K Nearest Neighbors (KNN) Algorithm | Machine Learning - Videos | GeeksforGeeks

In this video, we are going to cover KNN Algorithm and also cover some basic examples.

KNN or K-Nearest Neighbors is one of the most important classification algorithms in Machine Learning. It is supervised learning which is used to find intense application in pattern recognition, data mining, and intrusion detection in Machine Learning. It is a simple, easy-to-implement supervised Machine Learning (ML) algorithm that can be used to solve both classification and regression problems. The K-Nearest Neighbors algorithm assumes that similar things exist in close or nearness in space.

In other words, KNN captures the idea of similarity (sometimes called distance, proximity, or closeness) with some mathematics like calculating the distance between points on a graph. What way to choose to calculate distance totally depends on the problem that we are solving.

We will use Euclidean distance which is used to calculate the between two points in Euclidean space is the length of a line segment between the two points.

In the example, we will consider one table with different classes and try to predict whether the models belong to which classes, for this we will find the distance of spaces using the Euclidean distance formula and then sort the distances with minimum distance for getting the higher probability to apply KNN.

Related Article: https://www.geeksforgeeks.org/k-nearest-neighbours/ https://www.geeksforgeeks.org/k-nearest-neighbors-with-python-ml/ https://www.geeksforgeeks.org/k-nearest-neighbor-algorithm-in-python/