Abstract: An approach is provided for using a machine learning model to reduce food waste. Estimation models for food items are created by using a machine learning model. The estimation models have prediction functions specifying timelines during which the food items are not spoiled and are in a condition for consumption. Prediction function solutions are generated for a food item inventory to indicate menu items for respective time periods. Each menu item includes food item(s) which are in condition for consumption based on the estimation models. Menu recommendations corresponding to the time periods are generated. Each menu includes menu item(s) indicated by the prediction function solutions. The menu recommendations reduce a waste of the food items through spoilage by increasing a likelihood of consumption of the food items within the respective timelines.

Abstract: An approach is provided for using a machine learning model to reduce food waste. Estimation models for food items are created by using a machine learning model. The estimation models have prediction functions specifying timelines during which the food items are not spoiled and are in a condition for consumption. Prediction function solutions are generated for a food item inventory to indicate menu items for respective time periods. Each menu item includes food item(s) which are in condition for consumption based on the estimation models. Menu recommendations corresponding to the time periods are generated. Each menu includes menu item(s) indicated by the prediction function solutions. The menu recommendations reduce a waste of the food items through spoilage by increasing a likelihood of consumption of the food items within the respective timelines.