Abstract
International Tourism has been a very important contributor to a country's economic development. In developing countries like Argentina, Brazil, India etc., the tourism industry plays an important role in the Gross Domestic Product and Foreign Exchange Earnings. Now a days, India has been welcoming a highly impressive number of foreign tourists from all round the globe annually. This study aims at analysing the distribution and trend of foreign tourists visiting India, among the four quarters of a year, given different configurations of Gross Domestic Product and Foreign Exchange Earnings using Machine Learning and Regression Analysis. A 4 headed Machine Learning Model has been constructed and trained independently for the purpose. Finally, the results of the four individually trained sub models are collected together for Trend Anal ysis and Distribution Analysis. This final evaluation is done for the year 2012 post to Model Construction, Training, Tuning and Individual Validations of the 4 sub models. It has been found that the Distribution and Trend Analysis have been almost similar to the Original Distribution and Trends of Foreign Tourists among the four quarters of 2012. This similarity in Distribution Analysis has been shown using visualizations like Pie Chart and that in Trend Analysis has been shown using Line Plots.
References
- https://en.wikipedia.org/wiki/World_Tourism_ rankings
- Sun, Shaolong, et al. ”Forecasting tourist arrivals with machine learning and internet search index.” Tourism Management 70 (2019): 1-10.
- Ricardo, Hugo David dos Reis Barbosa. Forecasting tourism demand for Lisbon´ s region: a data mining approach. Diss. 2018.
- Claveria, Oscar, Enric Monte, and Salvador Torra. ”Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection.” arXiv preprint arXiv:1805.00878 (2018).
- Hu, Yi-Chung. ”Predicting foreign tourists for the tourism industry using soft computing-based Grey–Markov models.” Sustainability 9.7 (2017): 1228.
- Garc´ıa Rodr´ıguez, Oscar. ”Forecasting tourism arrivals with an online search engine data: A study of the Balearic Islands.” (2017).
- Petrevska, Biljana. ”Predicting tourism demand by ARIMA models.” Economic research-Ekonomska istraˇzivanja 30.1 (2017): 939-950.
- Yu, Ying, et al. ”Statistical modeling and prediction for tourism economy using dendritic neural network.” Computational intelligence and neuroscience 2017 (2017).
- Ali, Rafidah, and Ani Shabri. ”Modelling Singapore Tourist Arrivals to Malaysia by Using SVM and ANN.” (2017).
- Noersasongko, Edi, et al. ”A tourism arrival forecasting using genetic algorithm based neural network.” Indian Journal of Science and Technology 9.4 (2016).
- Cankurt, S., and A. Subasi. ”Developing tourism demand forecasting models using machine learning techniques with trend, seasonal, and cyclic components.” Balkan Journal of Electrical and Computer Engineering 3.1 (2015).
- Neupane, Hari Sharma, Chandra Lal Shrestha, and Tara Prasad Upadhyaya. ”Modelling monthly international tourist arrivals and its risk in Nepal.” NRB Economic Review 24.1 (2012): 28-47. 13. https://www.kaggle.com/navoneel/fta-data nces