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https://github.com/harmanveer-2546/heart-failure-prediction
Heart failure is a severe condition in which the heart is unable to pump blood effectively. Early prediction of heart failure can significantly improve patient outcomes. This project aims to build a predictive model using machine learning techniques to identify patients at risk of heart failure.
https://github.com/harmanveer-2546/heart-failure-prediction
exploratory-data-analysis feature-engineering jupyter-notebook matplotlib model-training-and-evaluation numpy pandas python sckiit-learn seaborn visualization-of-results
Last synced: 6 days ago
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Heart failure is a severe condition in which the heart is unable to pump blood effectively. Early prediction of heart failure can significantly improve patient outcomes. This project aims to build a predictive model using machine learning techniques to identify patients at risk of heart failure.
- Host: GitHub
- URL: https://github.com/harmanveer-2546/heart-failure-prediction
- Owner: harmanveer-2546
- Created: 2024-06-08T07:23:49.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-06-08T07:25:17.000Z (5 months ago)
- Last Synced: 2024-06-08T08:35:14.978Z (5 months ago)
- Topics: exploratory-data-analysis, feature-engineering, jupyter-notebook, matplotlib, model-training-and-evaluation, numpy, pandas, python, sckiit-learn, seaborn, visualization-of-results
- Language: Jupyter Notebook
- Homepage:
- Size: 253 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Heart Failure Prediction
Welcome to the Heart Failure Prediction project! This repository contains the code and resources for building a machine learning model to predict heart failure based on clinical data.
## Table of Contents
- [Overview](#overview)
- [Dataset](#dataset)
- [Requirements](#requirements)
- [Installation](#installation)
- [Usage](#usage)
- [Results](#results)
- [Contributing](#contributing)
- [Acknowledgements](#acknowledgements)
## Overview
Heart failure is a severe condition in which the heart is unable to pump blood effectively. Early prediction of heart failure can significantly improve patient outcomes. This project aims to build a predictive model using machine learning techniques to identify patients at risk of heart failure.
## Dataset
The dataset used for this project contains clinical features of patients, such as age, sex, ejection fraction, serum creatinine levels, and more. The dataset can be found in the `data` directory. It is crucial to ensure the data is preprocessed correctly before training the model.
## Requirements
To run this project, you will need the following packages and libraries:
- Python 3.7+
- NumPy
- Pandas
- Scikit-learn
- Matplotlib
- Seaborn
- Jupyter Notebook (optional, for interactive development)
## Installation
1. Clone the repository:
```bash
git clone https://github.com/your-username/heart-failure-prediction.git
cd heart-failure-prediction
```
2. Create a virtual environment and activate it:
```bash
python3 -m venv venv
source venv/bin/activate # On Windows, use `venv\Scripts\activate`
```
3. Install the required packages:
```bash
pip install -r requirements.txt
```
## Usage
1. Ensure your dataset is in the `data` directory.
2. Run the preprocessing script to clean and prepare the data:
```bash
python preprocess.py
```
3. Train the model using the prepared data:
```bash
python train.py
```
4. Evaluate the model performance:
```bash
python evaluate.py
```
5. (Optional) Run the Jupyter Notebook to interactively explore the data and results:
```bash
jupyter notebook Heart_Failure_Prediction.ipynb
```
## Results
The trained model achieves a high accuracy and recall in predicting heart failure. Detailed evaluation metrics and visualizations can be found in the `Heart_Failure_Prediction.ipynb` file.
## Contributing
Contributions are welcome! If you have any ideas, suggestions, or bug fixes, please open an issue or submit a pull request. Ensure your code adheres to the existing style and include relevant tests.
## Acknowledgements
We would like to thank the contributors and the community for their valuable input and support. Special thanks to the providers of the dataset for making this project possible.
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