Tourism Data Exploration: Analysis and Visualization for Impactful Insights
Vaishnavi C 1, Shruthi V 2, Ruthika S Shetty 3, Sreelatha PK 4
1 Department of Computer Science and Engineering, Presidency University, Bengaluru, India
2 Department of Computer Science and Engineering, Presidency University, Bengaluru, India
3 Department of Computer Science and Engineering, Presidency University, Bengaluru, India
4 Assistant Professor, Department of Computer Science and Engineering, Presidency University, Bengaluru, India
Abstract: Tourism happens to be a dynamic and fast-evolving sector, which is largely significant to economic development and cultural exchange. The proposal aims at improving the Indian tourism industry with an application of Artificial Intelligence and Machine Learning (AIML) techniques, providing insights and solutions to the industry through an analysis of varied datasets concerning Indian tourism-clustering, predictive modelling, and trend analysis to find useful patterns and insights within travel behaviour, regional performance, and socio-economic impacts.
Clustering models assign clusters of tourist spots on aspects such as geographic characteristics, popularity, and preferences of visitors to create possible configurations of travel options. Predictive models predict the travel patterns with which tourism authorities plan their domestic circuit tours and better allocation of resources. This proposal will specify seasonal trends through historical and demographic data analyses and preferences that are region-specific to enable stakeholders to promote sustainable tourism practices.
Advanced algorithms like K-Means clustering include data pre-processing, exploratory analysis, and generation of interactive data visualizations to drive insightful decision-making. Such results include identification of highly-rated landmarks, overcrowded tourist destinations, and performance statistics on tourism for regions, which are necessary for the right managing of the marketing strategy, planning of infrastructure, and optimization of resources.
This proposal builds a comprehensive framework with AI/ML-driven technique-based approaches alongside holistic datasets toward thinking about and acting in the complex world of the tourism ecosystem, which also points to the transformational power of data-driven decision-making toward a sustainable future.
Keywords—Tourism; Clustering; Predictive Modelling; Sustainable Tourism; K-Means; Travel Behaviour; Demographic Analysis; Trend Analysis; Data Visualization; Regional Tourism Performance; Resource Optimization; Covid-19.
