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Created a Decision Tree Classifier to predict which drug a new patient would respond to based on the data collected, or predict the condition a patient has based on the drug that worked for them.

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Kernel: Python 3 (system-wide)
def warn(*args, **kwargs):
    pass
import warnings
warnings.warn = warn

Importing all necessary libraries

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

# Importing our data from IBM object storage

my_data = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/drug200.csv', delimiter=",")
my_data.head()

Pre-processing

I declared our variables as the following;

X = Feature Matrix, y = response vector

X = my_data[['Age','Sex','BP','Cholesterol', 'Na_to_K']].values
X[0:5]
array([[23, 'F', 'HIGH', 'HIGH', 25.355], [47, 'M', 'LOW', 'HIGH', 13.093], [47, 'M', 'LOW', 'HIGH', 10.114], [28, 'F', 'NORMAL', 'HIGH', 7.798], [61, 'F', 'LOW', 'HIGH', 18.043]], dtype=object)

I also created labels for the data so we can still evaluate categorical data to form our Decision Tree

from sklearn import preprocessing
le_sex = preprocessing.LabelEncoder()
le_sex.fit(['F','M'])
X[:,1] = le_sex.transform(X[:,1]) 


le_BP = preprocessing.LabelEncoder()
le_BP.fit([ 'LOW', 'NORMAL', 'HIGH'])
X[:,2] = le_BP.transform(X[:,2])


le_Chol = preprocessing.LabelEncoder()
le_Chol.fit([ 'NORMAL', 'HIGH'])
X[:,3] = le_Chol.transform(X[:,3]) 

X[0:5]
array([[23, 0, 0, 0, 25.355], [47, 1, 1, 0, 13.093], [47, 1, 1, 0, 10.114], [28, 0, 2, 0, 7.798], [61, 0, 1, 0, 18.043]], dtype=object)
y = my_data['Drug']
y[0:5]
0 drugY 1 drugC 2 drugC 3 drugX 4 drugY Name: Drug, dtype: object

With the variables established, I set up the Decision Tree to determine which drug would be the best option

# I used train/test split to establish the testing dataset and the training dataset 

from sklearn.model_selection import train_test_split

X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.3, random_state=3)

# Making sure that the dimensions match

print('Shape of X training set {}'.format(X_trainset.shape),'&',' Size of Y training set {}'.format(y_trainset.shape))

print('Shape of X testing set {}'.format(X_testset.shape),'&',' Size of Y testing set {}'.format(y_testset.shape))
Shape of X training set (140, 5) & Size of Y training set (140,) Shape of X testing set (60, 5) & Size of Y testing set (60,) 
# To model, I made an instance of the DecisionTreeClassifier and called it drugTree - I selected 'entropy' because I wanted to be able to see the information gain of each node of the tree

drugTree = DecisionTreeClassifier(criterion="entropy", max_depth = 4)
drugTree

# Then I trained the model using the training sets

drugTree.fit(X_trainset,y_trainset)

# I made predictions on the testing dataset and stored it in the variable call pTree

pTree = drugTree.predict(X_testset)

# Looking at a visual comparison of our model's prediction versus the actual data

print (pTree [0:5])
print (y_testset [0:5])
['drugY' 'drugX' 'drugX' 'drugX' 'drugX'] 40 drugY 51 drugX 139 drugX 197 drugX 170 drugX Name: Drug, dtype: object 
# I used metrics from sklearn to see how accurate the model ended up being

from sklearn import metrics
import matplotlib.pyplot as plt
print("DecisionTrees's Accuracy: ", metrics.accuracy_score(y_testset, pTree))
DecisionTrees's Accuracy: 0.9833333333333333 
# Lastly, I visualized the newly created Decision Tree

tree.plot_tree(drugTree)
plt.show()
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