Leveraging machine learning models for food security and crisis management in Algeria

The food sector in Algeria and many countries around the world faces multiple challenges, ranging from environmental pollution and climate change to fluctuations in production and distribution processes, increasing the complexities of maintaining food safety and mitigating health and environmental risks. This paper addressed analysis and prediction of food insecurity risks through the development of predictive models. Various models were reviewed and applied to real market data, focusing on predicting product prices and assessing food safety risks. The results showed that Polynomial Regression and Decision Tree models were the most efficient in accurately predicting and classifying the products, confirming their effectiveness in food safety applications. These findings contribute to providing accurate recommendations and making informed decisions to enhance food safety and better crisis management in the future.