MUHIYADIN2025/Somalia-Food-Price-Prediction

# Somalia-Food-Price-Prediction # 🇸🇴 Machine Learning-Based Food Price Prediction System for Somalia ### Predicting Staple Food Prices Across Somali Markets Using Machine Learning An end-to-end **Machine Learning and data-driven forecasting system** designed to analyze and predict staple food commodity prices across local markets in Somalia. The project combines historical market data, temporal patterns, regional characteristics, and commodity information to develop predictive models and deliver results through an interactive **Streamlit web application**. Built using historical market price data from the **World Food Programme (WFP)**, the system provides a structured approach to food price analysis and forecasting that can support data-driven decision-making for traders, researchers, humanitarian organizations, and other stakeholders. --- ## 📌 Project Overview Food price volatility remains a significant economic and social challenge. Changes in commodity prices can affect household purchasing power, local businesses, market planning, and humanitarian operations. This project applies Machine Learning techniques to historical food price data in order to identify patterns associated with: * 📅 Temporal trends and seasonality * 🗺️ Regional and local market differences * 🛒 Commodity categories * 🌾 Individual food commodities * 💰 Historical price behavior The system processes these factors and uses trained regression models to estimate the **commodity price in USD**. ### 🎯 Prediction Target **Target Variable:** ```text usdprice ``` ### 🔑 Primary Features The model uses the following core features: * **Year** * **Month** * **Administrative Region (`admin1`)** * **Local Market (`market`)** * **Commodity Category (`category`)** * **Commodity Name (`commodity`)** ### 🤖 Machine Learning Models The project evaluates multiple regression models, with primary focus on: * **Random Forest Regressor** * **XGBoost Regressor** ### 🖥️ Interactive Application The tr …