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IITM Journal of Information Technology

ISSN (P) 2395-5457 | Single Blind Peer Reviewed Journal

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INSTITUTE OF INNOVATION IN TECHNOLOGY & MANAGEMENT
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A Comparative Study of Supervised and Unsupervised Machine Learning: Techniques, Applications, and Challenges

Abhinav Shukla
Belcan, Cognizant, USA
Author
Sushma Malik
Maharaja Surajmal Institute, New Delhi
Author
Anamika Rana
Maharaja Surajmal Institute, New Delhi
Author

Keywords: Supervised Machine Learning (SML), Unsupervised Machine Learning (UML), Machine Learning paradigms, Algorithms, Data labeling, Evaluation metrics, Applications of ML, Emerging trends in ML, Classification, Clustering

Abstract: Machine Learning (ML) is a rapidly evolving field that encompasses a wide range of techniques enabling computers to learn from data. Two of the most prominent paradigms in ML are Supervised Machine Learning (SML) and Unsupervised Machine Learning (UML), each suited to different problem types and requiring distinct approaches. This paper offers a comparative overview of these paradigms, focusing on their core concepts, key algorithms, evaluation metrics, and diverse applications. We discuss the challenges inherent to each approach—such as the need for labeled data in SML and the curse of dimensionality in UML—and explore emerging trends and future directions within both fields. By examining the strengths and limitations of SML and UML, this paper aims to underscore their complementary roles in addressing complex, real-world problems

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