Travel Mate: A Travel Destination Safety Recommender during the COVID-19 pandemic using Machine Learning

Abstract

This thesis contains of research on the travel recommendation system amid the pandemic using Machine Learning (ML). Since the breakout of the COVID-19 pandemic in Wuhan, China, which was discovered at the end of 2019, the travel and tourist industry has been severely hit. As the pandemic subsided, individuals in various nations and regions began to return to regional travel. People travel for a variety of reasons, but they are primarily divided into two categories: leisure and business travel. However, everything has now come to an end and the overseas travel has been resumed. This project proposes a for recommending trip destinations based on the amount of COVID-19 positive instances. The author created a machine learning model to propose places based on the accuracy of all the methods. In combination with the significant performance gap, the algorithms with the best accuracy will be picked.