This report presents a full exploratory and modeling workflow for Kenya's assistive technology indicators (WHO Global Health Observatory data). The analysis includes data cleaning, visualization, logistic regression, decision trees, random forests, clustering (with k-means and PCA), and ordinal regression, producing clear insights for policy and humanitarian planning. All code is provided reproducibly in RMarkdown, with diagnostics and troubleshooting tips for common errors when adapting scripts for RPubs. Data is public, and the workflow is usable for similar humanitarian or development indicator analysis in R.

Hypothesis Testing

Elaborado por willson avila y carlos mario gonzalez

GEOG-391 lab 7