severincandymellania-ux/Tanzania_Crop_Yield_ML

ML-powered crop yield prediction for Tanzania (maize, rice, cassava) - food security analysis and agricultural lending risk assessment using 35 years of FAO data. # 🌾 Food Security & Agricultural Lending Risk | Machine Learning Crop Yield Forecasting | Tanzania Agritech > **Can machine learning predict crop yields well enough to replace gut-feel agricultural lending decisions in Tanzania?** > This project proves it can - and quantifies exactly how much money wrong model selection costs. --- ## 📊 Impact at a Glance | Metric | Result | |---|--------------------------------------------------------------------------------------------------------------------------------------------------------------| | Best model R² (Rice) | **0.979** - explains 97.9% of yield variation | | Maize prediction improvement | **R² 0.003 → 0.632** after adding climate variables (+629%) | | Regional yield disparity identified | **Songwe yields 6.5x more maize than Lindi** | | Wrong model cost (cassava) | Linear Regression predicts **3,912 kg/ha** by 2030 vs Random Forest's **6,423 kg/ha** - a 64% underestimation that directly misallocates agricultural credit | | Farm records analysed | **26,000+** NBS Tanzania microdata records across 31 regions | | Years of data | **35 years** (1990–2024) | --- ## 🗂️ Executive Summary **Business Problem:** Tanzanian banks and agricultural lenders assess smallholder loan applications using field visits and subjective judgment - e …