Whats difference between KNN and Kmeans

Question

Accepted Answer

KNN is a supervised learning algorithm used for classification and regression, while Kmeans is an unsupervised clustering algorithm.

KNN is a supervised learning algorithm that classifies a new data point based on the majority class of its k-nearest neighbors.
Kmeans is an unsupervised clustering algorithm that partitions data into k clusters based on similarity.
KNN requires labeled training data, while Kmeans does not require labeled data.
KNN is a lazy learner, meaning it does not learn a discriminative function from the training data, while Kmeans is an eager learner that learns a model during training.
Example: KNN can be used for predicting the species of a flower based on its features, while Kmeans can be used for segmenting customers based on their purchasing behavior.

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