K-Nearest Neighbors (KNN) is a popular machine learning algorithm used for both classification and regression analysis
KNN is a non-parametric algorithm, meaning that it does not make any assumptions about the underlying distribution of the data.
It works by finding the K closest neighbours to a given input data point based on their Euclidean distance
The value of K is a hyperparameter that can be tuned to optimize the performance of the model.
In classification problems, the predicted class of a given input is determined by a majority vote of the K nearest neighbors.
In regression problems, the predicted output value of a given input is determined by a weighted average of the K nearest neighbors
KNN can be sensitive to the choice of distance metric and the scaling of the features.
KNN is widely used in various applications, such as image recognition, recommender systems, and anomaly detection.