Predictive Model for Accurate Horticultural Product Pricing Using Machine Learning

• Autores: Suclle Surco, Davis Alessandro, Assereto Huamani, Andres Antonio, Herrera-Trujillo, Emilio Antonio
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Universidad Peruana de Ciencias Aplicadas
• Repositorio: UPC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorioacademico.upc.edu.pe:10757/676092
• Enlace del recurso: http://hdl.handle.net/10757/676092
• Nivel de acceso: acceso embargado
• Materia: Horticultural Products; Machine Learning; Price Prediction; Recommendation System; Sale Risk

The trade of horticultural products is a crucial sector in the local economy of Lima, Peru. Microenterprises dedicated to this activity face various challenges, including demand volatility. This volatility can decrease the likelihood of generating profits and impact the stability of the business, primarily due to the challenges associated with adjusting selling prices. To address this issue, our proposal is based on implementing the XGBoost algorithm, which has the capability to handle heterogeneous data and variables of different types. This algorithm leverages historical data to provide accurate and up-to-date price recommendations for horticultural products. This, in turn, enables micro-entrepreneurs to make informed decisions when setting prices, thereby achieving expected benefits and enhancing their competitiveness. The integration of our project with microenterprises in Lima has the potential to mitigate the risk of economic losses by offering greater accuracy in predicting future market prices. Through the development of our project, we have achieved a high level of accuracy in forecasting future prices, reaching a minimum of 90% when compared to actual prices.