A book recommendation system based on popularity, correlation, and collaborative filtering.
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Updated
Apr 17, 2023 - Jupyter Notebook
A book recommendation system based on popularity, correlation, and collaborative filtering.
This is an end to end book recommendation system.
Book recommender api written in flask framework
Stuff of flask project
Book recommendation system using user base collaborative filter Algorithm and testing the accuracy result by comparing with different algorithms
Used Nearest Neighbours to create a book recommendation model
Book Recommendation System - Unsupervised
Welcome to my Book-Recommender-System!
Book Recommender system
NextRead is a book recommender system created specifically for book readers. It allows a user to get personalised recommendation with a user-friendly interface. This is my final year project.
Welcome to our Book Recommendation App Clone! This project brings the magic of personalized book recommendations to your fingertips. Discover your next favorite read with our user-friendly interface and recommendation algorithms. Start exploring the world of literature today!
Content-based book recommendation system
BOOK_Recommendation_Using_KNN
In this project we used a k-nearest neighbors algorithm (KNN) to recommend a book based on your previous book prefrecnces.
BOOKresource a place to get friends with books
This project uses machine learning to create a personalized bookrecommendation system. By combining collaborative filtering and content-based filtering, it analyzes user preferences and book attributes to suggest tailored book recommendations. The system offers real-time updates and accurate predictions to enhance the user experience.
NextRead is a book recommender system built for Book Lovers. Simply enter your current favourite book and get peronalized book list to find your new favourite.
This project is Book recommendation chatbot which we built by the help of IBM service , basically this project is all how we built conversational chatbot using IBM assistant that Recommends books on user Preference such as genre, author or mood.
In this challenge I had to create a book recommendation algorithm using K-Nearest Neighbours. I used the Book-Crossings dataset, which contains 1.1 million ratings (scale of 1-10) of 270,000 books by 90,000 users.
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