SVM - Works/Doesn't work for big range of numbers?
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I have been playing around with the SVM and I have stumbled upon something interesting. 

It might be something I may be doing wrong, hence the post for comments and clarification. 


I have data set of around 3000 x 30. 

Each value is in the range of -100 to 100. Plus, they are not integers. They are floating point numbers. They are not evenly distributed. 

It's like, 
the numbers are -99.659, -99.758, -98.234 and then we wont have something till like -1.234, -1.345 and so. 

So even though the range is big, the data is clustered around at some points and they usually differ by fraction values.  
( I thought and from what my readings and understanding goes, this shouldn't ideally affect the SVM classification accuracy. Correct me if I am wrong please. **Do Comment on this with a yes or no of I am right or wrong.** )

My labels for the classification are 0 and 1.

So, then I take a test data of 30 x 30 and tried to test my SVM. 

I am getting an accuracy of somewhere around 50% when is the kernel_function as mlp. 

In other methods, I simply get 0's and NaN's as result which is weird as no 1s were in the output and I didn't understand the NaN's in the output labels. 

So, mlp was basically giving me the best results and that too just 50%. 

I have then used the method as 'QP' with 'mlp' as kernel_function and the code has been running for like 8 hours now. I don't suppose, something as small as 3400 x 30 should take that much time. 

So the question really is, is the SVM a wrong choice for the data I have? (As asked above). 
Or is there something I am missing out that is causing the accuracy to drop significantly?

Also, I know the input data is not screwed up, because I tested the same using a Neural Network and I was able to have a very good accuracy. 

Is there a way to make SVM work? Because, from what I have read on the internet- SVM should generally work better than Neural Network in this label deciding problem. 