k-Nearest Neighbors in Python

k-Nearest Neighbors classification is a type of lazy learning as it does not attempt to construct a general internal model, but simply stores instances of the training data. Classification is computed from a simple majority vote of the k nearest neighbors of each point. Advantages: This algorithm is simple to implement, robust to noisy training data, and effective if training data is large. Disadvantages: Need to determine the value of k and the computation cost is high as it needs to computer the distanc