A Book Recommendation System which recommends the users a selection of books based on their interests. It also has a separate page for the list of all books.
Data used for this project was taken from here.
The dataset consists of three tables; Books, Users, and Ratings. Data from all three tables are cleaned and preprocessed separately.
We have sorted the dataset according to the total ratings each of the books have received in non-increasing order and then recommended top 50 books.
This system recommends books by calculating similarities in Book Titles. For this, TF-IDF feature vectors were created for unigrams and bigrams of Book-Titles; only those books' data has been considered which are having at least 50 ratings.
- ipython-notebook - Python Text Editor
- sklearn - Machine learning library
- numpy, scipy- number python library
- pandas - data handling library
- HTML, CSS & Javascript was used for the frontend of the application.
Cooked with ♥ by WilsonVD