Mwenda-Mugambi/Commodity_Price_Forecasting

We aim to launch a comprehensive project to analyze and predict the dynamics of food commodity prices in various regions of Kenya. The project will utilize time series analysis to understand trends, fluctuations, and seasonality in the prices of different commodities across various markets. # Commodity_Price_Forecasting. **By:** Charles Kagwanja **|** Kevin Kagia | Lucy Njambi | Mwenda Mugambi --- This project aimed to analyze and predict wholesale prices in Nairobi, Kenya, harnessing historical data to develop predictive models. It focuses on identifying trends and factors influencing these prices, thereby facilitating informed decision-making in agricultural planning, budgeting, and policy formulation. The project's overarching goal is to contribute to poverty alleviation, improved nutrition, and the realization of the UN Sustainable Development Goal of zero hunger in Kenya. Test the deployed model here ## Business Context Focused on Nairobi County's volatile food market, this project addresses the challenge of price unpredictability that affects various sectors. By leveraging data analytics and Time series modeling, the goal is to enhance decision-making processes for entities involved in the supply chain, aligning with efforts to combat poverty and ensure food security in Kenya. ## Business Challenge The main challenge was developing a reliable forecasting model capable of accurately predicting prices, crucial for effective planning and strategizing. This involved analyzing complex market dynamics and data patterns to anticipate future trends. **Stakeholders** * **Nairobi County Government:** Plays a pivotal role in local governance, including agricultural market oversight and support within Nairobi. * **Kenyan Ministry of Agriculture:** Responsible for agricultural policies and market regulation. * **Retailers and Distributors:** Key players in the supply chain, managing distribution and sales of beans. ## Data Source The dataset for this project was obtained from the Humanitarian Data Exchange (HDX), specifically from the World Food Programme's food price database for Kenya. The dataset can be accessed here. This dataset was particularly suitable for our analysis due to the following reasons: 1. **Comprehensiveness**: It covers a …