https://github.com/sleepyminer/heart-attack-prediction
Heart Attack Prediction using Logistic Regression, LGBM, XGB and Gaussian Naive Bayes.
https://github.com/sleepyminer/heart-attack-prediction
gaussian-naive-bayes-classification lgbmclassifier logistic-regression machine-learning python3 xgboost-classifier
Last synced: 3 months ago
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Heart Attack Prediction using Logistic Regression, LGBM, XGB and Gaussian Naive Bayes.
- Host: GitHub
- URL: https://github.com/sleepyminer/heart-attack-prediction
- Owner: SleepyMiner
- Created: 2025-01-21T20:56:22.000Z (5 months ago)
- Default Branch: master
- Last Pushed: 2025-01-22T16:05:14.000Z (5 months ago)
- Last Synced: 2025-01-31T06:48:47.922Z (5 months ago)
- Topics: gaussian-naive-bayes-classification, lgbmclassifier, logistic-regression, machine-learning, python3, xgboost-classifier
- Language: Jupyter Notebook
- Homepage: https://www.kaggle.com/datasets/hosammhmdali/heart-disease-dataset
- Size: 570 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
### Table of Contents
- [Heart Attack Prediction](#heart-attack-prediction)
- [Dataset](#dataset)
- [Correlation Heatmap](#correlation-heatmap)
- [Confusion Matrix Logistic Regression](#confusion-matrix-logistic-regression)
- [Confusion Matrix LGBM](#confusion-matrix-lgbm)
- [Confusion Matrix XGB Classifier](#confusion-matrix-xgb-classifier)
- [Confusion Matrix Gaussian Naive Bayes](#confusion-matrix-gaussian-naive-bayes)
- [Installation](#installation)
- [Usage](#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:
```bash
pip install scikit-learn==1.5.2 pandas numpy matplotlib plotly seaborn
```
## Usage
To use this repository, clone it and navigate to the directory:
```bash
git clone https://github.com/SleepyMiner/Heart-Attack-Prediction.git
cd Heart-Attack-Prediction
```
Open the Jupyter notebook:
```bash
jupyter notebook HeartAttackPrediction.ipynb
```