K-Nearest Neighbour is one of the simplest Supervised Machine Learning algorithms. The k-NN algorithm can be used for Regression and Classification, but mostly it is used for Classification problems. K-NN is a non-parametric algorithm, which means it does not make any assumptions on underlying data. The KNN algorithm can compete with the most accurate models because it makes highly accurate predictions. The main advantage of KNN over other algorithms is that KNN can be used for multiclass classification. It is commonly used for its ease of interpretation and low calculation time.

WHY DO WE NEED A K-NN ALGORITHM?

Suppose there are two categories, i.e., Category A and Category B, and we have a new data point p1. We need to find that this data point will lie in which of these categories, so we need a K-NN algorithm to solve this type of problem. With the help of K-NN, we can easily identify the category or class of a particular dataset. Consider the below diagram:

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