Heart Disease Predictor

Contributors: Sarah Eshafi, Hui Tang, Long Nguyen, Marek Boulerice

About

This repository covers a machine learning model analysis with a goal to predict angiographic coronary disease in patients. Data is pulled from patients undergoing angiography at the Cleveland Clinic in Ohio. This analysis is composed of Exploratory Data Analysis, testing of various machine models on a training data set, model optimization via hyperparameter, and final model performance analysis. The final model is shown to have promising results, though limitations apply and further testing and optimization is recommended.

Running the Report

To run the analysis:

1. Using Docker

note - the instructions in this section also depends on running this in a unix shell (e.g., terminal or Git Bash)

To replicate the analysis, install Docker. Then clone this GitHub repository and run the following command at the command line/terminal from the root directory of this project:

docker compose up

Copy the link from the output (the link would look like below)

and paste it to your browser and change the port number from 8888 to 9999 to launch jupyter notebook.

Dependencies

• conda (version 24.7.1 or higher)
• conda-lock (version 2.5.7 or higher)
• jupyterlab (version 4.2.5 or higher)
• nb_conda_kernels (version 2.5.1 or higher)
• Python and packages listed in environment.yml

Licenses

The software code contained within this repository is licensed under the MIT license. See the license file for more information.