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        "author": "Abdullah Selek",
        "author_email": "abdullahselek@gmail.com",
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            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
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        "description": "|logo| koolsla\n==============\n\n.. |logo| image:: https://images1-focus-opensocial.googleusercontent.com/gadgets/proxy?url=https://raw.githubusercontent.com/abdullahselek/koolsla/master/resources/logo.png&container=focus&resize_w=20&resize_h=20\n\n.. image:: https://img.shields.io/pypi/v/koolsla.svg\n    :target: https://pypi.python.org/pypi/koolsla/\n\n.. image:: https://img.shields.io/pypi/pyversions/koolsla.svg\n    :target: https://pypi.org/project/koolsla\n\n.. image:: https://readthedocs.org/projects/koolsla/badge/?version=latest\n    :target: http://koolsla.readthedocs.org/en/latest/?badge=latest\n\n.. image:: https://codecov.io/gh/abdullahselek/koolsla/branch/master/graph/badge.svg\n    :target: https://codecov.io/gh/abdullahselek/koolsla\n\n+--------------------------------------------------------------------------+------------------------------------------------------------------------------------+\n|                                Linux                                     |                                       Windows                                      |\n+==========================================================================+====================================================================================+\n| .. image:: https://travis-ci.org/abdullahselek/koolsla.svg?branch=master | .. image:: https://ci.appveyor.com/api/projects/status/l5bt8yw7n35cvsov?svg=true   |\n|   :target: https://travis-ci.org/abdullahselek/koolsla                   |    :target: https://ci.appveyor.com/project/abdullahselek/koolsla                  |\n+--------------------------------------------------------------------------+------------------------------------------------------------------------------------+\n\nDescription\n===========\n\nkoolsla (`Coleslaw <https://en.wikipedia.org/wiki/Coleslaw>`_) is a recommendation tool based on Machine Learning with contents.\nDeveloped with the power of `tf-idf <https://en.wikipedia.org/wiki/Tf%E2%80%93idf>`_ and `Cosine Similarity <https://en.wikipedia.org/wiki/Cosine_similarity>`_.\n\nThe user gives a natural number that corresponds to the ID of a unique dish name. Through `tf-idf` the plot summaries of 424508 different dishes that reside in the dataset, are analyzed and vectorized. \nSet of dishes (number set by user) is chosen as recommendations based on their `cosine similarity` with the vectorized input.\n\nkoolsla is mainly an educational project.\n\nInstallation\n============\n\nYou can install koolsla using::\n\n    $ pip install koolsla\n\nGetting the code\n================\n\nThe code is hosted at https://github.com/abdullahselek/koolsla\n\nCheck out the latest development version anonymously with::\n\n    $ git clone git://github.com/abdullahselek/koolsla.git\n    $ cd koolsla\n\nTo install test dependencies, run either::\n\n    $ pip install -Ur requirements.testing.txt\n\nRunning Tests\n=============\n\nThe test suite can be run against a single Python version which requires ``pip install pytest`` and optionally ``pip install pytest-cov``\n(these are included if you have installed dependencies from ``requirements.testing.txt``)\n\nTo run the unit tests with a single Python version::\n\n    $ py.test -v\n\nTo also run code coverage::\n\n    $ py.test --cov=koolsla\n\nTo run the unit tests against a set of Python versions::\n\n    $ tox\n\nSample Usage\n============\n\nImport recommender::\n\n    from koolsla import recommender\n\nGetting recommendations with dish id and recommendation count::\n\n    // Returns dictionary of tuples [(dish_id_1, similarity_ratio1), (dish_id_2, similarity_ratio2), (dish_id_3, similarity_ratio3)]\n    recommendatons = recommender.recommend(82, 3)\n\nCLI\n===\n\nAfter getting the code from https://github.com/abdullahselek/koolsla, run command::\n\n    $ pip install -r requirements.txt\n\nAnd it's ready to use, there is detailed help menu which you can follow. One of the most used function for recommendation::\n\n    $ python koolsla.py -d 25 --recommend 3\n\nFor the help menu::\n\n    $ python koolsla.py --help\n\n\n",
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        "license": "MIT License",
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