BookRecommenderSuite-MachineLearning-FullStack

This is a Book Recommendation Suite that recommends a book based on the comments/reviews given by the other users, not number of stars, but textual understanding decides the "likability" of a particular book and then matching with the user's liking.

Installation

pip install nltk
pip install django==1.10.5

For setting up the settings.py file, see this page

For the full list of settings and their values, see this page

Execution

django manage.py runserver

Read more from here to perform the initial database migrations

Some simple things you can do with NLTK

>>> import nltk
>>> sentence = """At eight o'clock on Thursday morning
... Arthur didn't feel very good."""
>>> tokens = nltk.word_tokenize(sentence)
>>> tokens
['At', 'eight', "o'clock", 'on', 'Thursday', 'morning',
'Arthur', 'did', "n't", 'feel', 'very', 'good', '.']
>>> tagged = nltk.pos_tag(tokens)
>>> tagged[0:6]
[('At', 'IN'), ('eight', 'CD'), ("o'clock", 'JJ'), ('on', 'IN'),
('Thursday', 'NNP'), ('morning', 'NN')]

For more information, see this page

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

More Information

This project was made as a part of my Bachelor Thesis, in a group of three.