Food Demand Forecasting using Machine Learning Approaches

With demand forecasting we can predict in how much amount consumer or customer need the product. The business will not function well without well-defined demand forecasting. Inadequate future demand forecasting could leads to loss of money and wastage of food. There are several techniques of artificial intelligence and machine learning which could be utilized for prediction of food demand. Authors in the proposed work applied machine learning techniques include Logistic Regression, Decision Tree, Random Forest, Extreme Gradient Boosting, K-Nearest Neighbours, Gaussian Naive Bayes, and Linear Discriminant Analysis for prediction of food demand. For validation of the proposed work authors have calculated accuracy, variance, and error for all the models. The proposed models are also compared and it has been concluded that Linear Discriminant Analysis performed well in terms of accuracy which was calculated to be 71.9%. In future authors will try same models on different datasets for generalisation.