Predicting food prices in Kenya using machine learning: a hybrid model approach with XGBoost and gradient boosting
This data-driven decision support system can help policymakers and agricultural stakeholders (such as the Kenyan government) plan for future trends in food prices, potentially helping to prevent food insecurity in Kenya.
Authors
- Kennedy Senagi (ORCID: https://orcid.org/0000-0002-0757-3907)
- Evans Omondi
- John Olukuru (ORCID: https://orcid.org/0000-0002-8534-2346)
- Betsy Muriithi (ORCID: https://orcid.org/0000-0003-1393-2045)
- Benard O. Ogol
Institutions
- Strathmore University (KE)
- International Centre of Insect Physiology and Ecology (KE)
- African Population and Health Research Center (KE)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2025-10-24
- DOI
- https://doi.org/10.3389/frai.2025.1661989
- Primary Topic
- Energy Load and Power Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 0.00