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    "info": {
        "author": "Edward R Jones",
        "author_email": "ejones@tamu.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Topic :: Utilities"
        ],
        "description": "AdvancedAnalytics\n===================\n\nA collection of python modules, classes and methods for simplifying the use of machine learning solutions.  **AdvancedAnalytics** provides easy access to advanced tools in **Sci-Learn**, **NLTK** and other machine learning packages.  **AdvancedAnalytics** was developed to simplify learning python from the book *The Art and Science of Data Analytics*.\n\nDescription\n===========\n\nFrom a high level view, building machine learning applications typically proceeds through three stages:\n\n    1. Data Preprocessing\n    2. Modeling or Analytics\n    3. Postprocessing\n\nThe classes and methods in **AdvancedAnalytics** primarily support the first and last stages of machine learning applications. \n\nData scientists report they spend 80% of their total effort in first and last stages. The first stage, *data preprocessing*, is concerned with preparing the data for analysis.  This includes:\n\n    1. identifying and correcting outliers, \n    2. imputing missing values, and \n    3. encoding data. \n\nThe last stage, *solution postprocessing*, involves developing graphic summaries of the solution, and metrics for evaluating the quality of the solution.\n\nDocumentation and Examples\n============================\n\nThe API and documentation for all classes and examples are available at https://github.com/tandonneur/AdvancedAnalytics/. \n\nUsage\n=====\n\nCurrently the most popular usage is for supporting solutions developed using these advanced machine learning packages:\n\n    * Sci-Learn\n    * StatsModels\n    * NLTK\n\nThe intention is to expand this list to other packages.  This is a simple example for linear regression that uses the data map structure to preprocess data:\n\n.. code-block:: python\n\n    from AdvancedAnalytics.ReplaceImputeEncode import DT\n    from AdvancedAnalytics.ReplaceImputeEncode import ReplaceImputeEncode\n    from AdvancedAnalytics.Tree import tree_regressor\n    from sklearn.tree import DecisionTreeRegressor, export_graphviz \n    # Data Map Using DT, Data Types\n    data_map = {\n        \u201cSalary\u201d:         [DT.Interval, (20000.0, 2000000.0)],\n        \u201cDepartment\u201d:     [DT.Nominal, (\u201cHR\u201d, \u201cSales\u201d, \u201cMarketing\u201d)] \n        \u201cClassification\u201d: [DT.Nominal, (1, 2, 3, 4, 5)]\n        \u201cYears\u201d:          [DT.Interval, (18, 60)] }\n    # Preprocess data from data frame df\n    rie = ReplaceImputeEncode(data_map=data_map, interval_scaling=None,\n                              nominal_encoding= \u201cSAS\u201d, drop=True)\n    encoded_df = rie.fit_transform(df)\n    y = encoded_df[\u201cSalary\u201d]\n    X = encoded_df.drop(\u201cSalary\u201d, axis=1)\n    dt = DecisionTreeRegressor(criterion= \u201cgini\u201d, max_depth=4\n                                min_samples_split=5, min_samples_leaf5)\n    dt = dt.fit(X,y)\n    tree_regressor.display_importance(dt, encoded_df.columns)\n    tree_regressor.display_metrics(dt, X, y)\n\nCurrent Modules and Classes\n=============================\n\nReplaceImputeEncode\n    Classes for Data Preprocessing\n        * DT defines new data types used in the data dictionary\n        * ReplaceImputeEncode a class for data preprocessing\n\nRegression\n    Classes for Linear and Logistic Regression\n        * linreg support for linear regressino\n        * logreg support for logistic regression\n        * stepwise a variable selection class\n\nTree\n    Classes for Decision Tree Solutions\n        * tree_regressor support for regressor decision trees\n        * tree_classifier support for classification decision trees\n\nForest\n    Classes for Random Forests\n        * forest_regressor support for regressor random forests\n        * forest_classifier support for classification random forests\n\nNeuralNetwork\n    Classes for Neural Networks\n        * nn_regressor support for regressor neural networks\n        * nn_classifier support for classification neural networks\n\nText\n    Classes for Text Analytics\n        * text_analysis support for topic analysis\n        * text_plot for word clouds\n        * sentiment_analysis support for sentiment analysis\n\nInternet\n    Classes for Internet Applications\n        * scrape support for web scrapping\n        * metrics a class for solution metrics\n\nInstallation and Dependencies\n=============================\n\n**AdvancedAnalytics** is designed to work on any operating system running python 3.  