Projects
Projects I have built — spanning public health dashboards, predictive tools, and geospatial analytics.
Malaria Forecasting Dashboard
An interactive malaria forecasting dashboard for public health monitoring and decision support. Built with R Shiny / Python Shiny, it provides real-time visualization of forecasted incidence trends and supports woreda-level, regional, and national public health decision-making.
Ethiopia Earthquake Tracker
A real-time web application monitoring seismic activity across Ethiopia, built with R Shiny. With Ethiopia experiencing a series of earthquakes, this tool has become more essential than ever.
Key features: Live earthquake tracking via USGS · Interactive map visualization · Auto-refresh every 5 minutes · Magnitude range filtering
Childhood Micronutrient Deficiency Prediction
An interactive prediction tool built with R Shiny that predicts childhood micronutrient deficiency using machine learning. The app employs an XGBoost model trained on demographic and health-related features, developed using the Tidymodels framework.
Customer Churn Prediction App
An interactive churn prediction tool built with R Shiny for the telecommunications sector. Uses the XGBoost algorithm integrated with the Tidymodels framework to analyze customer attributes and behaviors, providing insights into churn probability.
Spatial Distribution of Childhood MN Deficiency
An interactive spatial map showing that childhood micronutrient (MN) deficiency in Ethiopia is most prevalent in the Somali, Afar, and Amhara regions, while Gambela, Addis Ababa, and SNNP regions have the lowest prevalence.
COVID-19 Dashboard: The Case of Ethiopia
A comprehensive overview of the COVID-19 epidemic in Ethiopia. Built with R and the Quarto framework, this dashboard provides epidemiological trends, case statistics, and visualizations for evidence-based public health response.