Supervised vs. Unsupervised Learning: Key differences

Supervised and unsupervised learning are two primary types of machine learning used for different tasks. Supervised learning requires labeled data, meaning the algorithm learns from input-output pairs to make predictions. Unsupervised learning, on the other hand, deals with unlabeled data, where the algorithm identifies patterns and structures without predefined categories.

Supervised learning is used for classification and regression problems like spam detection, fraud detection, and price prediction. Unsupervised learning excels in clustering and pattern recognition, making it ideal for customer segmentation, anomaly detection, and recommendation systems.

Key Differences:

• Supervised Learning → Uses labeled data, ideal for predictive analytics.

• Unsupervised Learning → Works with unlabeled data, great for discovering hidden patterns.

• Supervised learning includes classification & regression, while unsupervised learning focuses on clustering & association.