A seven-year analysis of the tourism sector of India with a prediction model—A case study of smart tourism

Abstract

<p>The tourism sector requires an in-depth analysis and forecasting to bring a clear picture of various factors that affect the visit 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 year 2014 to 2020. Furthermore, the analysis of different states of India to which the tourists visited the most and quarterly analysis of the tourists to India are also presented. The impact of 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 the historical data of 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, the historical data of foreign exchange fees from 2001 to 2016 is used to train the model while it 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 by India.<strong></strong></p>