Explain K-Nearest Neighbors classification Algorithm with a suitable example

The K-Nearest Neighbors (K-NN) algorithm is a simple, non-parametric, and lazy supervised learning algorithm used for classification and regression tasks. It works by classifying data points based on their proximity to other data points in the feature space.

In K-NN, the "K" represents the number of nearest neighbors the algorithm considers when making a prediction. When a new data point is to be classified, the algorithm identifies the K nearest training data points and assigns the class _________ ____ _____ ___ _______ ___.
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