Machine Learning Hero

Practical Exercises Chapter 5

Exercise 1: K-Means Clustering

Task: You are given a synthetic dataset containing two features. Use K-Means clustering to group the data into three clusters and visualize the clusters with their centroids.

Feature 1	Feature 2
2.5	3.1
1.8	2.3
3.4	3.0
4.1	4.2
1.9	2.8
3.6	3.7
2.2	3.5
4.0	4.5

Solution:

import numpy as np

import matplotlib.pyplot as plt

from sklearn.cluster import KMeans

# Data: Features 1 and 2

X = np.array([[2.5, 3.1], [1.8, 2.3], [3.4, 3.0], [4.1, 4.2], [1.9, 2.8],

[3.6, 3.7], [2.2, 3.5], [4.0, 4.5]])

# Apply K-Means clustering with 3 clusters

kmeans = KMeans(n_clusters=3, random_state=42)

kmeans.fit(X)

# Get cluster labels and centroids ...