Applying linear regression

In this recipe, we'll see how to apply linear regression.

Getting ready

To step through this recipe, you will need a running Spark cluster in any one of the modes, for instance, local, standalone, YARN, or Mesos. For installing Spark on a standalone cluster, please refer to http://spark.apache.org/docs/latest/spark-standalone.html. Also, include the Spark MLlib package in the build.sbt file so that it downloads the related libraries and the API can be used. Install Hadoop (optionally), Scala, and Java.

How to do it…

1. The final step is to convert data into train and test datasets and apply the regression model on the training data:

           //Split the data frame into train and test sets 
           val train_Df = 
           final_Df.filter(final_Df("Item_Outlet_Sales").isNotNull) 
           val test_Df = 
           final_Df.filter(final_Df("Item_Outlet_Sales").isNull) 
           val train_Rdd =  train_Df.rdd.map { 
           row => val item_weight...