This report demonstrates logistic regression using a diabetes dataset. The outcome is diabetes diagnosis (Yes/No), predicted by age, BMI, glucose level, physical activity, and family history.

This report analyses a simulated disease outbreak affecting 30 individuals across 4 communities. Network methods identify high-risk individuals, transmission pathways, and key structural features that drive epidemic spread.

Geospatial Mapping, Prediction & Interpretation Using Shapefiles

A Comprehensive Burden of Disease Study

This report presents a comprehensive geospatial analysis of flood risk in Nairobi County, Kenya. Using proximity-weighted risk modelling, multi-factor susceptibility analysis, and seasonal prediction techniques, the study identifies and quantifies flood-prone areas across the city. The findings confirm that informal settlements along river corridors — particularly Mathare, Kibera, and Mukuru — face the highest and most persistent flood risk, driven by a combination of hydrological, topographic, infrastructural, and socio-economic factors.