Using linear regression

Linear regression is the approach to model the value of a response or outcome variable y, based on one or more predictor variables or features, represented by 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.ml.linalg.Vectors
scala> import org.apache.spark.ml.regression.LinearRegression

3. Create a DataFrame with the house price as the label:

scala>  val points = spark.createDataFrame(Seq(
  (1620000,Vectors.dense(2100)),
  (1690000,Vectors.dense(2300)),
  (1400000,Vectors.dense(2046)),
...

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