A practical comparison of four approaches for modeling insurance claim costs – Linear Regression, Two‑Part (Logistic+Gamma), Tweedie GLM, and XGBoost with Tweedie loss. Using simulated car insurance data, this project demonstrates the actuarial edge of the Tweedie distribution for zero‑inflated, skewed claim data. All models perform similarly, with XGBoost achieving a marginal 2.21% RMSE improvement. The real value lies in feature importance – revealing past claims and mileage as the strongest predictors.

Obesity is a growing global health concern strongly linked to behavioral and lifestyle factors such as diet, physical activity, alcohol consumption, and transportation habits. 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.

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