K-Means Clustering for Customer Segmentation

Machine Learning

AI Engineering

Using K-Means Clustering for Customer Segmentation

Author

DOSSEH Ameck Guy-Max Désiré

Published

August 31, 2025

>Estimated reading time: ~10 minutes # K-Means Clustering for Customer Segmentation

Objectives

Use scikit-learn’s k-means clustering to cluster data
Apply k-means clustering on real world data for customer segmentation

Introduction

K-means is widely used for clustering in data science applications, especially for discovering insights from unlabeled data. Applications include customer segmentation, pattern recognition, and feature engineering.

Import the required libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler
import plotly.express as px
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')

K-Means on a synthetic data set

np.random.seed(0)
X, y = make_blobs(n_samples=5000, centers=[[4,4], [-2, -1], [2, -3], [1, 1]], cluster_std=0.9)
plt.scatter(X[:, 0], X[:, 1], marker='.',alpha=0.3,ec='k',s=80)

Setting up k-means

k_means = KMeans(init = "k-means++", n_clusters = 4, n_init = 12)
k_means.fit(X)
k_means_labels = k_means.labels_
k_means_cluster_centers = k_means.cluster_centers_

Visualize the clusters

fig = plt.figure(figsize=(6, 4))
colors = plt.cm.tab10(np.linspace(0, 1, len(set(k_means_labels))))
ax = fig.add_subplot(1, 1, 1)
for k, col in zip(range(len([[4, 4], [-2, -1], [2, -3], [1, 1]])), colors):
    my_members = (k_means_labels == k)
    cluster_center = k_means_cluster_centers[k]
    ax.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.',ms=10)
    ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,  markeredgecolor='k', markersize=6)
ax.set_title('KMeans')
ax.set_xticks(())
ax.set_yticks(())
plt.show()

Customer Segmentation with k-means

cust_df = pd.read_csv("https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%204/data/Cust_Segmentation.csv")
cust_df = cust_df.drop('Address', axis=1)
cust_df = cust_df.dropna()
X = cust_df.values[:,1:]
Clus_dataSet = StandardScaler().fit_transform(X)
clusterNum = 3
k_means = KMeans(init = "k-means++", n_clusters = clusterNum, n_init = 12)
k_means.fit(X)
labels = k_means.labels_
cust_df["Clus_km"] = labels

Visualize customer clusters

area = np.pi * ( X[:, 1])**2
plt.scatter(X[:, 0], X[:, 3], s=area, c=labels.astype(float), cmap='tab10', ec='k',alpha=0.5)
plt.xlabel('Age', fontsize=18)
plt.ylabel('Income', fontsize=16)
plt.show()

We can also see this distribution in 3 dimensions for better understanding. Here, the education parameter will represent the third axis instead of the marker size.

k-means will partition your customers into mutually exclusive groups, for example, into 3 clusters. The customers in each cluster are similar to each other demographically.

Summary

This article demonstrated how to use k-means clustering for customer segmentation, including synthetic data and real-world customer data.