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The k-nearest neighbor (k-NN) algorithm is a distance-based classifier, which looks at the neighbors of the data point to classify a data object whose class is unknown. A majority vote is made for the classification decision. The two prominent parameters for the algorithm are the k (neighbor) number and the distance (distance) function. There is no exact method for determining the number of neighbors, so the ideal k value is often found after trials. The cosine similarity, Manhattan, Euclidian, or Chebyshev distance is used as the distance function. One of the problems with the k-NN algorithm is the scale problem. When the method based on operating in geometric space gives a scale problem, the problem is solved by feature engineering.