K-Means Clustering Explained: A Practical Guide with Real-World Example

📙 Chapter 3: Practical Implementation with Python

🎯 Objective

This chapter focuses on the hands-on application of K-Means clustering using Python. You’ll learn how to load data, implement clustering, visualize results, and evaluate cluster performance using tools like scikit-learn, matplotlib, and pandas.

🧰 Required Libraries

To run K-Means clustering in Python, you’ll need the following libraries:

• pandas: for data handling
• numpy: for numeric computations
• scikit-learn: for the KMeans algorithm
• matplotlib / seaborn: for data visualization

Install them using:

bash

pip install numpy pandas scikit-learn matplotlib seaborn

📥 Step 1: Import Libraries and Generate Data

Start by importing required packages and generating synthetic data:

python

import numpy as np

import pandas as pd

from sklearn.datasets import make_blobs

import matplotlib.pyplot as plt

from sklearn.cluster import KMeans

# Generate synthetic dataset

X, y = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0)

🔍 Step 2: Apply K-Means Clustering

python

# Apply KMeans with K=4

kmeans = KMeans(n_clusters=4, random_state=0)

kmeans.fit(X)

# Predict clusters

y_kmeans = kmeans.predict(X)

📌 Step 3: Visualize the Clusters

python

# Plot the clusters and centroids

plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='viridis')

plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=200, c='red', marker='X')

plt.title("K-Means Clustering Results")

plt.xlabel("Feature 1")

plt.ylabel("Feature 2")

plt.grid(True)

plt.show()

📈 Step 4: Evaluate with the Elbow Method

To determine the best value of K:

python

wcss = []

for i in range(1, 11):

    km = KMeans(n_clusters=i)

    km.fit(X)

    wcss.append(km.inertia_)

plt.plot(range(1, 11), wcss, marker='o')

plt.title('Elbow Method')

plt.xlabel('Number of clusters (K)')

plt.ylabel('WCSS')

plt.show()

🧮 Output Table: Cluster Centers and Assignments

🧠 Advanced: Apply to Real Dataset

You can load real-world data from CSV:

python

df = pd.read_csv('customers.csv')

X = df[['AnnualIncome', 'SpendingScore']]

kmeans = KMeans(n_clusters=5)

clusters = kmeans.fit_predict(X)

df['Cluster'] = clusters

Then, visualize using:

python

plt.scatter(df['AnnualIncome'], df['SpendingScore'], c=df['Cluster'], cmap='viridis')

plt.xlabel("Annual Income")

plt.ylabel("Spending Score")

plt.title("Customer Segmentation")

plt.show()

✅ Best Practices

• Always scale features before clustering
• Use KMeans++ for better initialization
• Run the model multiple times and average performance
• Use Silhouette Score for validation

📋 Summary Table