Logistic Regression for Heart Attacks

Abstract

This code segment demonstrates the implementation of a logistic regression model for heart attack prediction using the Heart Attack Analysis & Prediction dataset.

Imports

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report

Load Dataset

df = pd.read_csv("/kaggle/input/heart-attack-analysis-prediction-dataset/heart.csv")
df

X = df.drop(['output'], axis=1)
y = df['output']

Scaler

scaler = StandardScaler()
scaler.fit(X)
X_scaled = scaler.transform(X)

Test Train Split

X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state = 43)

Model Training

model = LogisticRegression()
model.fit(X_train,y_train)

Test Model

y_pred = model.predict(X_test)
print("Accuracy: {}".format(accuracy_score(y_test, y_pred)))

Accuracy: 0.8852459016393442

Analysis

feature_importance = pd.DataFrame({
    'Feature': X.columns,
    'Coefficient': model.coef_[0]
})


print(feature_importance.sort_values(by='Coefficient', ascending=False))

features

print(classification_report(y_test,y_pred))

report