Feature Selection for K Nearest Neighbour and Decision Trees
Like others said Good visual split is a good starting point. (To me it seems the f8-f1 is a good starting point.
However you could get better results by transforming the feature set via PCA and using top eigen factors(new combination features) to train.
You've not clarified whether it is a supervised(you know the no. of classes and actual class for some data set), assuming you don't and it is unsupervised, I'd also try an algorithm like dbscan. It is generally fast. 
If you want to try the neural network approach try either sompy or neural gas