Exercise

# Elbow method

In the previous exercise you've implemented MiniBatch K-means with 8 clusters, without actually checking what the right amount of clusters should be. For our first fraud detection approach, it is important to **get the number of clusters right**, especially when you want to use the outliers of those clusters as fraud predictions. To decide which amount of clusters you're going to use, let's apply the **Elbow method** and see what the optimal number of clusters should be based on this method.

`X_scaled`

is again available for you to use and `MiniBatchKMeans`

has been imported from `sklearn`

.

Instructions

**100 XP**

- Define the range to be between 1 and 10 clusters.
- Run MiniBatch K-means on all the clusters in the range using list comprehension.
- Fit each model on the scaled data and obtain the scores from the scaled data.
- Plot the cluster numbers and their respective scores.