# Customer Segmentation

Learn how to segment customer bases using k-means clustering.

## We'll cover the following

There are a number of unsupervised clustering algorithms, but k-means is one of the easiest. It can segment an unlabeled dataset into a predetermined number of groups. The input parameter `k`

stands for the number of clusters or groups we would like to form. However, if `k`

is too small, then the centroids won’t lie within the clusters. But if `k`

is too large, some of the clusters may be oversplit.

## Implementing k-means clustering

The k-means algorithm follows these steps:

Choose the number of clusters (

`k`

).Randomly assign centroids for each cluster.

Assign each observation to a cluster for which the centroid is the closest based on the similarity or distance measures.

Compute a new centroid for each cluster.

Repeat steps 3 and 4 as long as the centroids keep changing.

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