plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y, cmap='viridis', edgecolor='k') plt.title('PCA of Iris Dataset') plt.xlabel('Principal Component 1') plt.ylabel('Principal Component 2') plt.show()Read our in-depth tutorial showing PCA Python Examples.Enjoyed...
Given below is a simple example code for one of the unsupervised learning techniques. Let’s use the K-Means clustering algorithm as an example. For this, we’ll use the popular Python library scikit-learn. Make sure you have it installed using“pip install scikit-learn” import numpy as n...
(n_samples=300, noise=0.05, random_state=42) # Apply DBSCAN dbscan = DBSCAN(eps=0.2, min_samples=5) clusters = dbscan.fit_predict(X) # Plotting the clusters plt.scatter(X[:, 0], X[:, 1], c=clusters, cmap='viridis') plt.xlabel('Feature 1') plt.ylabel('Feature 2') plt....