The nearest neighbor algorithm classifies a data instance based on its neighbors. The class of a data instance determined by the k-nearest neighbor algorithm is the class with the highest representation among the k-closest neighbors.

In this chapter, we will cover the basics of the k-NN algorithm - understanding it and its implementation with a simple example: Mary and her temperature preferences. On the example map of Italy, you will learn how to choose a correct value k so that the algorithm can perform correctly and with the highest accuracy. You will learn how to rescale the values and prepare them for the k-NN algorithm with the example of house preferences. In the example of text classification, you will learn how to choose a good metric to measure the distances between the data points, and also how to eliminate the irrelevant dimensions in higher-dimensional space to ensure that the algorithm performs accurately.