KNN falls in the supervised learning family of algorithms. K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure. Classification is done by a majority vote to its neighbors. The data is assigned to the class which has the most nearest neighbors. As you increase the number of nearest neighbors, the value of k, accuracy might increase.
KNN is a non-parametric, lazy learning algorithm. When we say a technique is non-parametric , it means that it does not make any assumptions on the underlying data distribution.

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