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        "author": "Iordanis Fostiropoulos",
        "author_email": "danny.fostiropoulos@gmail.com",
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            "License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)",
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            "Programming Language :: Python :: 3"
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        "description": "# SQUAAD ANALYSIS FRAMEWORK\n\n## Installation\n\n`pip install squaad`\n\n### Releases\n\n* V2.0 `https://github.com/fostiropoulos/squaad/releases/download/v2.0/squaad-2.0.tar.gz`\n\n\n### Install from Binary\n`pip install squaad-2.0.tar.gz`\n\n## Usage\n\n### Creating new database connection\n~~~~\nmyConnection=db(\"config.json\",\"cache\")\nprint(\"Connection Status: %s\"%myConnection.testConnection())\n~~~~\n\n### Config.json and Cache\n\n* Config.json follows the following format:\n~~~~\n{\"pgsql\":{\"host\":\"\",\"user\":\"\",\"passwd\":\"\",\"db\":\"\"} }\n~~~~\n* Cache folder is used to save results of the queries and uses the cache next time you execute a query.\n\n### Games-Howell Statistics Test\n\n~~~~\nstats.gamesHowellBinomial({\"GROUP1\":{True:100, False:3999}, \"GROUP2\":{True:2999,False:2939}})\n~~~~~\n\n### Classification Pipeline with KFold Usage\n\nParameters\n\n* `X` Pandas dataframe with set of data. Each column is a feature\n* `Y`  Labels for the set of data.\n* `split_columns` (Optional) **unimplemented**, columns to split by. That is columns that can have bias, we take into consideration during splitting\n* `kfolds` (Optional)  number of folds to run.\n* `classifiers` (Optional)  dictionary containing classifiers to use\n* `balancers` (Optional)  the balancers you want to run\n\n### Classifiers\n\nDefault Classifiers:\n* Nearest Neighbors\n* Linear SVM\n* RBF SVM\n* Gaussian Process\n* Decision Tree\n* Random Forest\n* Neural Net\n* AdaBoost\n* Naive Bayes\n* QDA\n\n### Balancers\n\nDefault Classifiers:\n* Unbalanced\n* SMOTE\n* SMOTEEN\n* SMOTETomek\n* RandomUnderSampler\n\n### ML Pipeline examples\n\n~~~~\nX=df[['locs_inc', 'cplxs_inc', 'smls_inc', 'vuls_inc', 'fbgs_inc', 'locs_dec', 'cplxs_dec', 'smls_dec', 'vuls_dec', 'fbgs_dec']]\nY=df['affiliation']\nmlPipeline.classificationPipeLineKfold(X,Y)\n~~~~\n\n\n",
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