{'fraction_non_alphanumeric': 0.022453889334402566, 'fraction_numerical': 0.01764234161988773, 'mean_word_length': 4.571428571428571, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Predicting Growth in Tourism Industry Using Machine Learning Methods

Tourism industry is a significant industries in the world. It increases the revenue of the economy by creating thousands of jobs and developing the infrastructures of a country. With the normalcy returning in the lives of people around the world after the brutal waves of COVID19, it might be a high time people would be looking forward to visiting new places. Many types of research have been done in the direction of the use of machine learning to analyze the sentiments in the field of the tourism industry while very little research has been done on using machine learning in predicting the growth of the tourism industry. Thus, the present research has tried to explore the area of predicting the growth of the tourism industry using machine learning. The growth has been estimated by using footfall as the parameter. The research has applied four models namely Random Forest, Linear Regression, Gradient Boosting Machine (GBM), and Decision Tree. R-square along with RMSE, MAPE, and MAE has been used as the metrics for assessing the best fit model. GBM comes out on top in every category with R-square value of 96.84, MAPE of 15.96%, RMSE of 6017.74, and MAE of 4780.32.