Overview Welcome to the repository for my capstone project titled "Book Recommender System." This project focuses on building a recommendation system that suggests books to users based on their reading preferences and behavior. By leveraging machine learning algorithms and book-related data, we aim to develop a personalized book recommendation system.
Table of Contents
Introduction The world of books is vast, and readers often seek personalized recommendations to discover new titles that align with their interests. This project aims to create a book recommendation system that considers user preferences, reading history, and book attributes to provide tailored book recommendations.
Dataset The dataset used for this project includes information about books, user profiles, and user-book interactions. It contains details like book titles, genres, user ratings, and user-book interactions, making it suitable for recommendation system development.
Project Goals The primary objectives of this capstone project are:
- To preprocess and prepare the book-related data for recommendation system analysis.
- To implement and evaluate different recommendation algorithms for book suggestions.
- To develop a user-friendly interface for users to receive book recommendations.
- To provide an interactive and personalized book recommendation experience.
Methods Used
1. Data Preprocessing Data preprocessing involves handling missing values, encoding categorical features, and ensuring data quality. Additionally, we will split the data into training and testing sets for model development and evaluation.
2. Recommendation Algorithms This project explores various recommendation algorithms, including Popularity Based Filtering, Collaborative Filtering based Recommendation System--(User-User based), and Collaborative Filtering based Recommendation System--(Item-Item Based). These algorithms consider user behavior, book attributes, and user preferences to generate personalized book recommendations.
3. Evaluation Model evaluation will be based on recommendation metrics such as accuracy, precision, recall, and user satisfaction. We will assess the effectiveness of the recommendation algorithms in providing relevant and personalized book suggestions.
4. Results The results section will showcase the performance of different recommendation algorithms and their ability to provide personalized book recommendations. Users can interact with the system to receive real-time book suggestions.
Conclusion This capstone project aims to deliver a functional book recommendation system that enhances the reading experience for users. It provides a personalized approach to book discovery, helping users find books that match their tastes and interests. The conclusion section will summarize the project's outcomes and potential future enhancements.