Recommendation systems are increasingly being used to help users navigate the vast amount of information available on the internet. Book recommendation systems, in particular, can help readers find new books and authors that they are likely to enjoy. This paper presents a content-based book recommendation system that uses ratings data to generate personalized recommendations for users. The system utilizes a machine learning algorithm to compare the characteristics of the books that the user has liked to the characteristics of other books, in order to find similar books to recommend. The results of the evaluation demonstrate that the system is effective at providing relevant recommendations to users and improving the overall user experience. By incorporating ratings data, the system is able to provide more personalized recommendations and help users discover new books that align with their interests.