Analyzing the key factors affecting travel recommendation system based on random forest approach

This thesis focuses on the development and evaluation of a travel recommendation system. The objective is to analyze the factors effecting the travel recommendation system and propose a recommendation system for the travelers in Pakistan. By leveraging advanced algorithms and user preference data, the system aims to enhance user satisfaction and streamline travel planning. The system provides personalized travel suggestions by processing user preferences using the Random Forest algorithm. The research addresses the challenges of making recommendations with limited data availability and explores how data expansion can enhance accuracy. For the role of algorithmic efficiency and data quality on system performance in travel recommendation systems, the key components of these systems are analyzed. Recommendation accuracy, user satisfaction, and overall system performance from the basis for evaluating the specified system. The thesis suggests practical implementation in real-world scenarios and provides insights into potential system improvements. Due to limited available data, the system requires further dataset expansion for more accurate recommendations. Keywords: Travel Recommendation System, Random Forest, User Satisfaction, Travel Planning, Recommendation Algorithms