Heart Disease Prediction Project

• Tech Stack: Python, Machine Learning, Jupyter Notebook
• Github URL: Project Link

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.

Dataset:

• 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.

Installation:

• Clone the Repository: First, clone this repository to your local machine using Git commands: 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.

Usage:

• Once you have the Jupyter notebook open, you can run each cell sequentially to see the data preprocessing steps, model training, evaluation, and predictions.

Results:

• The project achieves an accuracy of over 85% in predicting the presence of heart disease.

Contributing:

• Contributions to this project are welcome. Feel free to open an issue or submit a pull request with any improvements or suggestions.

Acknowledgments:

• Heart Disease UCI dataset providers
• All contributors and collaborators