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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)

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> library(arules)
> data("Adult")
> rules <- apriori(Adult,parameter = list(supp = 0.5, conf = 0.9, target = "rules"))
> summary(rules)

#set of 52 rules

#rule length distribution (lhs + rhs):sizes
# 1  2  3  4 
# 2 13 24 13