It can be installed using **pip** or **conda**.\n\n.. code-block:: python\n\n    pip install AdvancedAnalytics\n    # or\n    conda install -c dr.jones AdvancedAnalytics\n\nGeneral Dependencies\n    There are dependencies.  Most classes import one or more modules from    \n    **Sci-Learn**, referenced as *sklearn* in module imports, and \n    **StatsModels**.  These are both installed with the current version\n    of **anaconda**.\n\nInstalled with AdvancedAnalytics\n    Most packages used by **AdvancedAnalytics** are automatically \n    installed with its installation.  These consist of the following \n    packages.\n\n        * statsmodels\n        * scikit-learn\n        * scikit-image\n        * nltk\n        * pydotplus\n        * python-graphviz\n        * wordcloud\n        * newspaper3k\n\nOther Dependencies\n    The *Tree* and *Forest* modules plot decision trees and importance\n    metrics using **pydotplus** and the **graphviz** packages.  These\n    should also be automatically installed with **AdvancedAnalytics**.\n\n    However, the **graphviz** install is sometimes not fully complete \n    with the conda install.  It may require an additional pip install.\n\n    .. code-block:: python\n\n        pip install graphviz\n\nText Analytics Dependencies\n    The *TextAnalytics* module uses the **NLTK**, **Sci-Learn**, and \n    **wordcloud** packages.  Usually these are also automatically \n    installed automatically with **AdvancedAnalytics**.  You can verify \n    they are installed using the following commands.\n\n    .. code-block:: python\n\n        conda list nltk\n        conda list sci-learn\n        conda list wordcloud\n\n    However, when the **NLTK** package is installed, it does not \n    install the data used by the package.  In order to load the\n    **NLTK** data run the following code once before using the \n    *TextAnalytics* module.\n\n    .. code-block:: python\n\n        #The following NLTK commands should be run once\n        nltk.download(\u201cpunkt\u201d)\n        nltk.download(\u201caveraged_preceptron_tagger\u201d)\n        nltk.download(\u201cstopwords\u201d)\n        nltk.download(\u201cwordnet\u201d)\n\n    The **wordcloud** package also uses a little know package\n    **tinysegmenter** version 0.3.  Run the following code to ensure\n    it is installed.\n\n    .. code-block:: python\n\n        conda install -c conda-forge tinysegmenter==0.3\n        # or\n        pip install tinysegmenter==0.3\n\nInternet Dependencies\n    The *Internet* module contains a class *scrape* which has some   \n    functions for scraping newsfeeds. Some of these use the \n    **newspaper3k** package.  It should be automatically installed with \n    **AdvancedAnalytics**.\n\n    However, it also uses the package **newsapi-python**, which is not \n    automatically installed.  If you intended to use this news scraping\n    scraping tool, it is necessary to install the package using the \n    following code:\n\n    .. code-block:: python\n\n        conda install -c conda-forge newsapi\n        # or\n        pip install newsapi\n\n    In addition, the newsapi service is sponsored by a commercial company\n    www.newsapi.com.  You will need to register with them to obtain an \n    *API* key required to access this service.  This is free of charge \n    for developers, but there is a fee if *newsapi* is used to broadcast \n    news with an application or at a website.\n\nCode of Conduct\n---------------\n\nEveryone interacting in the AdvancedAnalytics project's codebases, issue trackers, chat rooms, and mailing lists is expected to follow the PyPA Code of Conduct: https://www.pypa.io/en/latest/code-of-conduct/ .\n\n\n\n\n",
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