Learn

K-Means Clustering

Visualize After K-Means

We have done the following using `sklearn`

library:

- Load the embedded dataset
- Compute K-Means on the dataset (where
`k`

is 3) - Predict the labels of the data samples

And the labels resulted in either `0`

, `1`

, or `2`

.

Let’s finish it by making a scatter plot of the data again!

This time, however, use the `labels`

numbers as the colors.

To edit colors of the scatter plot, we can set `c = labels`

:

plt.scatter(x, y, c=labels, alpha=0.5) plt.xlabel('sepal length (cm)') plt.ylabel('sepal width (cm)')

### Instructions

**1.**

Create an array called `x`

that contains the Column `0`

of `samples`

.

Create an array called `y`

that contains the Column `1`

of `samples`

.

**2.**

Make a scatter plot of `x`

and `y`

, using labels to define the colors.