Understanding K-Nearest Neighbours (KNN): A Simple Guide to a Powerful Machine Learning Algorithm 🤖

K-Nearest Neighbours (KNN) is a simple and intuitive supervised machine learning algorithm that makes predictions based on how similar things are to each other.

They can be used for classification and regression.
Imagine you have a scatter plot with red and blue points, where red points represent one class and blue points represent another class. Now, let's say you get a new data point you haven't seen before, and want to know if it should be red or blue.

KNN looks at the "K" closest points (a hyperparameter that you set) to this new one — say, the 3 nearest points.

If 2 out of those 3 are red and 1 is blue, the new point is classified as red. It's like asking your closest neighbors what they are and choosing the majority answer. Although simple, KNN performs surprisingly well based on the principle of proximity.

C: visually explained
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