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        "author": "Mohit Awasthi",
        "author_email": "imohitawasthi@gmail.com",
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            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: Apache Software License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
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        "description": "=======\nAITools\n=======\n\n\n.. image:: https://img.shields.io/pypi/v/aitools.svg\n        :target: https://pypi.python.org/pypi/aitools\n\n.. image:: https://img.shields.io/travis/imohitawasthi/aitools.svg\n        :target: https://travis-ci.org/imohitawasthi/aitools\n\n.. image:: https://readthedocs.org/projects/aitools/badge/?version=latest\n        :target: https://aitools.readthedocs.io/en/latest/?badge=latest\n        :alt: Documentation Status\n\n\n.. image:: https://pyup.io/repos/github/imohitawasthi/aitools/shield.svg\n     :target: https://pyup.io/repos/github/imohitawasthi/aitools/\n     :alt: Updates\n\n\n\nArtificial Intelligence Tool Kit Mark Two.\n------------------------------------------\n\n\n* Free software: Apache Software License 2.0\n* Documentation: https://aitools.readthedocs.io.\n\n\nFeatures\n--------\n\n* N-GRAM\n\n    * Predict the next word of the sentence that further can be use for predicting the entire sentence.\n    * Generation of text like a paragraph/article on Health or environment or any other topic (given relevant data).\n    * Generation of the next GOT books!!!.\n\n    * Next Phase - Use of bayesian statistics and more nice probability models for getting the best results.\n\nLook For: Demo file n_gram.py\n\n* K-MEANS\n\n    * Creates clusters and classify new nodes into those clusters.\n    * Efficient and easy to implement.\n\n    * Next Phase - Multiple algorithms implementation.\n\nLook For: Demo file k_means.py and k_means_color_cluster.py\n\n* Logistic Regression\n\n    * From probability of event happening or not to the next Data point, it is a very versatile algorithm.\n    * Simple Implementation and Training routines.\n    * Uses Gradient Descent and Sigmoid Function.\n\n    * Next Phase - Improvements in Gradient Descent is still in progress.\n\nLook For: Demo file logistic_regression.py\n\n* Naive Bayes\n\n    * Algorithm based on Bayesian statistics, which is capable of creating classifications like an intent classifier or sentiment analysis engine(A true complex one not the Pathetic Twitter thing).\n    * Created using little bit of probability and bayesian statistics with a hint of programming and with a lot of love.\n\n    * Next Phase - Flexibility and more control on different algorithm.\n\nLook For: Demo file naive_bayes.py\n\n* Decision Tree\n\n    * Algorithm based on Gini logic and info gain for prediction.\n    * Trees can be very use full but may lead to aver fitting easily Be Cautious.\n\n    * Next Phase - Flexibility and more control on different algorithm.\n\nLook For: Demo file decision_tree.py\n\n* Pre Processing\n\n    * Utils for Pre Processing Data.\n\n    * Next Phase - More Cool Functions.\n\nLook For: Demo file pre_processing.py\n\n* Others\n\n    * In build Mathematics Util.\n        * Vector Math, Probability, and some Statistic\n        * Util will be upgraded as per the need.\n    * Gradient Descent.\n        * Entire Gradient Descent algorithm combined into one.\n        * Usability Docs will be Provided Soon.\n\n* Next Updates\n\n    * Improving all algorithms and giving proper documentation on usage.\n    * Providing more user control on algorithm selection and output handling.\n    * Detailed Demo files.\n    * Sudo codes and algorithm explanations.\n    * Will be available to lower version( > 2.7).\n\n\n\nCredits\n-------\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n\n\n=======\nHistory\n=======\n\n0.1.0 (2019-04-14)\n------------------\n\n* GitHub Setup.\n* Travis CI Build Added.\n* Documentation skeleton added on ReadTheDocs.\n* First release on PyPI.\n\n0.1.0 (2019-04-21)\n------------------\n\n* N-Gram algorithm version one completed.\n* First level of documentation.\n* N-Gram demo files added.\n\n0.1.0 (2019-04-25)\n------------------\n\n* Gradient Descent Created.\n* Word Stemmer Added(Still needs improvements and not been used through out the project).\n\n0.1.0 (2019-04-28)\n------------------\n\n* Logistic Regression Added.\n* Demo file added.\n\n0.1.2 (2019-05-01)\n------------------\n\n* Naive Bayes Created.\n\n\n0.1.2 (2019-05-01)\n------------------\n\n* Pre Processing Added.\n\n\n0.1.2 (2019-05-01)\n------------------\n\n* First Level of Documentation Created.\n\n\n0.1.2 (2019-05-27)\n------------------\n\n* Lost Track of most of the things.\n* Finally We have following\n    * K Means\n    * Logistic Regression\n    * N Gram\n    * Naive Bayes\n    * Pre Processing\n\n    * SVM in progress.\n\n\n0.1.3 (2019-06-24)\n------------------\n\n* Decision Tree Added(Gini and Impurity Method).\n* Previous algorithm improvements are in progress.\n\n\n",
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