a note about scaling.

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

@@ -202,6 +202,9 @@ Variance is defined as "the average of the squared differences from the Mean."[s
 ## 🚀Challenge
 
 Spend some time with this notebook, tweaking parameters. Can you improve the accuracy of the model by cleaning  the data more (removing outliers, for example)? You can use weights to give more weight to given data samples. What else can you do to create better clusters?
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+Hint: Try to sale your data. There's commented code in the notebook that adds Standard Scaling to make the data columns resemble each other more closely in terms of range. You'll find that while the silhouette score goes down, the 'kink' in the elbow graph smooths out. This is because leaving the data unscaled allows data with less variance to carry more weight. Read a bit more on this problem [here](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).
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 ## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/28/)
 ## Review & Self Study