This project presents a Content Filtering-Based Book Recommender System designed to provide personalized book suggestions based on user preferences and interaction patterns. Using memory-based and model-based content filtering techniques, the system analyzes user ratings to predict relevant recommendations. The dataset was sourced from Kaggle and processed using machine learning methods to improve recommendation accuracy and user engagement. The deployed system demonstrates the practical application of recommender systems in delivering personalized content and enhancing user experience.