Repository to preview, describe, and link to multiple health-related Tableau dashboards.
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Updated
May 13, 2023
Repository to preview, describe, and link to multiple health-related Tableau dashboards.
This repository contains the required code to reproduce the results reported on our paper entitled: Explaining the widening distribution of Body Mass Index: A decomposition analysis of trends for England, 2002/04-2012/14
Estimation of Obesity Levels
This was my final project for MGSC 310, I utilized R and Rstudio to visualize the data and make simple machine learning models to help predict which factors are most likely to contribute to a diagnosis of Obesity.
Le module d'Alerte de O Media est une application permettant de désamorcer les crises du comportement alimentaire. Destiné aux personnes souffrant d'hyperphagie, cet outil facilite la gestion des émotions lors d'une crise alimentaire grâce à des exercices thérapeutiques.
A data repository to show the amount of Unemployment and Adult Obesity in the state of North Carolina in 2014 and 2015.
Obesity, defined by BMI, is a global health concern linked to serious diseases. It's caused by more than just diet & exercise, with genes & social factors at play too. A combined approach of healthy habits, public health efforts, and accessible healthcare is needed to tackle this crisis.
Flux Modeling of Mammalian Energy Metabolism
Constructing an interactive scatter plot displaying relationships between factors shaping people's lives, such as rates of income, obesity, poverty and more.
Using D3, this repository takes the data from the US Census Bureau's 2014 ACS 1-year estimates and creates animated visualizations from it.
Obesity Nationally, 41.9 percent of adults have obesity. What are some factors that might be contributing to these high rates?
Scripts for assessing longitudinal quantitative traits in UKBIOBANK-linked primary care data
Implementing machine learning in comparing the accuracy of classification algorithms in classifying levels of obesity
Python & R scripts collection for AdipoAtlas project
JavaScript and D3 were used to analyze and visualize 2014 U.S. Census Bureau and the Behavioral Risk Factor Surveillance System. The data set includes data on rates of income, obesity, poverty, etc. by state. MOE stands for "margin of error."
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