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        "author": "Xuhao(Peter) Du",
        "author_email": "duxuhao88@gmail.com",
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        "classifiers": [
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            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation",
            "Topic :: Software Development :: Libraries"
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        "description": "MLFeatureSelection\n==================\n\n|License: MIT| |PyPI version|\n\nGeneral features selection based on certain machine learning algorithm\nand evaluation methods\n\n**Divesity, Flexible and Easy to use**\n\nMore features selection method will be included in the future!\n\nQuick Installation\n------------------\n\n.. code:: python\n\n    pip3 install MLFeatureSelection\n\nModulus in version 0.0.9.5\n--------------------------\n\n-  Modulus for selecting features based on greedy algorithm (from\n   MLFeatureSelection import sequence\\_selection)\n\n-  Modulus for removing features based on features importance (from\n   MLFeatureSelection import importance\\_selection)\n\n-  Modulus for removing features based on correlation coefficient (from\n   MLFeatureSelection import coherence\\_selection)\n\n-  Modulus for reading the features combination from log file (from\n   MLFeatureSelection.tools import readlog)\n\nModulus Usage\n-------------\n\n`Example <https://github.com/duxuhao/Feature-Selection/blob/master/Example>`__\n\nThis features selection method achieved\n---------------------------------------\n\n-  **1st** in Rong360\n\n-- https://github.com/duxuhao/rong360-season2\n\n-  **6th** in JData-2018\n\n-- https://github.com/duxuhao/JData-2018\n\n-  **12nd** in IJCAI-2018 1st round\n\n-- https://github.com/duxuhao/IJCAI-2018-2\n\nDEMO\n----\n\nMore examples are added in example folder include:\n\n-  Demo contain all modulus can be found here\n   (`demo <https://github.com/duxuhao/Feature-Selection/blob/master/Demo.py>`__)\n\n-  Simple Titanic with 5-fold validation and evaluated by accuracy\n   (`demo <https://github.com/duxuhao/Feature-Selection/tree/master/example/titanic>`__)\n\n-  Demo for S1, S2 score improvement in JData 2018 predict purchase time\n   competition\n   (`demo <https://github.com/duxuhao/Feature-Selection/tree/master/example/JData2018>`__)\n\n-  Demo for IJCAI 2018 CTR prediction\n   (`demo <https://github.com/duxuhao/Feature-Selection/tree/master/example/IJCAI-2018>`__)\n\nFunction Parameters\n-------------------\n\n`Parameters <https://github.com/duxuhao/Feature-Selection/blob/master/MLFeatureSelection>`__\n\nAlgorithm details\n-----------------\n\n`Details <https://github.com/duxuhao/Feature-Selection/blob/master/Algorithms_Graphs>`__\n\n.. |License: MIT| image:: https://img.shields.io/badge/License-MIT-yellow.svg\n   :target: https://opensource.org/licenses/MIT\n.. |PyPI version| image:: https://badge.fury.io/py/MLFeatureSelection.svg\n   :target: https://pypi.org/project/MLFeatureSelection/",
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