Accuracy difference on normalization in KNN
If you use normalized feature vectors, the distances between your data points are likely to be different than when you used unnormalized features, particularly when the range of the features are different. Since kNN typically uses euclidian distance to find k nearest points from any given point, using normalized features may select a different set of k neighbors than the ones chosen when unnormalized features were used, hence the difference in accuracy.
I had trained my model on KNN classification algorithm , and I was getting around 97% accuracy. However,I later noticed that I had missed out to normalise my data and I normalised my data and retrained my model, now I am getting an accuracy of only 87%. What could be the reason? And should I stick to using data that is not normalised or should I switch to normalized version.