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DOI: 10.14569/IJACSA.2021.0121107
PDF

Machine Learning based Forecasting Systems for Worldwide International Tourists Arrival

Author 1: Ram Krishn Mishra
Author 2: Siddhaling Urolagin
Author 3: J. Angel Arul Jothi
Author 4: Nishad Nawaz
Author 5: Haywantee Ramkissoon

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 11, 2021.

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Abstract: The international tourist movement has overgrown in recent decades, and travelers are considered a significant source of income to the tourism economy. When tourists visit a place, they spend considerable money on their enjoyment, travel, and hotel accommodations. In this research, tourist data from 2010 to 2020 have been extracted and extended with depth analysis of different dimensions to identify valuable features. This research attempts to use machine learning regression techniques such as Support Vector Regression (SVR) and Random Forest Regression (RFR) to forecast and predict worldwide international tourist arrivals and achieved forecasting accuracy using SVR is 99.4% and using RFR is 84.7%. The study also analyzed the forecasting deadlock condition after covid-19 in the sudden drop of international visitors due to lockdown enforcement by all countries.

Keywords: Tourists; forecasting; machine learning; Covid-19

Ram Krishn Mishra, Siddhaling Urolagin, J. Angel Arul Jothi, Nishad Nawaz and Haywantee Ramkissoon, “Machine Learning based Forecasting Systems for Worldwide International Tourists Arrival” International Journal of Advanced Computer Science and Applications(IJACSA), 12(11), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0121107

@article{Mishra2021,
title = {Machine Learning based Forecasting Systems for Worldwide International Tourists Arrival},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0121107},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0121107},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {11},
author = {Ram Krishn Mishra and Siddhaling Urolagin and J. Angel Arul Jothi and Nishad Nawaz and Haywantee Ramkissoon}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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