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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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Concepts
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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 (13) Chapters Close

Title Page
Credits
About the Author
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started with Spark FREE CHAPTER 2. Spark Basics and Spark Examples 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

Improving the quality of the similar movies example


Now it's time for your homework assignment. Your mission, should you choose to accept it, is to dive into this code and try to make the quality of our similarities better. It's really a subjective task; the objective here is to get you to roll up your sleeves, dive in, and start messing with this code to make sure that you understand it. You can modify it and get some tangible results out of your changes. Let me give you some pointers and some tips on what you might want to try here and we'll set you loose.

We used a very naive algorithm to find similar movies in the previous section with a cosine similarity metric. The results, as we saw, weren't that bad, but maybe they could be better. There are ways to actually measure the quality of a recommendation or similarity, but without getting it into that, just dive in there, try some different ideas and see what effect it has, and maybe they qualitatively will look better to you. At the end...

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