{"id":19861073,"url":"https://github.com/harmanveer-2546/heart-failure-prediction","last_synced_at":"2026-04-14T10:31:32.735Z","repository":{"id":243347923,"uuid":"812187194","full_name":"harmanveer-2546/Heart-Failure-Prediction","owner":"harmanveer-2546","description":"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. 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This repository contains the code and resources for building a machine learning model to predict heart failure based on clinical data.\n\n## Table of Contents\n\n- [Overview](#overview)\n- [Dataset](#dataset)\n- [Requirements](#requirements)\n- [Installation](#installation)\n- [Usage](#usage)\n- [Results](#results)\n- [Contributing](#contributing)\n- [Acknowledgements](#acknowledgements)\n\n## Overview\n\nHeart 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.\n\n## Dataset\n\nThe 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.\n\n## Requirements\n\nTo run this project, you will need the following packages and libraries:\n\n- Python 3.7+\n- NumPy\n- Pandas\n- Scikit-learn\n- Matplotlib\n- Seaborn\n- Jupyter Notebook (optional, for interactive development)\n  \n\n## Installation\n\n1. Clone the repository:\n\n   ```bash\n   git clone https://github.com/your-username/heart-failure-prediction.git\n   cd heart-failure-prediction\n   ```\n\n2. Create a virtual environment and activate it:\n\n   ```bash\n   python3 -m venv venv\n   source venv/bin/activate  # On Windows, use `venv\\Scripts\\activate`\n   ```\n\n3. Install the required packages:\n\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n## Usage\n\n1. Ensure your dataset is in the `data` directory.\n\n2. Run the preprocessing script to clean and prepare the data:\n\n   ```bash\n   python preprocess.py\n   ```\n\n3. Train the model using the prepared data:\n\n   ```bash\n   python train.py\n   ```\n\n4. Evaluate the model performance:\n\n   ```bash\n   python evaluate.py\n   ```\n\n5. (Optional) Run the Jupyter Notebook to interactively explore the data and results:\n\n   ```bash\n   jupyter notebook Heart_Failure_Prediction.ipynb\n   ```\n\n## Results\n\nThe 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.\n\n## Contributing\n\nContributions 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.\n\n## Acknowledgements\n\nWe 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.\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fharmanveer-2546%2Fheart-failure-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fharmanveer-2546%2Fheart-failure-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fharmanveer-2546%2Fheart-failure-prediction/lists"}