Decision Tree Classifier in practice

 Problem statement:

Predicting whether a patient having heart disease or not based on some independent variables like sex, age, blood vessels, different diagnosis and etc.,

import pandas as pd

import numpy as np

Importing data set:

df=pd.read_csv("D:\\Raj_DataScience\\Documents\\heart.csv")

df.head()

	age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	ca	thal	target
0	63	1	3	145	233	1	0	150	0	2.3	0	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	0	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	0	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	0	2	1

df.shape

(303, 14)

Attributes and Labels:

x=df.drop('target',axis=1)

y=df['target']

df['target'].unique()

array ([0, 1])

Split the data into Train and Test:

from sklearn.model_selection import train_test_split

x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)

Implementing Decision Tree classifier:

from sklearn.tree import DecisionTreeClassifier

classifier=DecisionTreeClassifier()

classifier.fit(x_train,y_train)

y_pred=classifier.predict(x_test)

Model Evaluation:

from sklearn.metrics import confusion_matrix,classification_report,accuracy_score

print(confusion_matrix(y_test,y_pred))

print(classification_report(y_test,y_pred))

print(accuracy_score(y_test,y_pred))

[[19  7]
 [ 5 30]]
             precision    recall  f1-score   support

          0       0.79      0.73      0.76        26
          1       0.81      0.86      0.83        35

avg / total       0.80      0.80      0.80        61

0.8032786885245902