Nobertwabwire/east-africa-food-price-volatility

Food price volatility and forecasting intelligence system for East African agricultural markets using time series analysis and ARIMA modeling. # east-africa-food-price-volatility Food price volatility and forecasting intelligence system for East African agricultural markets using time series analysis and ARIMA modeling. The East Africa Food Price Volatility Analysis examined the drivers, trends, and forecasting patterns of staple food prices across regional markets. The objective was to understand how food price fluctuations affect market stability, supply chain planning, and food security, while demonstrating how data science can support data-driven decision-making for agribusinesses, financial institutions, policymakers, and humanitarian organizations. In this analysis, I utilized Python, SQL, machine learning, and time-series forecasting techniques to analyze historical food price data from multiple East African markets. The analysis involved a complete data science workflow, including data extraction, cleaning, transformation, visualization, statistical analysis, and predictive modeling. Large and unstructured datasets were processed using Python libraries such as Pandas and NumPy, where missing values, inconsistencies, and outliers were addressed to improve analytical accuracy and reliability. I applied advanced SQL query techniques to efficiently organize and aggregate relational datasets, demonstrating strong database management and analytical skills. To generate predictive insights, I used ARIMA forecasting models and Scikit-learn machine learning techniques to identify seasonal patterns and forecast future price volatility. I created Interactive dashboards and visualizations using Power BI and Python visualization libraries to effectively communicate trends, regional price differences, and forecasting outputs. The project highlights the ability to combine technical expertise with agricultural economics and market intelligence to solve practical, high-impact problems. Overall, the analysis demonstrated strong competencies in predictive modeling, data visualization, SQL, Python, and data-driven d …