K-Nearest Neighbors, or KNN, is a classification algorithm used in machine learning. It is a non-parametric and lazy learning algorithm, meaning it does not make any assumptions about the underlying data distribution and does not require any prior training.

In KNN, the idea is to find the “k” nearest data points to a given data point and use the majority class of those points to predict the class of the given data point. For example, let’s say we have a dataset of red and blue points and we want to predict the class of a new point. Using KNN, we would first identify the k-nearest points to the new point. If the majority of those points are red, then the new point would be classified as red. If the majority are blue, then it would be classified as blue.

One advantage of KNN is that it is simple and easy to implement. It also has a low computational cost, as it only requires calculating the distance between the new point and the existing points. However, one limitation of KNN is that it can be sensitive to the choice of k and the distance metric used.

Another example of using KNN is in recommendation systems. Let’s say we have a dataset of users and the movies they have watched. Using KNN, we can predict which movies a given user would like based on the movies that similar users have liked. In this case, the k-nearest users would be determined based on the similarity of their movie preferences. The majority of the movies liked by those users would then be recommended to the given user.

Overall, KNN is a useful classification algorithm that can be applied to a variety of scenarios. It is simple to implement and can provide accurate predictions, but it is important to carefully select the value of k and the distance metric used to avoid overfitting or underfitting the data.