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 features of this dataset are Age, Sex, Blood Pressure, and the Cholesterol of the patients, and the target is the drug that each patient responded to.
1. It is a sample of multiclass classifier. You should build a Decision Tree classifier to prescribe a drug to a new patient.
2. Datasets: Drug200.
3. Convert the categorical data into numerical using Python.
4. Report the results in terms of accuracy.
5. Try Decision Tree classifier from Scikit with three criterions: Gini, entropy, log loss. Report the accuracy for each criterion.
6. Try Decision Tree classifier from Scikit with three different max_depth value according to your choice. One must be default value=None. Report the accuracy for different max depth.
