The K-Nearest Neighbor (KNN) algorithm is used to classify and segregate the data into groups. Another name for this method is the k-dimensional tree structure, useful for partitioning data points into a few small dimensions. Simply enter the variables you need to classify and enter the number of clusters desired. For instance, the required model inputs look like the following:
Figure 9.60: AI/ML K-Nearest Neighbor
As illustrated in Figure 9.60, the KNN results will show the testing points and identify the nearest neighbors. For example, in the first row of the testing data, we have the values 4, 7, 5 and this numerical sequence, as compared to all the other data, is most closely related to 4, 8, 5 (this can be either in the testing set or the training set).
