For the dataset on the link : http://archive.ics.uci.edu/ml/machine-learning-databases/00240/

Build a SVM (with RBF Kernel) classfier for this data.

SVM with RBF takes 2 parameters: gamma (length scale of the RBF kernel) and C (the cost

parameter). Use the following values for gamma: 1e-3, 1e-4. Use the following values for C: 1, 10,

100, 1000.

Choose the best values of gamma and C using 10-fold cross-validation, based on model F1-score.

Draw a surface plot of F1-score with respect to gamma and C.

Use the best value of gamma and C to re-train the model on the training set and use it to predict the

labels of the test set. Report the confusion matrix, multi-class averaged F1-score and accuracy.