Support Vector Machine analysis - Python, how do I determing whether to use linear, square, or other types of SVM models?
By Default, SVM in Sklearn uses RBF Kernel. You have to try out all the 3 kernels, with different Gamma and C.

SVM treats outliers better and add a penalty on every outlier it detects.

You should understand your data well or vizualize well to know what kernel fits well.
SVM literally adds a new dimension to the data to create a hyperplane.