https://github.com/shafaq-aslam/predicting-heart-disease-risk-with-logistic-regression-techniques
Develop a predictive model using logistic regression techniques to assess heart disease risk based on patient health metrics and data analysis.
https://github.com/shafaq-aslam/predicting-heart-disease-risk-with-logistic-regression-techniques
data-analysis heart-disease logistic-regression machine-learning machine-learning-models matplotlib numpy pandas python scikit-learn seaborn
Last synced: 6 months ago
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Develop a predictive model using logistic regression techniques to assess heart disease risk based on patient health metrics and data analysis.
- Host: GitHub
- URL: https://github.com/shafaq-aslam/predicting-heart-disease-risk-with-logistic-regression-techniques
- Owner: shafaq-aslam
- Created: 2024-10-17T04:09:34.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-11-15T17:54:30.000Z (11 months ago)
- Last Synced: 2025-02-13T21:51:38.304Z (8 months ago)
- Topics: data-analysis, heart-disease, logistic-regression, machine-learning, machine-learning-models, matplotlib, numpy, pandas, python, scikit-learn, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 195 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Predicting Heart Disease Risk with Logistic Regression Techniques
## Project Overview
This project implements logistic regression techniques to predict the risk of heart disease based on various health metrics and patient data. The model aims to assist healthcare professionals in early diagnosis and intervention.
## Table of Contents
- [Installation](#installation)
- [Usage](#usage)
- [Work Flow](#work-flow)
- [Dataset](#dataset)
- [Model Evaluation](#model-evaluation)
- [Results](#results)
- [Contributing](#contributing)
## Installation
To set up the project, clone the repository and install the required packages using the following commands:
```bash
git clone https://github.com/shafaq-aslam/Predicting-Heart-Disease-Risk-with-Logistic-Regression.git
cd Predicting-Heart-Disease-Risk-with-Logistic-Regression
pip install -r requirements.txt
```
## Usage
Run the Jupyter Notebook for training and evaluating the logistic regression model:
## Work Flow
## Dateset
The dataset used for this project contains various health metrics, including age, cholesterol levels, blood pressure, and other relevant features that contribute to heart disease risk. Dataset
## Model Evaluation
The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions.
## Results
The logistic regression model demonstrates high accuracy in predicting heart disease risk, providing valuable insights for healthcare professionals.
## Contributing
Contributions are welcome! Please feel free to submit a pull request or open an issue for any suggestions or improvements.