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Intermediate 18 min readModule: Module 5: Unsupervised Learning & Clustering

K-Means Clustering & Dimensionality Reduction (PCA)

Discover hidden customer segments with K-Means and compress high-dimensional feature spaces with PCA.

What You Will Learn in This Lesson

  • K-Means clustering: centroid convergence algorithm and the Elbow Method for optimal K
  • Principal Component Analysis (PCA) for reducing 100 features to 2D/3D visual embeddings
  • Anomaly and fraud detection with Isolation Forests

Introduction & Core Concept

Unsupervised learning finds hidden patterns, groupings, and clusters in unlabeled data without predefined ground-truth target labels.
WHY DOES THIS MATTER IN THE REAL WORLD?

K-Means clustering segments e-commerce users into behavioral cohorts (e.g. VIP shoppers vs bargain hunters) automatically.

Euclidean Distance Metric in Clustering

python
python
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import numpy as np
 
def euclidean_dist(p1, p2):
    return np.sqrt(np.sum((p1 - p2) ** 2))
 
user_point = np.array([25.0, 50000.0]) # Age, Income
cluster_center = np.array([28.0, 52000.0])
 
print("Distance to Cluster Center:", euclidean_dist(user_point, cluster_center))

Line-by-Line Technical Breakdown

1PCA calculates eigenvectors of the covariance matrix to project data onto axes of maximum variance.

Try It Yourself (Interactive Editor)

Modify the code in real-time and click Run to test live browser output and console logs.

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Industry Best Practices & Professional Standards

  • Always standardize features before running K-Means or PCA so high-magnitude numbers don't dominate distance.

Lesson Summary & Core Takeaways

  • Unsupervised learning discovers natural groupings and structures in unlabeled datasets.