Decision Tree

Decision Tree

In this chapter we will show you how to make a "Decision Tree". A Decision Tree is a Flow Chart, and can help you make decisions based on previous experience.

Decision Tree Example

import pandas as pd
from sklearn import tree
import pydotplus
from sklearn.tree import DecisionTreeClassifier

df = pd.read_csv("shows.csv")

d = {'UK': 0, 'USA': 1, 'N': 2}
df['Nationality'] = df['Nationality'].map(d)
d = {'YES': 1, 'NO': 0}
df['Go'] = df['Go'].map(d)

features = ['Age', 'Experience', 'Rank', 'Nationality']

X = df[features]
y = df['Go']

dtree = DecisionTreeClassifier()
dtree = dtree.fit(X, y)

print(dtree.predict([[40, 10, 7, 1]]))