• Dipali Koshti
• Supriya Kamoji
• Scarlet Lopes

Abstract: In today’s world, travel and tourism is one of the major sources of foreign exchange for both developed and developing economies. According to the information given in statista.com, approximate contribution of this industry in the world’s GDP is 8 to 10 percent. Therefore, increasing the revenue generated from tourism in countries has become a serious concerned for most of the countries, especially for developing countries like India. The proposed system predicts the tourist arrival in India from 56 different countries. Given the name of the county, the year and the month, it predicts percentage of tourist expected to arrive in India from the given country. With the current advancement in the neural networks, Deep learning has proven its results for vast number of applications. We have compared two models namely Support Vector Regression and Long Short Term Memory for predicting tourism flow in India. Our experimental results show that LSTM model performs better than SVR model for time series data.

Tourism prediction, Long Short Term Memory, Support Vector Regression, Deep learning Model

"PREDICTION OF TOURISM FLOW IN INDIA USING LSTM MODEL", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.190-195, May-2019, Available :http://www.jetir.org/papers/JETIR1905A34.pdf

"PREDICTION OF TOURISM FLOW IN INDIA USING LSTM MODEL", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp190-195, May-2019, Available at : http://www.jetir.org/papers/JETIR1905A34.pdf