Exercise

# Clustering 2D points

From the scatter plot of the previous exercise, you saw that the points seem to separate into 3 clusters. You'll now create a KMeans model to find 3 clusters, and fit it to the data points from the previous exercise. After the model has been fit, you'll obtain the cluster labels for some new points using the `.predict()`

method.

You are given the array `points`

from the previous exercise, and also an array `new_points`

.

Instructions

**100 XP**

- Import
`KMeans`

from`sklearn.cluster`

. - Using
`KMeans()`

, create a`KMeans`

instance called`model`

to find`3`

clusters. To specify the number of clusters, use the`n_clusters`

keyword argument. - Use the
`.fit()`

method of`model`

to fit the model to the array of points`points`

. - Use the
`.predict()`

method of`model`

to predict the cluster labels of`new_points`

, assigning the result to`labels`

. - Hit 'Submit Answer' to see the cluster labels of
`new_points`

.