Hf-wip: a machine learning approach for behavioral insights and sustainable food waste management

The Household Food Waste Intention Predictor (HF-WIP) is an advanced tool designed to predict behavioral intentions for effective food waste management. This study analyses nine critical demographic, economic, and behavioral aspects using data from 505 respondents to identify factors that influence food waste reduction practices. Developed with a rigorous approach of nine non-linear models, including fine-tuned SVR (RBF), Random Forest, Gradient Boosting, and Neural Networks, HF-WIP demonstrates robust accuracy and adaptability. Its novel approach bridges analytics with personalized recommendations, enabling actionable insights for users. The HF-WIP’s primary advantage lies in its capacity to identify tailored strategies for households, while supporting policymakers with data-driven interventions and assisting retailers in reducing supply chain inefficiencies. This integration of predictive capabilities with practical applications fosters sustainable practices at multiple levels. The tool’s usefulness can be extended to food waste reduction education through interactive materials and smart platforms. The commercialization potential is immense, ranging from subscription-based apps for households to partnerships with organizations and retailers. HF-WIP aligns directly with sustainability development goal (SDG) by promoting responsible consumption, aimed at reducing food waste and reducing environmental impact. As a transformative solution, it offers innovative, scalable benefits to address the pressing issue of global food waste.