1. Start the Spark shell:

        $ spark-shell

2. Do the required imports:

        scala> import org.apache.spark.mllib.util.MLUtils
        scala> import org.apache.spark.mllib.classification.SVMWithSGD

3. Load the data as an RDD:

        scala> val data = 
        MLUtils.loadLibSVMFile(sc,"s3a://sparkcookbook
        /medicaldata/diabetes.libsvm")

4. Count the number of records:

        scala> data.count

5. Divide the dataset into equal halves of training data and testing data:

        scala> val trainingAndTest = data.randomSplit(Array(0.5,0.5))

6. Assign the training and test data:

        scala> val trainingData = trainingAndTest(0)
scala> val testData = trainingAndTest(1)

7. Train the algorithm and build the model for 100 iterations (you can try different iterations, but at a certain point of inflection , you'll see that the results start to converge and that point of inflection is the right number of iterations to choose):

        scala> val model = SVMWithSGD.train(trainingData,100)

8. Now you can use this model to predict a label for any dataset. Predict the label for the first point in the test data:

        scala> val label = model.predict(testData.first.features)

9. Create a tuple that has the first value as a prediction for the test data and the second value as the actual label, which will help us compute the accuracy of our algorithm:

        scala> val predictionsAndLabels = testData.map( r => 
        (model.predict(r.features),r.label))

10. You can count how many records have predictions and actual label mismatches:

        scala> predictionsAndLabels.filter(p => p._1 != p._2).count