This project aims to predict the presence of heart disease in patients based on various medical attributes. It utilizes machine learning algorithms to analyze data and make predictions.
The dataset used in this project is the Heart Disease UCI dataset, which contains various attributes such as age, sex, cholesterol levels, and resting blood pressure, among others.
To run this project locally, follow these steps:
- Clone the repository:
git clone https://github.com/Anshg07/Heart_PROJECT.git
- Install the required dependencies:
pip install -r requirements.txt
- Run the Jupyter notebook:
jupyter notebook
Navigate to the Heart_Disease_Prediction.ipynb file and open it to explore the project.
Once you have the Jupyter notebook open, you can run each cell sequentially to see the data preprocessing steps, model training, evaluation, and predictions.
The project achieves an accuracy of over 85% in predicting the presence of heart disease.
Contributions to this project are welcome. Feel free to open an issue or submit a pull request with any improvements or suggestions.