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Added a space after the full-stop and capitalised the initial.

5-Clustering/2-K-Means/README.md

@@ -134,7 +134,7 @@ You see an array printed out with predicted clusters (0, 1,or 2) for each row of
 
 ## Silhouette score
 
-Look for a silhouette score closer to 1. This score varies from -1 to 1, and if the score is 1, the cluster is dense and well-separated from other clusters. A value near 0 represents overlapping clusters with samples very close to the decision boundary of the neighboring clusters.[source](https://dzone.com/articles/kmeans-silhouette-score-explained-with-python-exam). 
+Look for a silhouette score closer to 1. This score varies from -1 to 1, and if the score is 1, the cluster is dense and well-separated from other clusters. A value near 0 represents overlapping clusters with samples very close to the decision boundary of the neighboring clusters. [(Source)](https://dzone.com/articles/kmeans-silhouette-score-explained-with-python-exam)
 
 Our score is **.53**, so right in the middle. This indicates that our data is not particularly well-suited to this type of clustering, but let's continue.