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Frank Kane's Taming Big Data with Apache Spark and Python

You're reading from   Frank Kane's Taming Big Data with Apache Spark and Python Real-world examples to help you analyze large datasets with Apache Spark

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781787287945
Length 296 pages
Edition 1st Edition
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Author (1):
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Frank Kane Frank Kane
Author Profile Icon Frank Kane
Frank Kane
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Table of Contents (8) Chapters Close

Preface 1. Getting Started with Spark 2. Spark Basics and Spark Examples FREE CHAPTER 3. Advanced Examples of Spark Programs 4. Running Spark on a Cluster 5. SparkSQL, DataFrames, and DataSets 6. Other Spark Technologies and Libraries 7. Where to Go From Here? – Learning More About Spark and Data Science

Introducing MLlib


If you're doing any real data or science data mining or machine learning stuff with Spark, you're going to find the MLlib library very helpful. MLlib (machine learning library) is built on top of Spark as part of the Spark package. It contains some useful libraries for machine learning and data mining and some functions that you might find helpful. Let's review what some of those are and take a look at them. When we're done, we'll actually use MLlib to generate movie recommendations for users using the MovieLens dataset again.

MLlib capabilities

The following is a list of different features of MLlib. They have support in the library to help you with these various techniques:

  • Feature extraction
    • Term Frequency / Inverse Document frequency useful for search
  • Basic statistics
    • Chi-squared test, Pearson or Spearman correlation, min, max, mean, and variance
  • Linear regression and logistic regression
  • Support Vector Machines
  • Naïve Bayes classifier
  • Decision trees
  • K-Means clustering
  • Principal...
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