FaradayOtieno/wfp_food_price_ke

Empirical analysis of staple food price trends in Kenya using Python & Power BI. Explores inflation, volatility, seasonality, and forecasting to provide data-driven insights into food security & market stability. # Staple Food Price Trends in Kenya: Inflation, Volatility, Seasonality and Predictive Forecasts This project analyzes WFP food price data for Kenya using **Python** (data cleaning, time-series modeling) and **Power BI** (interactive visualization). It explores **inflation pressures, price volatility, seasonal patterns**, and **predictive forecasts** for staple foods. --- ## 📂 Repository Structure - `data/` → raw & cleaned datasets - `notebooks/` → Jupyter notebooks for cleaning, inflation, volatility, seasonality, forecasting - `powerbi/` → Power BI dashboard (`.pbix`) + screenshots - `outputs/` → Forecast CSVs - `requirements.txt` → Python dependencies --- ## 📊 Dashboard Pages 1. **Executive Overview** → National KPIs & food price cards 2. **Inflation & Volatility** → YoY inflation, volatility by commodity 3. **Seasonality** → Heatmaps & seasonal decomposition 4. **Forecasting** → SARIMAX maize forecasts & scenario analysis --- ## 🛠 Tools Used - **Python** → Pandas, Statsmodels, Matplotlib, Seaborn - **Power BI** → KPI cards, bar charts, line charts, heatmaps, forecasting visuals --- ## 🔑 Key Insights - Beans are the most volatile staple; potatoes are the most stable - Rice is consistently the most expensive staple - Maize shows long-term deflationary trends, dragging average inflation down - Forecasts suggest rising price risks in 2026 under drought conditions --- ## 🚀 How to Run 1. Clone this repo 2. Install dependencies: ```bash pip install -r requirements.txt