I have gone through a tone of links to understand the concept of Kernel linear and rbf but it's still not clear to me along with gamma and C values (I do know for linear kernel we only use C value). From what I have read if the number of features are greater it's better to use linear and if the features are less rbf should be used. I have total 821 instances in data and 19 features and 1 class label, which kernel should I use? Also please explain to me gamma and C in possible easiest way. Thanks
You can use GridSearchCV in this case and can specify all the kernels in the parameters and it will give you the best model. check the following link. http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html GridSearchCv is pretty good in tuning the parameters and since your dataset is not that big so it won't take much time.
"C" I guess is used for regularization to avoid the overfitting. gamma is used for non-linear classification problems. It tells the influence of one training example on the others. Smaller gamma means high influence and vice-versa