Applied ML · Forecasting · ETL
Avian Forecast
County-level bird-presence forecasting from large multi-source ecological and weather data.
Question
Can we estimate the odds that a bird species is present in a U.S. county on a given day by combining sighting logs, geography, and long-term weather history?
Method
I built an ETL pipeline over roughly 700 GB of structured and unstructured sources (sightings, county shapefiles, ~20 years of weather), hosted weather retrieval with Docker/Open-Meteo, and produced feature-rich training sets for 500+ species (~39M rows). A Random Forest classifier was tuned with 10-fold cross-validation.
Result
The model reached about 0.83 precision and 0.76 F1 for county-level presence odds, giving bird-watchers actionable probability scores rather than raw occurrence dumps.
Limitation
Ecological presence is noisy and biased by observer effort. Weather and geography features help, but rare species and under-sampled counties remain harder; causal claims are out of scope.