What is the difference between supervised and unsupervised learning in data science?

Supervised and unsupervised learning are fundamental machine learning paradigms used for different types of data science problems.\n\n• Supervised: Uses labeled training data to predict outcomes\n• Unsupervised: Finds hidden patterns in unlabeled data\n• Supervised examples: Classification (spam detection), regression (price prediction)\n• Unsupervised examples: Clustering (customer segmentation), dimensionality reduction (PCA)\n\nExample: Netflix uses supervised learning to predict movie ratings based on user history (labeled data). It uses unsupervised learning to group similar users into clusters for targeted content recommendations without predefined categories.