0. Data Preprocessing

0.1 Importing the libraries 

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

0.2 Importing the dataset 

dataset = pd.read_csv("heart-disease.csv")
dataset
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
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
298 57 0 0 140 241 0 1 123 1 0.2 1 0 3 0
299 45 1 3 110 264 0 1 132 0 1.2 1 0 3 0
300 68 1 0 144 193 1 1 141 0 3.4 1 2 3 0
301 57 1 0 130 131 0 1 115 1 1.2 1 1 3 0
302 57 0 1 130 236 0 0 174 0 0.0 1 1 2 0

303 rows × 14 columns

0.3 Check if any null value 

dataset.isna().sum()

age         0
sex         0
cp          0
trestbps    0
chol        0
fbs         0
restecg     0
thalach     0
exang       0
oldpeak     0
slope       0
ca          0
thal        0
target      0
dtype: int64
dataset.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 303 entries, 0 to 302
Data columns (total 14 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   age       303 non-null    int64  
 1   sex       303 non-null    int64  
 2   cp        303 non-null    int64  
 3   trestbps  303 non-null    int64  
 4   chol      303 non-null    int64  
 5   fbs       303 non-null    int64  
 6   restecg   303 non-null    int64  
 7   thalach   303 non-null    int64  
 8   exang     303 non-null    int64  
 9   oldpeak   303 non-null    float64
 10  slope     303 non-null    int64  
 11  ca        303 non-null    int64  
 12  thal      303 non-null    int64  
 13  target    303 non-null    int64  
dtypes: float64(1), int64(13)
memory usage: 33.3 KB

0.4 Split into X & y 

X = dataset.drop("target", axis=1)
X.head()
age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal
0 63 1 3 145 233 1 0 150 0 2.3 0 0 1
1 37 1 2 130 250 0 1 187 0 3.5 0 0 2
2 41 0 1 130 204 0 0 172 0 1.4 2 0 2
3 56 1 1 120 236 0 1 178 0 0.8 2 0 2
4 57 0 0 120 354 0 1 163 1 0.6 2 0 2 
y = dataset["target"]
y.head()

0    1
1    1
2    1
3    1
4    1
Name: target, dtype: int64

0.6 Splitting the dataset into the Training set and Test set 

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 2509)

1.Training the model on the Training set 

from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(random_state=2509)
classifier.fit(X_train, y_train)

RandomForestClassifier(random_state=2509)

1.1 Score 

classifier.score(X_test,y_test)

0.8552631578947368

2.Predicting the Test set results 

y_pred = classifier.predict(X_test)

2.2 Making the Confusion Matrix 

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
print(cm)

[[28  5]
 [ 6 37]]

from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred))

              precision    recall  f1-score   support

           0       0.82      0.85      0.84        33
           1       0.88      0.86      0.87        43

    accuracy                           0.86        76
   macro avg       0.85      0.85      0.85        76
weighted avg       0.86      0.86      0.86        76