def clusterUsingKmeans(data: DataFrame, k: Int, seed: Long):KMeansModel =
    {
      val kmeans = new KMeans().setK(k).setSeed(seed) //here setK refers to the centroids, and setSeed to whatever - its like that function in python.
      val model = kmeans.fit(data)

      val pred = model.transform(data)

      val evaluator = new ClusteringEvaluator()

      val silhouette = evaluator.evaluate(pred)
      println(s"Silhouette with squared euclidean distance = $silhouette")

      // Shows the result.
      println("Cluster Centers: ")
      model.clusterCenters.foreach(println)

      model
    }