Table of Contents

• Heart Attack Prediction
• Dataset
  • Correlation Heatmap
  • Confusion Matrix Logistic Regression
  • Confusion Matrix LGBM
  • Confusion Matrix XGB Classifier
  • Confusion Matrix Gaussian Naive Bayes
• Installation
• Usage

Heart Attack Prediction

This repository contains a Jupyter notebook for predicting heart attacks using machine learning models. The dataset used in this project includes various health metrics and indicators to help predict the likelihood of a heart attack.

Dataset

The dataset used in this project is Dataset Heart Disease.csv. It contains the following columns:

• age: Age of the patient
• sex: Gender of the patient (1 = male, 0 = female)
• chest pain type: Type of chest pain experienced
• resting bps: Resting blood pressure
• cholesterol: Cholesterol level
• fasting blood sugar: Fasting blood sugar level (1 = true, 0 = false)
• resting ecg: Resting electrocardiographic results
• max heart rate: Maximum heart rate achieved
• exercise angina: Exercise-induced angina (1 = yes, 0 = no)
• oldpeak: ST depression induced by exercise relative to rest
• ST slope: Slope of the peak exercise ST segment
• target: Heart disease (1 = yes, 0 = no)

Correlation Heatmap

Confusion Matrix Logistic Regression

Confusion Matrix LGBM

Confusion Matrix XGB Classifier

Confusion Matrix Gaussian Naive Bayes

Installation

To run this project, you need to install the required libraries. You can install them using pip:

pip install scikit-learn==1.5.2 pandas numpy matplotlib plotly seaborn

Usage

To use this repository, clone it and navigate to the directory:

git clone https://github.com/SleepyMiner/Heart-Attack-Prediction.git
cd Heart-Attack-Prediction

Open the Jupyter notebook:

jupyter notebook HeartAttackPrediction.ipynb