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<< Day 20 | Day 22 >>

📘 Day 21: Clustering with KMeans in Scikit-learn

Welcome to Day 21 of the 30 Days of Data Science series! Today, we explore Clustering using the KMeans algorithm with Python's Scikit-learn library. Clustering is a fundamental unsupervised learning technique used to group similar data points together.

Table of Contents

🔍 What is Clustering?

Clustering is a type of unsupervised learning that involves grouping data into clusters based on their similarities. Unlike supervised learning, clustering does not use labeled data. It’s widely used in:

  • Customer segmentation
  • Document clustering
  • Image segmentation
  • Anomaly detection

📌 The KMeans Algorithm

The KMeans algorithm works by:

  1. Randomly initializing K cluster centroids.
  2. Assigning each data point to the nearest centroid.
  3. Updating centroids by calculating the mean of the assigned points.
  4. Repeating steps 2 and 3 until convergence.

KMeans tries to minimize the within-cluster sum of squares (WCSS) to ensure compact clusters.

⚙️ Installing Required Libraries

Before we proceed, ensure you have Scikit-learn installed:

pip install scikit-learn matplotlib numpy

🛠️ KMeans with Scikit-learn: Step-by-Step

Let’s implement KMeans using Scikit-learn.

1️⃣ Data Preparation

We’ll generate a sample dataset using Scikit-learn’s make_blobs function:

import numpy as np
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt

# Generating synthetic data
X, y = make_blobs(n_samples=300, centers=4, cluster_std=0.6, random_state=42)

# Visualizing the data
plt.scatter(X[:, 0], X[:, 1], s=50)
plt.title("Sample Data for Clustering")
plt.show()

Explanation:

  • n_samples: Number of data points.
  • centers: Number of clusters.
  • cluster_std: Spread of each cluster.

2️⃣ Applying KMeans

Now, let’s apply the KMeans algorithm to cluster the data into 4 groups.

from sklearn.cluster import KMeans

# Applying KMeans
kmeans = KMeans(n_clusters=4, random_state=42)
y_kmeans = kmeans.fit_predict(X)

# Printing centroids
print("Cluster Centers:")
print(kmeans.cluster_centers_)

Explanation:

  • n_clusters: The number of clusters.
  • fit_predict(): Assigns each point to a cluster and returns labels.

3️⃣ Visualizing Clusters

Let’s visualize the resulting clusters and centroids.

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

# Marking centroids
centroids = kmeans.cluster_centers_
plt.scatter(centroids[:, 0], centroids[:, 1], s=200, c='red', marker='X')
plt.title("Clusters and Centroids")
plt.show()

Output:

  • Data points are colored based on their cluster.
  • Red X marks represent the centroids.

🧠 Use Cases of Clustering

  • Market Segmentation: Grouping customers based on purchasing behavior.
  • Image Compression: Reducing colors in an image using cluster centroids.
  • Document Clustering: Grouping similar text documents.
  • Biological Analysis: Grouping genes with similar expression patterns.

🧪 Practice Exercises

  1. Apply KMeans to a custom dataset of your choice.
  2. Experiment with different values of n_clusters and observe the results.
  3. Explore the Elbow Method to determine the optimal number of clusters.

🌟 Summary

  • Clustering is an essential unsupervised learning technique.
  • KMeans groups data into clusters by minimizing WCSS.
  • Scikit-learn provides easy-to-use tools for implementing KMeans.
  • Visualizing clusters helps interpret results effectively.