SUPERVISED LEARNING: K-NEAREST NEIGHBOURS

Machine learning is the science and art of giving computers the ability to learn to make decisions from data without being explicitly programmed. It is a research field at the intersection of statistics, artificial intelligence, and computer science and is also known as predictive analytics or statistical learning. Machining learning methods in recent years have been integrated into our everyday life. From automatic recommendations of which movies to watch on Netflix, what foods to order, or products to purchase on platforms like Amazon and analyzing DNA sequences, and providing personalized cancer treatments. Machining learning that learns from known inputs and output pairs is called supervised learning. When there are labels present, we call it supervised learning. When there are no labels present, we call it unsupervised learning.

There are two types of supervised learning problems called classification and regression. In this article, we will be discussing the supervised classification learning algorithm K- Nearest Neighbors. We build a machine learning model from these input/output pairs, which comprise our training set. Our goal is to make accurate predictions for new, ne