Decision Tree Classification Practice: Diabetes¶

Practice using a Decision Tree to predict diabetes from a set of patient data¶

Import Libraries, File and Inspect Data¶

In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn import tree
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, precision_score, recall_score, f1_score

import warnings
warnings.filterwarnings('ignore')
In [2]:
df = pd.read_csv('diabetes.csv')
In [3]:
df.head()
Out[3]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
0 6 148 72 35 0 33.6 0.627 50 1
1 1 85 66 29 0 26.6 0.351 31 0
2 8 183 64 0 0 23.3 0.672 32 1
3 1 89 66 23 94 28.1 0.167 21 0
4 0 137 40 35 168 43.1 2.288 33 1

Histograms for each variable¶

In [4]:
plt.rcParams.update({'font.size': 12})
df.hist(figsize=(12,8))
plt.show()

Separate data into predictor and target datasets... then train, test and split¶

In [5]:
X = df[df.columns[:8]]
y = df[df.columns[8]]
In [6]:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

Build the Decision Tree¶

In [7]:
classifier = tree.DecisionTreeClassifier(random_state=0)
classifier.fit(X_train, y_train)
Out[7]:
DecisionTreeClassifier(random_state=0)
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DecisionTreeClassifier(random_state=0)
In [8]:
plt.figure(figsize=(24,16))

tree.plot_tree(classifier)
plt.show()

Checking accuracies of the model as the max-depth varies¶

In [9]:
accuracies = []
for k in range(1,8):
  classifier = tree.DecisionTreeClassifier(random_state=0, max_depth=k)
  classifier.fit(X_train, y_train)
  accuracies.append(classifier.score(X_test, y_test))
In [10]:
k_list = range(1,8)

plt.plot(k_list, accuracies)
plt.xlabel('Max Depth of Tree')
plt.ylabel('Accuracy Score')
plt.title('Regression Accuracy using Different Decision Tree Widths')
plt.show()

Rebuild tree with depth of 7¶

In [11]:
classifier = tree.DecisionTreeClassifier(random_state=0, max_depth=7)
classifier.fit(X_train, y_train)
Out[11]:
DecisionTreeClassifier(max_depth=7, random_state=0)
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DecisionTreeClassifier(max_depth=7, random_state=0)
In [12]:
plt.figure(figsize=(24,16))

tree.plot_tree(classifier)
plt.show()

Observe Confusion Matrix¶

In [13]:
y_pred = classifier.predict(X)

print('Confusion matrix: ')

test_conf_matrix = pd.DataFrame(
    confusion_matrix(y, y_pred), 
    index=['Actual 0:', 'Actual 1:'], 
    columns=['Predicted 0:', 'Predicted 1:']
)

print(test_conf_matrix)

Confusion matrix: 
           Predicted 0:  Predicted 1:
Actual 0:           471            29
Actual 1:            73           195

Test Model with a new patient¶

In [14]:
df.columns
Out[14]:
Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',
       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],
      dtype='object')
In [15]:
#'Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'

NewPatient = [[2, 135, 70, 20, 0,30.0,0.48,35]]

predict = classifier.predict(NewPatient)

if predict == 0:
    print("The model predicts you may not have diabetes.")
else:
    print("The model predicts you may have diabetes.")

The model predicts you may not have diabetes.
In [ ]: