kerbemkator/kwara-yield-predictor

Statistical crop yield predictor for Kwara State farmers — OLS from scratch + Bayesian uncertainty # Statistical Agricultural Yield Predictor ### Kwara State, Nigeria — Phase 1 AI Engineering Project > *Farmers in Kwara State have no data-driven way to estimate crop yields.* > *This project builds a statistical model using regression fundamentals and* > *Bayesian probability to predict yield based on rainfall, temperature, soil pH,* > *and fertilizer application data.* --- ## Problem Statement Agricultural planning in Kwara State, Nigeria relies heavily on intuition and historical memory. This project demonstrates how statistical modeling — built from mathematical first principles — can give farmers and agricultural planners a data-driven yield estimate with calibrated uncertainty bounds. --- ## Technical Approach ### 1. OLS Regression (From Scratch) Implemented using the **Normal Equation** with NumPy only: $$\theta = (X^TX)^{-1}X^Ty$$ No scikit-learn in the core model. This validates mathematical understanding before reaching for abstractions. ### 2. Bayesian Linear Regression Extended OLS with a conjugate Normal prior to produce **full predictive distributions**: - **Posterior mean** — best estimate of yield - **95% Credible Interval** — probabilistic uncertainty bounds - **Epistemic uncertainty** — how uncertain the model is in sparse data regions Key insight: A farmer planning fertilizer budgets benefits more from knowing the *range* of possible yields than a single point estimate. --- ## Dataset | Field | Description | |---|---| | `year` | 2010–2023 | | `lga` | 16 Local Government Areas in Kwara State | | `crop` | Maize, Rice, Sorghum, Yam, Cassava | | `rainfall_mm` | Annual rainfall (mm) | | `temp_celsius` | Mean temperature (°C) | | `soil_ph` | Soil pH reading | | `fertilizer_kg_ha` | Fertilizer application rate | | `yield_kg_ha` | **Target** — crop yield in kg/hectare | > **Note:** Current dataset is synthetic, statistically calibrated to Kwara State conditions. > Real FMARD/FAO Nigeria data integration is planned for v2. --- ## Proje …