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Apache Spark 2.x Cookbook

You're reading from   Apache Spark 2.x Cookbook Over 70 cloud-ready recipes for distributed Big Data processing and analytics

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Product type Paperback
Published in May 2017
Publisher
ISBN-13 9781787127265
Length 294 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 (13) Chapters Close

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

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.We see correlation in our daily life. The height of a person is correlated with the weight of a person, the load carrying capacity of a truck is correlated with the number of wheels it has, and so on. 

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. The 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...

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