$1.$ Create a for loop that runs the code from Part I ten more times to build different number of clusters: $.$ You can change the number of clusters by changing the parameter value that you pass to this function in the code: This function is called in the second line of the Databricks Scala code that you might have used in Part I.

$08$:$14$ AM $(33$s$)$

$1$

from pyspark.ml$.$clustering import KMeans

from pyspark.ml$.$evaluation import ClusteringEvaluator

Loads data.

dataset $=$ spark.read.format$("$libsvm$").$load$("/$FileStore$/$tables$/$colon$\_$cancer$\_\_1\_.$bz$2")$

Trains a k$-$means model.

kmeans $=$ KMeans$().$setK$(2).$setSeed$(1)$

model $=$ kmeans.fit$($dataset$)$

Make predictions

predictions $=$ model.transform$($dataset$)$

Evaluate clustering by computing Silhouette score

evaluator $=$ ClusteringEvaluator$()$

silhouette $=$ evaluator.evaluate$($predictions$)$

print$("$Silhouette with squared euclidean distance $="+$ str$($silhouette$))$

Shows the result.

centers $=$ model.clusterCenters$()$

print$("$Cluster Centers: $")$

for center in centers:

print$($center$)$