A Machine Learning Approach to Forecast the Food Prices for Food Security Issues

Food security is one of the major challenges that must be addressed to provide sufficient and nutritious food to everyone. In this work, we have used Ethiopia's monthly food price data from 2000 to 2023. The work aims to effectively predict food prices by applying machine learning models. We have applied the SVM, KNN, LR, DT, and RM models, in which it is found that RF is one of the best models with the highest accuracy 98.98%. Further, to consolidate it, we have applied the Friedman test to measure the model's ranking, and we have observed that RF is the highest ranked among all the applied models. The findings of the work conclude the same.