Restaurant Revenue Prediction Using Machine Learning: A Comparative Study of Multiple Linear Regression and Random Forest Models

Yunmiao Wu

2024

Abstract

As the food delivery industry develops, the restaurant sector is becoming increasingly competitive. To help restaurant owners have a brief estimation on whether their restaurant can be profitable or not, it is crucial to accurately predict restaurant revenue. In this paper, Multiple linear regression and random forest methods will be used to predict restaurant sales. First, some brief data preprocessing will be made, and then the correlation p-value will be calculated to select the numerical variables that are correlated with restaurant sales. Then, multiple linear regression model will be used to evaluate the linear relationships between these features and the target variable. The random forest model will also be applied. The result shows that the random forest method outperforms the multiple linear regression method in terms of prediction accuracy when the feature has a non-linear relationship with the restaurant revenue. This research could help the restaurant owners to make their business strategies.

Paper Citation

in Harvard Style

Wu Y. (2024). Restaurant Revenue Prediction Using Machine Learning: A Comparative Study of Multiple Linear Regression and Random Forest Models. In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI; ISBN 978-989-758-726-9, SciTePress, pages 299-306. DOI: 10.5220/0013215500004568

in Bibtex Style

@conference{ecai24,
author={Yunmiao Wu},
title={Restaurant Revenue Prediction Using Machine Learning: A Comparative Study of Multiple Linear Regression and Random Forest Models},
booktitle={Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI},
year={2024},
pages={299-306},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013215500004568},
isbn={978-989-758-726-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI
TI - Restaurant Revenue Prediction Using Machine Learning: A Comparative Study of Multiple Linear Regression and Random Forest Models
SN - 978-989-758-726-9
AU - Wu Y.
PY - 2024
SP - 299
EP - 306
DO - 10.5220/0013215500004568
PB - SciTePress