Global Gradient Code — Food Price Forecasting
XGBoost-based ML system predicting monthly food prices in Sultan Kudarat
Timeline
Feb – Mar 2026
Role
Thesis Author / Fullstack Developer
Status
Thesis Defense
About
My undergraduate thesis project — a machine learning forecasting system that predicts next-month prices for 19 basic commodities (rice, fish, pork, vegetables) in Sultan Kudarat, Philippines. Combines XGBoost regression with hybrid correction, using satellite-derived climate data (CHIRPS rainfall, MODIS NDVI) and engineered price features. Built during my thesis research under faculty supervision, evaluated via rolling-origin backtesting against a naive baseline.
Tech Stack
ML Framework
XGBoost / Python
Backend
FastAPI
Frontend
React + TypeScript
Data Sources
CHIRPS, MODIS, DA Market Price Survey
Evaluation
Rolling-Origin Backtesting
Deployment
Local / VPS
Features
ML Forecasting Engine
- XGBoost regression with 15 engineered features (momentum, volatility, mean reversion)
- Hybrid correction formula with 0.1 shrinkage factor for conservative predictions
- Rolling-origin backtesting evaluation against naive baseline
- Covers 19 commodities across 784 monthly observations (May 2020 – Dec 2025)
Climate Data Integration
- Satellite rainfall data from CHIRPS dataset
- Vegetation index (NDVI) from MODIS satellite imagery
- Climate anomaly and change detection features
- Cyclical encoding for seasonal patterns
Fullstack Dashboard
- React + TypeScript frontend with chart visualizations
- FastAPI backend with Python ML inference
- Historical price charts and forecast comparisons
- Commodity-wise prediction breakdowns