDs FREE CHAPTER 3. Abstracting Data with DataFrames 4. Preparing Data for Modeling 5. Machine Learning with MLlib 6. Machine Learning with the ML Module 7. Structured Streaming with PySpark 8. GraphFrames – Graph Theory with PySpark

Machine Learning with MLlib

In this chapter, we will cover how to build machine learning models with PySpark's MLlib module. Even though it is now being deprecated and most of the models are now being moved to the ML module, if you store your data in RDDs, you can use MLlib to do machine learning. You will learn the following recipes:

  • Loading the data
  • Exploring the data
  • Testing the data
  • Transforming the data
  • Standardizing the data
  • Creating an RDD for training
  • Predicting hours of work for census respondents
  • Forecasting the income level of census respondents
  • Building a clustering model
  • Computing performance statistics
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