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Spark Cookbook

You're reading from   Spark Cookbook With over 60 recipes on Spark, covering Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX libraries this is the perfect Spark book to always have by your side

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Product type Paperback
Published in Jul 2015
Publisher
ISBN-13 9781783987061
Length 226 pages
Edition 1st Edition
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Author (1):
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Rishi Yadav Rishi Yadav
Author Profile Icon Rishi Yadav
Rishi Yadav
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Toc

Table of Contents (14) Chapters Close

Preface 1. Getting Started with Apache Spark 2. Developing Applications with Spark FREE CHAPTER 3. External Data Sources 4. Spark SQL 5. Spark Streaming 6. Getting Started with Machine Learning Using MLlib 7. Supervised Learning with MLlib – Regression 8. Supervised Learning with MLlib – Classification 9. Unsupervised Learning with MLlib 10. Recommender Systems 11. Graph Processing Using GraphX 12. Optimizations and Performance Tuning Index

Calculating correlation


Correlation is a statistical relationship between two variables such that when one variable changes, it leads to a change in the other variable. Correlation analysis measures the extent to which the two variables are correlated.

If an increase in one variable leads to an increase in another, it is called a positive correlation. If an increase in one variable leads to a decrease in the other, it is a negative correlation.

Spark supports two correlation algorithms: Pearson and Spearman. Pearson algorithm works with two continuous variables, such as a person's height and weight or house size and house price. Spearman deals with one continuous and one categorical variable, for example, zip code and house price.

Getting ready

Let's use some real data so that we can calculate correlation more meaningfully. The following are the size and price of houses in the City of Saratoga, California, in early 2014:

House size (sq ft)

Price

2100

$1,620,000

2300

$1,690,000

2046

$1...

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