A Comparative Study of Supervised and Unsupervised Learning Algorithms

Machine learning (ML) is a cornerstone of artificial intelligence, with supervised and unsupervised learning as two fundamental paradigms. While supervised learning is widely used for predictive modeling with labeled data, unsupervised learning excels in discovering hidden patterns in unlabeled data. This study compares popular algorithms from both categories based on accuracy, scalability, complexity, and application domain. Through literature review, experimentation, and performance metrics, we offer insights into their optimal use cases and practical limitations.