K-Nearest Neighbor in Python

Description

The K Nearest Neighbor (KNN) is a learning algorithm that has been studied in the pattern recognition method for decades. KNN is recognized as one of the most efficient methods, and many studies have used KNN on Reuter’s experimental documents. These studies suggest  KNN and SVM works better than other methods such as linear approximation of the smallest squares, Naïve Bayes, and neural networks.
KNN is still an efficient and simple way to categorize text. The KNN idea is as follows: There is an instructional document for categorization, the K Neighbor algorithm is similar among the pre-categorized instructional documents based on one criterion, and the categories of this K-nearest neighbor are used to predict the experimental document category by scoring. Documents of each selected category are used. If more than one neighbor belongs to the same category, their total score is used as the weight of that category, and the category with the highest score is assigned to the test document, which, if it exceeds a threshold value, is more than one category.

Content title:

Introducing KNN’s nearest neighbor

Normalize data

Python programming is KNN’s closest neighbor

Two examples of Python programming KNN’s closest neighbor