Project Title

Heart Disease Analysis and Prediction.

Project Description

The purpose of this analysis was to use our knowledge of supervised machine learning to create a binary classifier that can predict the chance of heart disease.

You are very welcome to have a look at our presentation slide deck and Tableau for deeper insight into our story on Heart Disease Analysis and Prediction.

Heart Disease Analysis and Predictions.pptx

https://public.tableau.com/app/profile/sandra.botica
Heart_Disease viz

Contributing Members

Ufuoma Atakere & Sandra Botica

Students @ UWA 6 month Data Analytics Bootcamp November 2022- June 2023

Acknowledgments

Data sourced from Kaggle.

https://www.kaggle.com/datasets/rashikrahmanpritom/heart-attack-analysis-prediction-dataset?select=heart.csv

Folder Resources <heart.csv>

Technologies used

• Python notebook
• Matplotlib
• Seaborn
• Sklearn
• QuickDBD
• PostgreSQL
• pgAdmin4
• Tableau

Getting Started

1. <heart_ML.ipynb> for summary statistics, plots, machine learning models.

• Random Forest

• Logistic Regression

• PCA (Principal Component Analysis)

  This notebook populates the images folder used for the slidedeck.

2. <heart_ML_NN.ipynb>

• Neural Network - colab

3. A report/writeup of this project can be found in the file <report.md>

Contact

Feel free to contact Ufuoma or Sandra with any questions.

• Ufuoma Ijevu [email protected]
• Sandra Botica [email protected]