Abstract:
The right amount of food is an important part of maximizing restaurant revenues and also
satisfying customers. When demand is lower than production, assets and resources will be
lost, and when demand is more than availability there will be a lack of consumer
satisfaction. This scenario is a real-life challenge for resource optimization, which can be
efficiently overcome by machine learning, pattern recognition, and data mining techniques.
Demand forecasting systems are developed to the anticipation of the future demand for a
specific product. This study is an outcome of the project to build such a demand forecasting
system. The developed solution, FoodScape was built by making use of previous sales data
and additional attributes of promotions and discount data to forecast the demand for a food
item for the upcoming week.
FoodScape is focused on regression and a machine learning model that was trained by
applying a voting regressor with several other regression algorithms. A web graphical
interface was facilitated to interact and escort the user. Prediction is made considering the
inputs captured by the user. The demand for food items will be able to forecast one by one
and the results will be displayed separately.
