Decision Trees

In this lab exercise, you will learn a popular machine learning algorithm, Decision Tree. You will use this classification algorithm to build a model from historical data of patients, and their respond to different medications. Then you use the trained decision tree to predict the class of a unknown patient, or to find a proper drug for a new patient.

Import the Following Libraries:

numpy (as np)
pandas
DecisionTreeClassifier from sklearn.tree

import numpy as np 
import pandas as pd
from sklearn.tree import DecisionTreeClassifier

About dataset

Imagine that you are a medical researcher compiling data for a study. You have collected data about a set of patients, all of whom suffered from the same illness. During their course of treatment, each patient responded to one of 5 medications, Drug A, Drug B, Drug c, Drug x and y.

Part of your job is to build a model to find out which drug might be appropriate for a future patient with the same illness. The feature sets of this dataset are Age, Sex, Blood Pressure, and Cholesterol of patients, and the target is the drug that each patient responded to.

It is a sample of binary classifier, and you can use the training part of the dataset
to build a decision tree, and then use it to predict the class of a unknown patient, or to prescribe it to a new patient.