Recently Published
Predicting Risk of Hospital Readmission Within 30 Days of Discharge Using Machine Learning Models With Tidymodels in R
In this project, I will be following a guided lesson to learn more about using the tidymodels package in R for machine learning. The overarching aim of this project is to build, tune, compare and evaluate multiple machine learning models to examine the effect of patient demographic factors and clinical records (HbA1c level, number of diagnosis, length of stay) on the likelihood of readmission within 30 days of discharge.
What Predicts Skepticism Towards New GLP-1 Weight Loss Medications Like Ozempic?: A Partial Proportional Odds Logistic Regression Analysis of U.S. Survey Data
This project uses publicly-available survey data from the American Trends Panel Wave 142 by the Pew Research Center published in 2024. The current study will analyze responses from the survey that pertain to evaluating public perception of GLP-1 receptor antagonists,as well as which factors contribute to higher levels of skepticism of the drugs.
Creating a Lolipop Graph
Using the in-built data set msleep in R to create a lolipop graph. This project was created by following a tutorial by R Programming 101 on YouTube. His channel is an extremely helpful resource in learning R and ggplot2. Link to video: https://www.youtube.com/watch?v=XbeYUzsdlgw
Exploratory Analysis of BRFSS Dataset for the Duke Statistics with R Course
This project aims to answer the following research questions using the BRFSS dataset:
1. Is there a link between sleeping less and depression. Is this relationship moderated by self-perceived emotional support?
2. Are people who are diagnosed with diabetes at a younger age more likely to need insulin and have eye-health related issues.
3. Are people who sleep less, more likely to have high blood pressure (hypertension)?
Predicting My Girlfriend’s Weight Given Her Height: A Simple Linear Regression Model
Learning simple linear regression in R.