Atul Harsha gives us a demo on k nearest neighbors in Python:
In order to make any predictions, you have to calculate the distance between the new point and the existing points, as you will be needing k closest points.
In this case for calculating the distance, we will use the Euclidean distance. This is defined as the square root of the sum of the squared differences between the two arrays of numbers
Specifically, we need only first 4 attributes(features) for distance calculation as the last attribute is a class label. So for one of the approach is to limit the Euclidean distance to a fixed length, thereby ignoring the final dimension.
Check it out.