Sitemap
A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Bayesian_for_Vege_Waste_Reduction
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Sugar_in_Student_Obesity
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Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Automated Food Allergy Alert System
This project involves creating a daily allergy alert list based on school lunch menus, their ingredients, and the data of students with allergies. By doing so, it helps prevent students from unknowingly consuming allergenic foods and reduces the risk of emergency situations.
Bayesian Predicted Goal for Vegetable Waste Reduction
This code analyzes vegetable waste by grade and week using Bayesian modeling in Stan, with R for data prep, prior estimation, and visualization. It predicts waste probability, compares observed vs. predicted values, and plots trends over weeks and grades.
Interactive Shiny Dashboard for Crop Seasonality
This Shiny app visualizes Korean crop production seasons using interactive filters for crops, months, and seasons, displaying results with Plotly segments and a data table, built with R, dplyr, and ggplot2.
Sugar Correlation in Student Obesity
This code analyzes meal evaluation data and BMI using R, calculating correlations, visualizing distributions with boxplots and word clouds, identifying high-risk students, and assessing program impact on snack/drink consumption with paired t-tests.