DYNAMIC TOURIST DESTINATION SUGGESTIONS THROUGH DECISION TREE-BASED DATA ANALYSIS

Abstract

Tourist recommendation systems play a crucial role in enhancing travelers' experiences by offering personalized suggestions based on individual preferences and historical data. This paper presents a dynamic tourist destination suggestion system utilizing the decision tree algorithm, a powerful machine learning technique, to analyze user data and generate optimal recommendations. The system integrates various factors such as location, user interests, budget, seasonality, and past travel behavior to provide real-time, context-aware suggestions for tourists.