Abstract:
Lack of fast-food fulfillment to the consumer, excesses of fast food over the estimated demand and business loss profit cause by inaccurate demand prediction are common nowadays in fast food centers. Therefore, this study proposes a solution to avoid this problem by predicting consumer demand for fast food using a forecasting algorithm known CatBoost with a data categorization technique. Fast food demand is affected by several independent variables such as seasonality, trend, price fluctuation and length of historical data. A combination of these selected variables was used to calculate demand prediction using parameter tuning in the CatBoost algorithm and other algorithms. Such as Linear Regression, LGBM and XGBoost. However, CatBoost was the best performing model were selected. Therefore, windows native standalone solution was developed to yield fast-food demand prediction statistics.
