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Vidushi Gupta 1 year ago committed by GitHub
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5-Clustering/2-K-Means/README.md
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@ -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.

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