1. Start the Spark shell:

        $ spark-shell

2. Perform the required imports:

        scala> import org.apache.spark.mllib.tree.DecisionTree
        scala> import org.apache.spark.mllib.regression.LabeledPoint
        scala> import org.apache.spark.mllib.linalg.Vectors
        scala> import org.apache.spark.mllib.tree.configuration.Algo._
        scala> import org.apache.spark.mllib.tree.impurity.Entropy

3. Load the file:

        scala> val data = sc.textFile("tennis.csv") 

4. Parse the data and load it into LabeledPoint:

        scala>  val parsedData = data.map {
line =>  val parts = line.split(',').map(_.toDouble)
 LabeledPoint(parts(0), Vectors.dense(parts.tail)) }

5. Train the algorithm with this data:

        scala> val model = DecisionTree.train(parsedData,
        Classification, Entropy, 3) 

6. Create a vector for no rain, high wind, and cool temperature:

        scala> val v=Vectors.dense(0.0,1.0,0.0)

7. Predict whether tennis should be played:

        scala> model.predict(v)