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A machine learning model application designed to predict the likelihood of a person having heart disease based on various health parameters. The model is built using the Random Forest algorithm and trained on the Cleveland Heart Disease dataset, achieving a high level of accuracy in predicting the presence or absence of heart disease.

Pre-model has practical applications in healthcare, allowing healthcare providers to assess the risk of heart disease in patients and take necessary preventive measures. Additionally, individuals can use the model to monitor their own risk factors and take proactive steps towards improving their health.The Heart Disease Prediction Model is available on GitHub and welcomes contributions from developers and researchers to improve and refine the model.

To use this heart disease prediction model, you need to have Python installed on your system. You can install Python from the official website (https://www.python.org/downloads/).

Clone the repository to your local machine using the following command:

git clone https://github.com/username/heart-disease-prediction.git

pip install -r requirements.txt

Usage The heart disease prediction model can be run by executing the heart disease predictive model.ipynb file.

python Heart_Disease_Predictive_Model.ipynb

Once the model is executed, it will prompt you to enter various health parameters such as age, blood pressure, cholesterol levels, smoking habits, etc. Once you have entered all the required information, the model will predict the likelihood of a person having heart disease.

Contributions are welcome to improve the heart disease prediction model. To contribute, follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b new-feature)
3. Make your changes
4. Commit your changes (git commit -m "Add new feature")
5. Push to the branch (git push origin new-feature)
6. Create a pull request

License This heart disease prediction model is released under the GNU GENERAL PUBLIC License. See LICENSE for details.