decision_tree.py
def prediction(clf: "classifier object", data: {'Time': int, 'Mood':int, 'Age':int}):
    '''Takes in a classifier and dict of data and outputs a number that represents
    the type of food they would want'''
    d=[[data['Time'],data['Mood'], data['Age']]]
    return clf.predict(d)
Takes in a classifier and dict of data and outputs a number that represents
    the type of food they would want
Takes in a classifier and dict of data and outputs a number that represents
the type of food they would want
Takes in a classifier and dict of data and outputs a number that represents the type of food they would want
def prediction(clf: "classifier object", data: {'Time': int, 'Mood':int, 'Age':int}):
    d=[[data['Time'],data['Mood'], data['Age']]]
    return clf.predict(d)
def prediction ( clf : "classifier object" , data : { 'Time' : int , 'Mood' : int , 'Age' : int } ) : d = [ [ data [ 'Time' ] , data [ 'Mood' ] , data [ 'Age' ] ] ] return clf . predict ( d )
Takes in a classifier and dict of data and outputs a number that represents
the type of food they would want
Takes in a classifier and dict of data and outputs a number that represents the type of food they would want
'''Takes in a classifier and dict of data and outputs a number that represents
    the type of food they would want'''
clf
"classifier object"
data
{'Time': int, 'Mood':int, 'Age':int}