Prediction of Top Tourist Attraction Spots using Learning Algorithms
Sagar Gupta1, Jenila Livingston L.M.2, Agnel Livingston L.G.X.3

1Sagar Gupta, M. Tech, School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
2Jenila Livingston L.M., Associate Professor, School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
3Agnel Livingston L.G.X., Assistant Professor, St. Xavier’s Catholic College of Engineering, Chunkankadai, Nagercoil, Kanyakumari District, Tamil Nadu, India.

Manuscript received on 11 August 2019. | Revised Manuscript received on 17 August 2019. | Manuscript published on 30 September 2019. | PP: 1063-1067 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4241098319/19©BEIESP | DOI: 10.35940/ijrte.C4241.098319
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Dealing with the growing amount of user posted content like preferences, responses, comments, past experiences and beliefs spread through social media is a vital but challenging task. Being applied in several domains, recommender systems are used to find solutions and suggestions based on users interests including tourism-related opinion detection and tourist-attraction spot identification. Tourists can access and analyze this information for making decisions and predicting best tourist places. This study aims to predict tourist attraction spots and their related information by analyzing the data from social media (Facebook, Twitter etc.) which in turns help the tourist industry by deliberating what kind of attractions tourists can have and how to obtain their preferences. For this purpose four algorithms such as Kernel Density Estimation, K- Nearest Neighbor, Random forest and XG Boost have been used. The findings revealed that XG Boost yields better results in terms of accuracy than other three algorithms.
Index Terms: Tourism, Social media, Random Forest, Kernel Density Estimation, K-Nearest Neighbour, XG Boost, Classification algorithm
Scope of the Article:
VLSI Algorithms