K-Nearest Neighbors (KNN) algorithm is a classification algorithm
that works by finding the most similar data points in the training data, and attempt to make an educated guess based on their classifications

The algorithm follows these steps:

1. Select a value for k (e.g.: 1, 2, 3, …..)
2. Calculate the Euclidian distance between the point to be classified and every other point in the training data set
3. Pick the k closest data points (points with the k smallest distances)
4. Run a majority cote among selected data points, the dominating classification is the winner. Point is classified based on the dominant class
5. Repeat


I hope you will enjoy this video and find it useful and informative.

Happy Learning!

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#KNN #KNearestNeighbors

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