• K-Nearest Neighbors (KNN) is a simple and intuitive supervised machine learning algorithm used for classification and regression tasks.
• It is based on the idea that objects (data points) that are close to each other in a feature space are likely to belong to the same class or have similar numeric values.
• In KNN, the "K" represents the number of nearest neighbors used to make predictions.
  
  

In the context of KNN, the Euclidean distance is used to quantify the similarity (or dissimilarity) between data points when determining the K nearest neighbors.

The Euclidean distance between two points, A and B, in a two-dimensional space (2D) with coordinates (x1, y1) and (x2, y2) can be calculated as:

In a multidimensional space (nD), where each data point consists of n features (attributes), the Euclidean distance between two data points,A and B can be calculated as:

from math import sqrt
plot1 = [1,3]
plot2 = [2,5]
euclidean_distance = sqrt( (plot1[0]-plot2[0]) + (plot1[1]-plot2[1]) )

import numpy as np
plot1 = [1,3]
plot2 = [2,5]
eucildean_distance = np.linalg.norm(np.array(plot1)-np.array(plot2))

The second method is mainly used for computing large datasets and its mainly used in KNN algorithm. It takes lesser computing time compared to the First method.