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Oct 17, 2017 - R
obesity
Here are 52 public repositories matching this topic...
Discovering the association between children's weight outcomes and the food environment around their homes
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Oct 4, 2018 - R
More takeaways on high street despite anti-obesity push
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Nov 27, 2018
Food For Thought - Do the main cuisines of food establishments affect obesity rates within Michigan?
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Dec 24, 2019
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May 27, 2020 - Jupyter Notebook
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Jun 15, 2020
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
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Jul 20, 2020 - Rich Text Format
Constructing an interactive scatter plot displaying relationships between factors shaping people's lives, such as rates of income, obesity, poverty and more.
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Oct 28, 2020 - JavaScript
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Jan 15, 2021 - HTML
The Obesity Explorer Dashboard (a UBC-MDS Project by Group #1)
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Feb 6, 2021 - HTML
Estimation of Obesity Levels
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Feb 9, 2021 - Jupyter Notebook
Using D3, this repository takes the data from the US Census Bureau's 2014 ACS 1-year estimates and creates animated visualizations from it.
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Apr 21, 2021 - JavaScript
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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May 2, 2021 - CSS
This repository demonstrates the usage of a Random Forest Model to to determine risk factors that lead to obesity.
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May 5, 2021 - Jupyter Notebook
Code to reproduce analysis and figures for 'Genetic mapping of etiologic brain cell types for obesity' (Timshel, eLife 2020)
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May 20, 2021 - R
Analyze and visualize current trends shaping people's lives, focusing on correlations discovered between health risks, age and income using D3/JavaScript and data from US Census Bureau & the Behavioral Risk Factor Surveillance System.
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Jun 16, 2021 - JavaScript
Use of OLS method, Linear Regression, K-means, Agglomerative Hierarchical, DBSCAN, Decision Tree, Random Forest, Logistic Regression, Support Vector Classifier, K-nearest neighbors, and Naive Bayes algorithms in the case study to estimate obesity levels.
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Jul 2, 2021 - Jupyter Notebook
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