Obesity is a growing global health concern strongly linked to behavioral and lifestyle factors such as diet, physical activity, alcohol consumption, and transportation habits. Several studies, including Palechor & De la Hoz Manotas (2019) and subsequent research using the “Estimation of Obesity Levels Based on Eating Habits and Physical Condition” dataset, have applied machine-learning approaches to predict obesity levels. However, these studies primarily emphasize predictive accuracy rather than interpretable relationships among categorical variables. This project proposes a categorical data analysis approach to uncover direct statistical associations and visualization of how lifestyle behaviors correspond with obesity categories, providing an interpretable and health-relevant perspective.

As of June 15, 2026

Spatial Machine Learning Analysis of the 2023 Parliamentary Election Zuzanna Herniczek, Weidong Bai

A Monthly MPP Adaptation Inspired by Bogdanski, Tombini & Werlang (2000) Inspired by the BCB Small-Scale Model (MPP) — Bogdanski, Tombini & Werlang (2000) June 18, 2026

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eCOTS 2026 Regional Conference @ ISU