A computer system which can predict consumer demand for fast food sector.
Note: This solution developed using the scratch level of python language usage. The aim is to achieve higher accuracy as much as possible in consumer demand statistics prediction in the fast-food sector. The overall goal of this application did not succeed 100%. But able to achieve closer accuracy when comparing with the actual data.
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 and fast food based businesses(based on local context - Sri Lanka). Therefore, proposes a solution to avoid this problem by predicting consumer demand for the fast-food sector. Used a forecasting algorithm known as 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 (slightly different but the same domain) used for the experiment (Such as Linear Regression, LGBM, and XGBoost). However, CatBoost was the best performing model that was selected. Therefore, windows based standalone solution was developed to yield fast-food demand prediction statistics.
pandas, numpy, scikit learn, matplotlib, seaborn, xgboost, lightgbm, catboost
https://youtu.be/qMAt5fyCOyQBlogger - https://manji-mahel.blogspot.com/2021/02/consumer-demand-prediction-for-fast.html
Dev Community - https://dev.to/devxtreme0/consumer-demand-prediction-for-fast-food-sector-4ap7