Heart Disease Prediction

The "Heart-Disease-Prediction" dataset contains information on risk factors for heart disease. It includes 15 medical parameters such as age, sex, blood pressure, cholesterol, and obesity, which are used for predicting the likelihood of heart disease.

To get started with using this dataset, follow the steps below:

1. Clone the repository:

   git clone https://github.com/spandan0724/Heart-Health-Predictor/new/main?readme=1

   This command will download the code and dataset from the GitHub repository to your local machine.

2. Install required Python packages: Make sure you have the necessary Python packages installed to run the code.

3. Run the code on Jupyter Notebook: Launch Jupyter Notebook on your machine and navigate to the cloned repository. Open the notebook file (usually with a .ipynb extension) and run the code cells to perform heart disease prediction based on the provided dataset.

Paraphrased instructions: To use the "Heart-Disease-Prediction" dataset, first, clone the repository from the provided GitHub link. Ensure that you have the required Python packages installed. Finally, open the code in Jupyter Notebook and execute the cells to perform heart disease prediction using the dataset.