Parameter tuning for SVM using Grid Search

parameter_tuning.py

from sklearn.model_selection import GridSearchCV 

parameters = {'kernel':('linear', 'rbf'), 'C':[1,2,3,4,5,6,7,8,9,10], 'gamma': 
              [0.01,0.02,0.03,0.04,0.05,0.10,0.2,0.3,0.4,0.5]}
svr = svm.SVC()
grid = GridSearchCV(svr, parameters)
grid.fit(X_train, y_train)
predicted = grid.predict(X_test)
cnf_matrix = confusion_matrix(y_test, predicted)
print(cnf_matrix)
[[16  0  0]
 [ 0 13  5]
 [ 0  3  8]]

You are using too much values for each hyperparameter. The GridSearch will take forever to run even with a decent compute power.