Okay, I have spent a whole day now looking for an answer. I think I have a unique case when it comes to scaling and SVM. Briefly, the data attached is proteomic data collected from mass spectrometry. Each row is a protein and columns (mEV_1....nEV1...etc) are different donors/blood samples. Each protein has been normalized to internal control (a specific protein) in order to calculate a relative amount (number in each cell).
There are obvious differences in the range of numbers within each protein, however, they aren't ridiculously different like example online. My main question is if it is always necessary to scale data for rbf SVM? I understand one feature may have more influence, however, this may be the case because not every protein will be at equal levels in the blood. The rbf SVM I have generated is able to get 100% accuracy on a 70/30 train/test split with or without scaling (albeit different hyperparameters were used).