For classification algorithms like KNN, we measure the distances between pairs of samples and these distances are influenced by the measurement units also. ... To avoid this miss classification, we should normalize the feature variables.

Is normalization necessary for KNN?

That's a pretty good question, and is unexpected at first glance because usually a normalization will help a KNN classifier do better. Generally, good KNN performance usually requires preprocessing of data to make all variables similarly scaled and centered.

Does KNN need feature scaling?

KNN requires scaling of data because KNN uses the Euclidean distance between two data points to find nearest neighbors. Euclidean distance is sensitive to magnitudes. The features with high magnitudes will weight more than features with low magnitudes.