python

my_data=[['slashdot','USA','yes',18,'None'], ['google','France','yes',23,'Premium'], ['digg','USA','yes',24,'Basic'], ['kiwitobes','France','yes',23,'Basic'], ['google','UK','no',21,'Premium'], ['(direct)','New Zealand','no',12,'None'], ['(direct)','UK','no',21,'Basic'], ['google','USA','no',24,'Premium'], ['slashdot','France','yes',19,'None'], ['digg','USA','no',18,'None'], ['google','UK','no',18,'None'], ['kiwitobes','UK','no',19,'None'], ['digg','New Zealand','yes',12,'Basic'], ['slashdot','UK','no',21,'None'], ['google','UK','yes',18,'Basic'], ['kiwitobes','France','yes',19,'Basic']] class decisionnode: def __init__(self,col=- 1,value=None,results=None,tb=None,fb=None): self.col=col self.value=value self.results=results self.tb=tb self.fb=fb • Col is the column index of the criteria to be tested. • Value is the value that the column must match to get a true result. • T and fb are decisionnodes, True or False • Results store a dictionary of results for this branch.