Theoretical Evaluation of Machine Learning Approaches For Hotel Recommendation

This project is to enhance the effectiveness of online hotel recommendation systems in the tourism industry. Through a comprehensive literature analysis and the exploration of advanced recommendation algorithms such as SVM, Naïve Bayes, and KNN, the project aims to address the challenges associated with recommending hotels based on crowdsourced data. Ultimately, the goal is to develop a model that empowers users to make informed hotel choices aligned with their preferences by leveraging customer feedback and improving the accuracy of hotel predictions.