@@ -157,11 +157,11 @@ Our score is **.53**, so right in the middle. This indicates that our data is no
> 🎓 range: These are the iterations of the clustering process
> 🎓 random_state: "Determines random number generation for centroid initialization."[source](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans)
> 🎓 random_state: "Determines random number generation for centroid initialization." [Source](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans)
> 🎓 WCSS: "within-cluster sums of squares" measures the squared average distance of all the points within a cluster to the cluster centroid.[source](https://medium.com/@ODSC/unsupervised-learning-evaluating-clusters-bd47eed175ce).
> 🎓 WCSS: "within-cluster sums of squares" measures the squared average distance of all the points within a cluster to the cluster centroid. [Source](https://medium.com/@ODSC/unsupervised-learning-evaluating-clusters-bd47eed175ce).
> 🎓 Inertia: K-Means algorithms attempt to choose centroids to minimize 'inertia', "a measure of how internally coherent clusters are."[source](https://scikit-learn.org/stable/modules/clustering.html). The value is appended to the wcss variable on each iteration.
> 🎓 Inertia: K-Means algorithms attempt to choose centroids to minimize 'inertia', "a measure of how internally coherent clusters are." [Source](https://scikit-learn.org/stable/modules/clustering.html). The value is appended to the wcss variable on each iteration.
> 🎓 k-means++: In [Scikit-learn](https://scikit-learn.org/stable/modules/clustering.html#k-means) you can use the 'k-means++' optimization, which "initializes the centroids to be (generally) distant from each other, leading to probably better results than random initialization.
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@@ -224,7 +224,7 @@ Previously, you surmised that, because you have targeted 3 song genres, you shou
## Variance
Variance is defined as "the average of the squared differences from the Mean" [source](https://www.mathsisfun.com/data/standard-deviation.html). In the context of this clustering problem, it refers to data that the numbers of our dataset tend to diverge a bit too much from the mean.
Variance is defined as "the average of the squared differences from the Mean" [(Source)](https://www.mathsisfun.com/data/standard-deviation.html). In the context of this clustering problem, it refers to data that the numbers of our dataset tend to diverge a bit too much from the mean.
✅ This is a great moment to think about all the ways you could correct this issue. Tweak the data a bit more? Use different columns? Use a different algorithm? Hint: Try [scaling your data](https://www.mygreatlearning.com/blog/learning-data-science-with-k-means-clustering/) to normalize it and test other columns.
