k Nearest Neighbor (KNN): introduction The necessity of data mining techniques has emerged quite immensely nowadays due to massive increase in data. Data mining is the process of extracting patterns and mining knowledge from data. K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure (e.g., distance functions). KNN has been used in statistical estimation and pattern recognition already in the beginning of 1970’s as a non-parametric technique. The model for KNN is the entire training dataset. When a prediction is required for a unseen data instance, the KNN algorithm will search through the training dataset for the k-most similar instances. The prediction attribute of the most similar instances is summarized and returned as the prediction for the unseen instance. Nearest neighbor classifiers is a lazy learner’s method and is based on learning by analogy. It is a supervised classification technique which is used widely. Unlike the previously described methods the nearest neighbor method waits until the last minute before doing any model construction on a given tuple. In this method the training tuples are represented in N-dimensional space. When given an unknown tuple, k-nearest neighbor classifier searches the k…