K-Nearest Neighbors (KNN) is a popular machine learning algorithm used for classification and regression tasks. The goal of KNN is to predict the class of a new data point by analyzing the K closest data points in the training set.

The "K" in KNN refers to the number of neighbors that are considered when making a prediction. For example, if K = 3, then the algorithm will look at the 3 closest data points to the new data point and predict the class that is most common among them.

KNN is a non-parametric algorithm, meaning that it makes no assumptions about the underlying data distribution. This makes it a versatile algorithm that can be applied to a wide variety of problems, but also means that it can be sensitive to noisy or irrelevant features in the data.

To use KNN, the first step is to split the data into a training set and a test set. The algorithm will use the training set to learn the patterns in the data, and the test set will be used to evaluate its performance.

KNN is known for its simplicity and ease of implementation, but it can be computationally expensive for large datasets. Additionally, choosing the optimal value of K can be difficult and requires some trial and error.

Overall, K-Nearest Neighbors is a powerful algorithm that can be used for a variety of machine learning tasks. Its simplicity and flexibility make it a popular choice among data scientists and machine learning practitioners.