A book recommendation system for recommending books based on user search.
You can download the data from here
BX-Book-Ratings.csv
- User-ID (the user who have provided the rating)
- ISBN (the ISBN of the book rated by the user)
- Book-rating (rating from 0 to 10)
BX-Books.csv
- ISBN (the ISBN of the book)
- Book-Title (the title of the book)
- Book-Author (the author of the book)
- Year-Of-Publication (the year in which the book was published)
- Publisher (publisher of the book)
- Image-URL-S (URL for a small-sized image)
- Image-URL-M (URL for a medium-sized image)
- Image-URL-L (URL for a large-sized image)
BX-Users.csv
- User-ID (the user)
- Location (the address of the user)
- Age (age of the user)
The algorithm used is KNN machine learning algorith Which is actually an algorithm which look up for its Nearest neighbours.
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You can clone or download the project directly from github.
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Once you downloaded the project, you need the dataset which you can download from here.
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After downloading the dataset, put it in the project directory.
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Open up project in VSCode.
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Fire up vscode integrated terminal.
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Run: $ pipenv install --ignore-pipfile to create the same virtual environment on your machine as needed. You might need to install pipenv globally first by running $ pip install pipenv.
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Vscode will detect the virtual environment and activate it. You might need to reload it once.
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Once the virtual environment is activated, run $ flask run.
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Your application will be start serving at port 5000.
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At home page, you will see some random books and a search box.
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Search a book and you below that book, you will see the recommended books based on publication year.