mariamnawara4/Data-science-project-Predictive-Analytics-for-Egypt-s-Food-Market-

Machine learning pipeline to analyze and predict food price volatility in Egypt using PDF/Excel extraction from CAPMAS data (2018–2026). Unlike standard time-series models, this solution integrates macroeconomic shocks—specifically USD exchange rates and fuel prices—with seasonal events (Ramadan) and global crises. # Data science project - Predictive Analytics for Egypt's Food Market Machine learning pipeline to analyze and predict food price volatility in Egypt using PDF/Excel extraction from CAPMAS data (2018–2026). Unlike standard time-series models, this solution integrates macroeconomic shocks—specifically USD exchange rates and fuel prices—with seasonal events (Ramadan) and global crises. ### Data Extraction - **2018–2020:** PDF extraction from semi-structured government reports - **2021–2026:** Excel extraction from modern formatted files ### Preprocessing & Feature Engineering - Integrated multiple sources into unified DataFrame - Applied domain filtering (removed wholesale errors, kept inflation spikes) - Added macroeconomic drivers (USD rates, fuel prices) - Engineered calendar events (Ramadan, Eids) with Sin/Cos encoding ### Modeling - **Algorithm:** XGBoost Regressor - **Leakage Prevention:** Chronological split (Jan 2024 cutoff) - **Validation:** 5-Fold Walk-Forward Time-Series Cross-Validation ### Team - Abrar Ahmed Gharbia - Mariam Mohamed Elshilek - Mariam Mohamed Nawara - Norhan Hany Elladam