Predictive Analytics Capstone Project - Classification Models in Python

This Jupyter Notebook is the notebook for my capstone project on heart disease prediction.

Source of dataset

Vanderbilt University Medical Center Department of Biostatistics http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/rhc.html

Project Goal

Predict will a critically ill patient who undergoes RHC die during the procedure.

Introduction

A right heart catheterization (RHC) is a procedure to check conditions of patient's heart and lungs. 58 features were created for this predictive models. Seven classification models (K nearest neighbors, Logistic Regression, Stochastic Gradient Descent, Naive Bayes, Decision Tree, Random Forest, Gradient Boosting) were built, trained, and evaluated. Best model that gives the highest AUC score was chosen and outcomes were communicated.

Prerequisites

Python (using version 3.6.8) Jupyter Notebook

Authors

Lee Ping Tay

Acknowledgments

My professor, Andrew Long GitHub Page