dt.py

# -*- coding: utf-8 -*-
"""DT.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1Fm7pU3JyQHDDg9rLO1roAFOh8ot06EMW
"""

import pandas as pd
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
import matplotlib as plt

df = pd.read_csv("/content/pima-indians-diabetes.csv")

df

X = df.iloc[:,:-1]

Y= df['label']

df

from sklearn.model_selection import train_test_split

X_train , X_test , Y_train , Y_test = train_test_split(X,Y ,test_size= 0.3 , random_state= 0)

model = DecisionTreeClassifier()

model.fit(X_train,Y_train)

from matplotlib import pyplot as plt

fig = plt.figure(figsize=(100,100))
_ = tree.plot_tree(model , feature_names= Y)

predict = model.predict(X_test)

import matplotlib.pyplot as plt
import numpy
from sklearn import metrics

confusion_matrix = metrics.confusion_matrix(Y_test, predict)

cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix, display_labels = [False, True])

cm_display.plot()
plt.show()

accuracy_score(Y_test, predict)