Using the scikit-learn library and its `cluster`

module , you can use the `KMeans()`

method to build an original K-Means model that finds 6 clusters like so:

model = KMeans(n_clusters=6, init='random')

The `init`

parameter is used to specify the initialization and `init='random'`

specifies that initial centroids are chosen as random (the original K-Means).

But how do you implement K-Means++?

There are two ways and they both require little change to the syntax:

**Option 1:** You can adjust the parameter to `init='k-means++'`

.

test = KMeans(n_clusters=6, init='k-means++')

**Option 2:** Simply drop the parameter.

test = KMeans(n_clusters=6)

This is because that `init=k-means++`

is actually *default* in scikit-learn.

### Instructions

**1.**

We’ve brought back our small example where we intentionally selected unlucky initial positions for the cluser centroids.

On line 22 where we create the model, change the `init`

parameter to `"k-means++"`

and see how the clusters change. Were we able to find optimal clusters?