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A seven-year analysis of the tourism sector of India with a prediction model—A case study of smart tourism
Vol 5, Issue 1, 2024
VIEWS - 58 (Abstract)
Abstract
The tourism sector requires in-depth analysis and forecasting to provide a clear picture of various factors that affect the visits of foreign tourists to certain countries. In this context, the work carried out in this paper provides an in-depth analysis of the number of tourists to India and the revenue generated from them from the years 2014 to 2020. Furthermore, the analysis of the different states of India to which the tourists visited the most and the quarterly analysis of the tourists to India are also presented. The impact of the corona pandemic on the tourism sector of India is also shown by comparing the number of tourists in 2019 and 2020. Support vector regression (SVR) is trained with historical data on the number of tourists from 2001 to 2016 and validated for 2017 to 2019. This trained model is used to forecast the number of tourists from 2020 to 2023 to study the impact of corona pandemic on the number of foreign tourists to India. Similarly, historical data on foreign exchange fees from 2001 to 2016 is used to train the model, which is validated with data from 2017 to 2019. This train model is used to predict the Foreign Exchange Earning (FEE) for the years 2020 to 2022. The actual FEE is compared with the predicted FEE to show the impact of the coronavirus pandemic on the revenue generated from tourism in India.
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Prof. Hung-Che Wu
Nanfang College Guangzhou, China
