Food Demand Forecasting Using Machine Learning And Statistical Analysis - Agarwal, Monika; Kulkarni, Samarth; Nagre, Vaishnavi; Joshi, Aanchal; Nagpure, Damini (International Journal of Computer Sciences and Engineering, 2022)
Food loss is considered a problem because food loss refers to the loss of resources such as water, soil nutrition, and investment. Food shortages lead to food shortages. This means that poor people around the world are being deprived of food as the cost of available food is increasing. Providing fresh food is one of the major constraints which is already considered by various meal provider agents or companies. Many of them want to get an estimated number of stocks for given respective times, which could help them understand patterns and stocks required. Meal delivery companies want to know the estimated number of stocks that would be delivered or manufactured over the given period based upon previous data. Forecasting process is useful in various domains like weather forecasting, restaurants, retailing etc. It determines the expected demand for the future and establishes the level of readiness required on the supply side to meet the demand. This paper represents machine learning algorithms as an application to solve such problem with forecasting number of orders for given week and meal using algorithms Random Forest, XgBoost, Support Vector Machine, etc. with optimized results.