A book recommendation system based on popularity, correlation, and collaborative filtering.
-
Updated
Apr 17, 2023 - Jupyter Notebook
A book recommendation system based on popularity, correlation, and collaborative filtering.
Book recommender api written in flask framework
This is an end to end book recommendation system.
Used Nearest Neighbours to create a book recommendation model
Book recommendation system using user base collaborative filter Algorithm and testing the accuracy result by comparing with different algorithms
Book Recommender system
Book Recommendation System - Unsupervised
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!
Stuff of flask project
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.
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
Built for the Nosu AI Hackathon - a AI web app that gives you book recommendations!
Python notebook for Book Recommendation System using collaborative filtering.
This is a fully fledged book shopping webapp including NLP & recommender system
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.
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.
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.
Add a description, image, and links to the bookrecommendsystem topic page so that developers can more easily learn about it.
To associate your repository with the bookrecommendsystem topic, visit your repo's landing page and select "manage topics."