PREDICTING FUTURE VISITORS IN THE RESTAURANT BUSINESS USING MACHINE LEARNING

Aygerim Bortan,Beisenbek Baisakov

doi:10.55452/1998-6688-2021-18-3-36-41

Abstract

Restaurant owners must reliably assess restaurant customers in order to function effectively and productively to enhance the restaurant's service. It is important to have a successful forecast in order to prevent losses and boost service and market optimization. There are a variety of machine learning (ML) approaches that can be used to make these predictions, but each visitor is unique and will act in a unique way. As a result, we want to estimate how many guests a restaurant may expect in the future using big data and supervised training in this study. We used three different machine learning methods in a real dataset from supervised training to predict how many visitors a restaurant dataset "Recruit restaurant visitor forecasting" will receive: Neural Network, XGBoost and Random Forest regressor. The predicted values were compared to the real data after the simulation. Basically, algorithms used had mean errors of less than 9.5278, but the Random Forest regressor exceeded, with mean errors of 9.2902.

Highlights

Contrary to different methods of forecasting visitors for different designs, such as national tourism, as well as the demand for hotel rooms for accommodation in the literature, restaurant managers have little idea about the number of possible visitors in the future making use of big data
The remainder of this paper is set out as follows: “Related work” - includes the work that has been done in relation to and visitor prediction
Researchers have suggested various methods for forecasting the number of potential visitors based on big data

Summary

Introduction

Contrary to different methods of forecasting visitors for different designs, such as national tourism, as well as the demand for hotel rooms for accommodation (for example, [1], [2] [3] [4] [5]) in the literature, restaurant managers have little idea about the number of possible visitors in the future making use of big data. «Мейрамханаға келушілерді болжау» деректер жинағында қанша келуші бар екенін болжағымыз келеді: XGBoost, кездейсоқ орман регрессоры және нейрондық желі. Түйінді сөздер: болжам, машиналық оқыту, үлкен деректер, бақыланатын оқыту, деректер жиынтығы, XGBoost, кездейсоқ орман регрессоры, нейрондық желі. Три различных метода машинного обучения были применены к реальному набору данных из контролируемого обучения, мы хотим предсказать, сколько посетителей в наборе данных «Прогнозирование посетителей ресторана»: XGBoost, регрессор случайного леса и нейронная сеть.

Results

Conclusion