https://github.com/chouikhi-abdallah/supervised_machine_learning_algorithms

This repository reflects my leaning journey of supervised machine learning algorithms where i have worked on a project which aims to predict wether a person has some heart disease or not and comparing the differnet algorithms
https://github.com/chouikhi-abdallah/supervised_machine_learning_algorithms

desiciontree logistic-regression naive-bayes-classifier random-forest-classifier support-vector-machines

Last synced: 7 months ago
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This repository reflects my leaning journey of supervised machine learning algorithms where i have worked on a project which aims to predict wether a person has some heart disease or not and comparing the differnet algorithms

• Host: GitHub
• URL: https://github.com/chouikhi-abdallah/supervised_machine_learning_algorithms
• Owner: Chouikhi-abdallah
• Created: 2025-02-02T15:57:59.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2025-02-02T16:10:55.000Z (8 months ago)
• Last Synced: 2025-02-02T17:19:16.953Z (8 months ago)
• Topics: desiciontree, logistic-regression, naive-bayes-classifier, random-forest-classifier, support-vector-machines
• Language: Python
• Homepage:
• Size: 23.4 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Heart Disease Prediction using Machine Learning

![Model Accuracies](model_accuracies.png)

## Overview

This project aims to predict whether a patient has heart disease based on various clinical features. The dataset used contains information about patients' cardiac health, and several machine learning models are applied to classify whether a patient has heart disease or not.the code in ml_work is well commented presenting the data cleaning first then the implementation of the algorithm and the metrics of each.

## Dataset

The dataset contains the following features:

- **age**: Age of the patient.

- **sex**: Gender of the patient (0: female, 1: male).

- **cp**: Type of chest pain (0-3).

- **trestbps**: Resting blood pressure.

- **chol**: Serum cholesterol.

- **fbs**: Fasting blood sugar > 120 mg/dl.

- **restecg**: Resting electrocardiographic results.

- **thalach**: Maximum heart rate achieved.

- **exang**: Exercise-induced angina.

- **oldpeak**: ST depression induced by exercise relative to rest.

- **slope**: Slope of the peak exercise ST segment.

- **ca**: Number of major vessels colored by fluoroscopy.

- **thal**: Thalassemia (0 = normal; 1 = fixed defect; 2 = reversible defect).

- **target**: The target variable (1 = heart disease, 0 = no heart disease).

## Models Used

The following supervised learning models were implemented:

1. **Logistic Regression**

2. **Naive Bayes**

3. **Support Vector Machine (SVM)**

4. **Decision Tree**

5. **Random Forest**