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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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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

Using linear regression


Linear regression is the approach to model the value of a response variable y, based on one or more predictor variables or feature x.

Getting ready

Let's use some housing data to predict the price of a house based on its size. The following are the sizes and prices of houses in the City of Saratoga, CA, in early 2014:

House size (sq ft)

Price

2100

$ 1,620,000

2300

$ 1,690,000

2046

$ 1,400,000

4314

$ 2,000,000

1244

$ 1,060,000

4608

$ 3,830,000

2173

$ 1,230,000

2750

$ 2,400,000

4010

$ 3,380,000

1959

$ 1,480,000

Here's a graphical representation of the same:

How to do it…

  1. Start the Spark shell:

    $ spark-shell
    
  2. Import the statistics and related classes:

    scala> import org.apache.spark.mllib.linalg.Vectors
    scala> import org.apache.spark.mllib.regression.LabeledPoint
    scala> import org.apache.spark.mllib.regression.LinearRegressionWithSGD
    
  3. Create the LabeledPoint array with the house price as the label:

    scala> val points = Array(
    LabeledPoint(1620000...
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