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)),
  (2000000,Vectors.dense(4314)),
  (1060000,Vectors.dense(1244)),
  (3830000,Vectors.dense(4608)),
  (1230000,Vectors.dense(2173)),
  (2400000,Vectors.dense(2750)),
  (3380000,Vectors.dense(4010)),
  (1480000,Vectors.dense(1959))
  )).toDF("label","features")

4. Initialize linear regression:

scala> val lr = new LinearRegression()

5. Train a model using this data:

scala> val model = lr.fit(points)

5. Create some test data:

scala> val test = spark.createDataFrame(Seq(Vectors.dense(2100)).map(Tuple1.apply)).toDF("features")

6. Make predictions for the test data:

scala> val predictions = model.transform(test)