Kernel selections in SVM
Start with linear kernel and see if your data is linearly seperable or not. Performing that is simpler than looking for early indications. 

Linear kernels are suggested when the number of features is larger than the number of observations in the dataset (otherwise RBF will be a better choice).

However, once you conclude that you have non-linear data, you can try to visualize and map the non-linear separable data into a higher dimensional space and see if it makes the data linearly separable. That is what a kernel does anyway and so you visualizing will give you insights and indications about the type of kernel to be used.



This link might help.