The K-Nearest Neighbors (KNN) algorithm is one of the simplest and most effective supervised machine learning algorithms used for both classification and regression tasks.It’s known as a lazy learning or instance-based learning algorithm because it doesn’t learn an explicit model during training. Instead, it makes decisions based on stored data — comparing new input with its “neighbors.”

KNN works on a very simple idea:

“Similar things exist close to each other.”

When a new data point comes in, KNN looks at the ‘K’ nearest data points (neighbors) from the training dataset and decides its output (class or value) based on the majority vote or average of those neighbors.

For example, if you want to predict whether a new email is spam, KNN checks the most similar past emails (neighbors). If most of them are spam, the new one will likely be spam too.