v3.0.0 Milestone 4

We are excited to announce the latest updates in the CardioPredict project for our Milestone 4! I

What's Changed

• Created a Makefile
• Improved the repository based on previous milestones and peer review

Contributors:

@sandygross
@joeywwwu
@hema2022ubc
@MDSFusionist

v2.0.0 Milestone 3

We are excited to announce the latest updates in the CardioPredict project for our Milestone 3! In this release, we abstracted our code to scripts and thus could replace code cells in our notebook. The notebook is properly rendered to html.

Contributors:
@sandygross
@joeywwwu
@hema2022ubc
@MDSFusionist

Release v1.0.2: Docker Integration and Pytest Support

We are excited to announce the latest updates in the CardioPredict project! In this release, we've made significant enhancements to streamline your experience:

Docker Integration: Simplifying setup and ensuring consistency across environments, we've introduced Docker support. Now, you can easily run our project in a Docker container, ensuring a smooth and uniform experience regardless of your local setup.

Pytest Implementation: To bolster the reliability and quality of our code, we've integrated pytest for comprehensive testing. This addition enables you to run automated tests easily, ensuring the functionality and stability of the project with each change.

These updates are part of our ongoing efforts to enhance the usability and robustness of the CardioPredict project. We encourage you to try out these new features and look forward to your feedback!

Contributors:
@sandygross
@joeywwwu
@hema2022ubc
@MDSFusionist

Initial Release

We are excited to announce the initial release of our project, version v0.1.0. This release marks the first step in our journey to develop a comprehensive solution for predicting coronary heart disease risk using the Framingham Heart Study dataset.

Key Features in this release:

• Implementation of Logistic Regression and k-Nearest Neighbours (kNN) models for risk prediction.
• Initial analysis and preprocessing of the Framingham dataset.
• Basic feature analysis to identify major risk factors for heart disease.

Please note that this version is an early release and may undergo significant changes as we continue to refine our models and methods. We welcome feedback and contributions to improve future versions.

Thank you for your interest in our project, and we look forward to evolving this tool into a robust and reliable resource for heart disease risk prediction.

Contributors:
@sandygross
@hema2022ubc
@joeywwwu
@MDSFusionist