SVM model classifying into one class only, after standardization
I can't add a comment so I'll post this here. 

My main question is why are your feature values so wildly different different between you your test and training sets? 

For example the raw values of feature 1.

Training set: All values > 20, looks like they average about 35-40.
Test set: All values around 2, looks like they all fall between 2-3. 

What is the reason for this. Also have you shuffled you data before performing a train  test split? The you should plot a distribution of the feature values for both your training and test sets sets, and make sure they are somewhat similar. 

It looks like you you have a bad covariate shift between training and test sets. Don't worry about this yet as I think it's a data preprocessing problem. First answer the question if the distribution of values of each feature are similar between training and test sets.