Sirkamal/somalia-food-price-time-series-analysis

This project analyzes historical market food prices in Somalia using the World Food Programme (WFP) VAM dataset. The goal is to transform a large, messy real-world dataset into structured, interpretable information and extract economic insights from staple food prices over time. .. # Somalia Food Price Time Series Analysis ## Project Overview This project analyzes historical staple food prices in Somalia using the World Food Programme (WFP VAM) dataset. The goal is to transform a large, messy real-world dataset into a clean analytical dataset and extract meaningful economic insights. The analysis focuses on staple commodities including maize, sorghum, rice, and wheat flour across multiple regions and years. --- ## Objectives * Clean and preprocess raw market price data * Handle missing values and inconsistent entries * Analyze long-term food price trends * Compare price behavior between staple commodities * Study correlation between major staple foods * Prepare dataset for predictive modeling --- ## Dataset Source: World Food Programme (WFP) - Vulnerability Analysis and Mapping (VAM) The dataset contains: * Market locations * Commodity type * Units and currency * Monthly price observations * Multi-year coverage (1995–2021) --- ## Data Preparation Key preprocessing steps: * Column selection and renaming * Date construction from Year and Month * Handling missing values (NaN analysis) * Filtering Somalia-specific records * Creating time-indexed data * Feature reduction using correlation analysis --- ## Exploratory Data Analysis Performed analyses include: * Price distribution visualization * Commodity average price comparison * Monthly trend visualization * Time series plots * Correlation heatmap between staple foods Main insight: Strong correlation was observed between Maize and Sorghum prices, suggesting shared market drivers. --- ## Tools Used * Python * Pandas * NumPy * Matplotlib * Seaborn * Jupyter Notebook --- ## Future Work * Price forecasting using regression models * Time-series modeling * Inflation proxy estimation * Market shock detection --- ## Author Kamal Mohamed Mogadishu, Somalia Open to Remote Opportunities