We have already seen K-Means, Let’s see it’s neighbor K-Nearest Neighbors

K-Means and K-Nearest Neighbors(KNN) use similar algorithms, their prediction and use cases are different and they fall in different category of ML, that is Unsupervised and Supervised respectively.

K-Means is used for clustering, given a set of data, K-Means clusters them into K clusters.

KNN is used for Classification and Regression, that is classify a new data point into a nearest to the K data points, to say in terms of algorithm,

- Take the least squared distances of the data set,
- Sort them in ascending order and
- Take top K data points, find out the majority of classes
- In case of Regression, take the mean of K data points

We can see it’s different from the K-Means, though it uses same Least Squared for computing.

**Use Cases**

- Recommender Systems, for example based on user purchased items we can recommend similar items
- Classify documents , aka Text Classification, we can find similar documents

That’s all one need to know..