Practical 6: K-Means Clustering

Objective

Apply K-Means clustering to segment data into meaningful groups.

Duration

3-4 hours

Prerequisites

• Practicals 1-5 completed
• Understanding of unsupervised learning

What You’ll Learn

• ✅ Implement K-Means algorithm
• ✅ Determine optimal number of clusters
• ✅ Use elbow method and silhouette analysis
• ✅ Visualize clusters
• ✅ Interpret clustering results

📋 Tasks

1. Perform K-Means Clustering

from sklearn.cluster import KMeans

kmeans = KMeans(n_clusters=3, random_state=42)
clusters = kmeans.fit_predict(X)

2. Elbow Method

inertias = []
for k in range(1, 11):
    kmeans = KMeans(n_clusters=k)
    kmeans.fit(X)
    inertias.append(kmeans.inertia_)

plt.plot(range(1, 11), inertias)
plt.xlabel('Number of clusters (k)')
plt.ylabel('Inertia')
plt.show()

3. Silhouette Analysis

from sklearn.metrics import silhouette_score

for k in range(2, 11):
    kmeans = KMeans(n_clusters=k)
    labels = kmeans.fit_predict(X)
    score = silhouette_score(X, labels)
    print(f"k={k}: {score:.4f}")

📊 Learning Outcomes

• Apply K-Means clustering
• Find optimal cluster count
• Evaluate clustering quality
• Visualize cluster results
• Interpret cluster characteristics