Artificial intelligence’s main component, machine learning, enables systems to learn on their own and improve performance via experience, doing away with the need for explicit programming. This cutting-edge field focuses on equipping computer programs with the ability to access vast datasets and derive intelligent decisions from them. One of the cornerstone algorithms in machine learning, the K-nearest neighbours (KNN) algorithm, is known for its simplicity and effectiveness. KNN leverages the principle of storing all available data points within its training dataset and subsequently classifying new, unclassified cases based on their similarity to the existing dataset. This proximity-based classification approach renders KNN a versatile and intuitive tool with applications spanning diverse domains. This document explores the inner workings of the K-nearest neighbours’ algorithm, its practical applications across various domains, and a comprehensive examination of its strengths and limitations. Additionally, it offers insights into practical considerations and best practices for the effective implementation of KNN, illuminating its significance in the continually evolving landscape of machine learning and artificial intelligence.
Keywords: Machine learning; k-nearest neighbours; artificial intelligence; knn; cuting edge field
Citation : Rekha Gangula and Venkateswarlu .B, “Exploring the Power and Practical Applications of K-Nearest Neighbours (KNN) in Machine Learning”, Journal of Computer Allied Intelligence (JCAI), vol.02, no.01, pp.8-15,2024.