K-Nearest Neighbor Classifier Best K Value
There's several ways that you can choose your k value for kNN - 

You can use the common formula k = sqrt(n) where n is the number of data points in your training set or you can try choosing k where there is a good balance between computation expense vs noise.

Consider your what fits your problem: 
Do you care about runtime? The higher the k, the more expensive computationally it is to run.

It looks like around  k= 6-10 you get some diminishing returns - you could set it there to get a good balance between noise and computation cost, but ultimately it is a very arbitrary selection, so pick what suits your use case best.