Abstract
The paper highlights the process of predicting how popular a particular tourist destination would be for a given set of features in an English Wikipedia corpus based on different places around the world. Intelligent predictions about the possible popularity of a tourist location will be very helpful for personal and commercial purposes. To predict the demand for the site, rating score on a range of 1–5 is a proper measure of the popularity of a particular location which is quantifiable and can use in mathematical algorithms for appropriate prediction. We compare the performance of different machine learning algorithms such as Decision Tree Regression, Linear Regression, Random Forest and Support Vector Machine and maximum accuracy (74.58%) obtained in both the case of Random Forest and Support Vector Machine.
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Thanks to all the anonymous reviewer for extensive and helpful comments.
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Jamatia, A., Baidya, U., Paul, S., DebBarma, S., Dey, S. (2020). Rating Prediction of Tourist Destinations Based on Supervised Machine Learning Algorithms. In: Behera, H., Nayak, J., Naik, B., Pelusi, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 990. Springer, Singapore. https://doi.org/10.1007/978-981-13-8676-3_11
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DOI: https://doi.org/10.1007/978-981-13-8676-3_11
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