Three models were developed to predict monthly energy cost using building characteristics, occupancy, customer type, and regional information. A multiple linear regression (MLR), a Random Forest (RF) model, and an Extreme Gradient Boosting (XGBoost) model. Model performance was evaluated using the Root Mean Square Error (RMSE) and the Coefficient of Determination (R²)

Teaching a hands-on module on Satellite Data for Agricultural Economists, covering Sentinel-2 preprocessing, cloud masking, canopy height integration, and building machine-learning training data in Google Earth Engine. Practical, coding-oriented, and focused on real agricultural applications.

Kicking off our Satellite Data for Agricultural Economists course with a hands-on introduction to R, geospatial data, and Google Earth Engine—guiding participants from setting up their first R project to extracting and visualizing real-world boundary data for applied agricultural analysis.

R data analysis

This R script details the process of using API call from "nasapower" package, to download temperature data for statistical analysis to isolate heatwaves frequency and visualizations of trends.

In this study, a conditional logistic regression model was employed to explore the relationship between selected clinical and behavioral predictors—smoking status, systolic blood pressure (SBP), and electrocardiogram (ECG) abnormalities—and the likelihood of myocardial infarction.

This cross-sectional analysis explores how demographic and behavioral variables influence knowledge levels, perceptions, and attitudes towards participation in COVID-19 clinical trials.

유럽 여행 계획 세우기 보고서입니다. 12월 8일에 작성했습니다.

This clinical trial aims to evaluate the safety profiles of two drugs by comparing their impact on key vital-sign-related adverse events.