Agricultural Yield Prediction Pipeline
Designed and implemented a supervised ML modelling framework comparing linear regression, decision trees, and random forest to predict maize, rice, and cassava yields across Tanzania; 35 years of geospatial data (FAO, NASA EarthData, NBS), automated preprocessing, cross-validation, and hyperparameter tuning.