bmi-analysis

Investigates how age, gender, BMI, and hypertension relate to pulse, blood pressure, and glucose using MANOVA, MANCOVA, and multivariate regression. Includes data cleaning, Box‑Cox transforms, model comparison, and post‑hoc analyses to identify key health predictors.

Support vector machine model predicting BMI deviation in psychiatric populations. Trained on N=1,504, validated on N=559 (SCZ/CHR/ROD). Multi-site pipeline with clinical measure integration. Published research code.

A structured categorical analysis of BMI using statistical inference, visualization, and exploratory techniques to evaluate relationships between weight classification and demographic or behavioral factors. This project demonstrates clean analytical workflows, reproducible methods, and clear interpretation of categorical data for real‑world health.

BMI and obesity analysis of 64 individuals using Python, Pandas, Matplotlib, and Seaborn, with BMI classification and data visualizations.