Machine Learning based Forecasting Systems for Worldwide International Tourists Arrival

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.