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K nearest neighbor is a super simple supervised machine learning algorithm that can be solved for both classification and regression problem. Here its a simple two dimensional example for you to have a better understanding of this algorithm. Lets say we want to classify the given point into one of the three groups. In order to find the k nearest neighbors of the given point, we need to calculate the distance between the given point to the other points. There are many distance functions but Euclidean is the most commonly used one. Then, we need to sort the nearest neighbors of the given point by the distances in increasing order. For the classification problem, the point is classified by a vote of its neighbors, then the point is assigned to the class most common among its k nearest neighbors. K value here control the balance between overfitting and underfitting, the best value can be found with cross validation and learning curve. A small k value usually leads to low bias but high va
