Practical 8: PCA for Dimensionality Reduction

Objective

Reduce data dimensionality while preserving important information.

Duration

3-4 hours

Prerequisites

• Practicals 1-7 completed

What You’ll Learn

• ✅ Understand principal component analysis
• ✅ Implement PCA
• ✅ Determine optimal number of components
• ✅ Visualize reduced data
• ✅ Interpret explained variance

📋 Tasks

1. Apply PCA

from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

pca = PCA(n_components=2)
X_reduced = pca.fit_transform(X_scaled)

2. Explained Variance

print(f"Explained variance: {pca.explained_variance_ratio_}")
print(f"Cumulative: {np.cumsum(pca.explained_variance_ratio_)}")

plt.plot(np.cumsum(pca.explained_variance_ratio_))
plt.xlabel('Number of components')
plt.ylabel('Cumulative explained variance')
plt.show()

📊 Learning Outcomes

• Standardize features
• Apply PCA transformation
• Determine component count
• Visualize PCA results
• Interpret explained variance