KNN means K-Nearest Neighbour Algorithm. It can be used for both classification and regression.

It is the simplest machine learning algorithm. Also known as lazy learning (why? Because it does not create a generalized model during the time of training, the testing phase is very important where it does the actual job. Hence Testing is very costly — in terms of time & money). Also called an instance-based or memory-based learning

In k-NN classification, the output is a class membership. An object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is assigned to the class of that single nearest neighbor.

In k-NN regression, the output is the property value for the object. This value is the average of the values of k nearest neighbors.

All three distance measures are only valid for continuous variables. In the instance of categorical variables, the Hamming distance must be used.

How to choose the value of K: K value is a hyperparameter that needs to choose during the time of model building

Also, a small number of neighbors are the most flexible fit, which will have a low bias, but the high variance and a large number of neighbors will have a smoother decision boundary, which means lower variance but higher bias.

We should choose an odd number if the number of classes is even. It is said the most common values are to be 3 & 5.