'phd': 'yes'
}, False)]
dt = DecisionTree()
tree = dt.build_tree_id3(inputs)
print(tree)
new_input = {'level': 'Mid', 'lang': 'Python', 'tweets': 'no', 'phd': 'no'}
label = dt.classify(tree, new_input)
print(label)
inputs = [[19, 28], [21, 27], [20, 23], [28, 13], [11, 15], [13, 13], [-49, 0],
[-46, 5], [-41, 8], [-49, 15], [-34, -1], [-22, -16], [-19, -11],
[-25, -9], [-11, -6], [-12, -8], [-14, -5], [-18, -3], [-13, -19],
[-9, -16]]
hcl = HierarchicalClustering()
base_cluster = hcl.bottom_up_cluster(inputs)
print(base_cluster)
clusters = hcl.generate_clusters(base_cluster, 3)
print(clusters)
users = Table(["user_id", "name", "num_friends"])
users.insert([0, "Hero", 0])
users.insert([1, "Dunn", 2])
users.insert([2, "Sue", 3])
users.insert([3, "Chi", 3])
users.insert([4, "Thor", 3])
users.insert([5, "Clive", 2])
users.insert([6, "Hicks", 3])
users.insert([7, "Devin", 2])
users.insert([8, "Kate", 2])
users.insert([9, "Klein", 3])
