Published 2026-05-27
Keywords
- Expense Forecasting on Tourism,
- Machine Learning,
- Random Forest Classifier
How to Cite
Copyright (c) 2026 Swami Vivekananda University Press
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Abstract
Tourism is a major economic activity that contributes to the growth of many countries. However, the tourism industry is also a major expense for businesses and governments. In order to manage these expenses effectively, it is important to be able to forecast them accurately. This paper proposes a machine learning model for expense forecasting in tourism using a random forest classifier. The model is trained on a dataset of historical tourism expenses and other relevant factors, such as the number of tourists, the average spending per tourist, and the cost of living in the destination country.
The results of the study show that the random forest classifier is able to achieve a high accuracy in forecasting tourism expenses.
The model can be used by businesses and governments to better manage their tourism expenses and make more informed decisions about the future of the tourism industry.
DOI - https://doi.org/10.65525/SVUP.9788199565418.2026.182-193