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    "info": {
        "author": "Martin Skarzynski",
        "author_email": "marskar@gmail.com",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
        ],
        "description": "# The `datacamprojects` python package\n\nSkip the boilerplate of scikit-learn machine learning examples.\n\n## Installation\n```bash\npip install datacamprojects\n```\n\n## Usage\n\nIn a shell environment, you can run `datacamprojects`\nwith no arguments to perform a Logistic Regression\non the `digits` dataset.\n\nThis will produce a 10 x 10 confusion matrix\nwith the Accuracy Score at the top.\n\nYou can also pass arguments to datacamprojects at the command line.\n\nFor example, \n```bash\ndatacamprojects -dataset diabetes -model linear_model.Lasso\n# Or\ndatacamprojects -d diabetes -m linear_model.Lasso\n```\nwill run a linear regression with lasso regularization (L1)\non the `diabetes` dataset.\n\nThe `dataset` argument can be any of\nthe following built-in scikit-learn datasets:\n- Regression\n    - `boston`\n    - `diabetes`\n- Classification\n    - `digits`\n    - `iris`\n    - `wine`\n    - `breast_cancer`\n\nThe `model` argument refers to the model type and name from scikit-learn.\nThe first part is the submodule, e.g. \n- `linear_model`\n- `naive_bayes`\n- `ensemble`\n- `svm`\n\nwhile the second is what is actually imported, e.g.\n- `LinearRegression`\n- `GaussianNB`\n- `RandomForestRegressor`\n- `SVC`\n\nSimplify code to a single function call per step:\n```python\nfrom sklearn.metrics import confusion_matrix, accuracy_score\nimport datacamprojects as dcp\n\ndataset = dcp.get_data('digits')\nx_train, x_test, y_train, y_test = dcp.split_data(dataset)\n\nmodel = dcp.get_model(model_type='ensemble',\n                      model_name='RandomForestClassifier')\n\nfit = model.fit(x_train, y_train)\ndcp.pickle_model(filename='digits_rf.pickle', model=fit)\npredictions = fit.predict(x_test)\n\nconfmat = confusion_matrix(y_true=y_test, y_pred=predictions)\naccuracy = accuracy_score(y_true=y_test, y_pred=predictions)\n\ndcp.confusion_matrix_plot(cm=confmat,\n                          acc=accuracy,\n                          filename='digits_rf.png')\n```\n\nOr run a whole pipeline with one function:\n\n```python\nimport datacamprojects as dcp\n\ndcp.classification(dataset='digits',\n                   model_type='ensemble',\n                   model_name='RandomForestClassifier',\n                   pickle_name='digits_rf.pickle',\n                   plot_name='digits_rf.png')\n```\n\nFor inspiration, look at the example pipeline in the\n[pipeline folder](https://github.com/marskar/datacamprojects/tree/master/pipeline)\nof the\n[datacamprojects repo](https://github.com/marskar/datacamprojects).\n\n\n",
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        "name": "datacamprojects",
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        "summary": "Tools for the DataCamp Creating Robust Python Projects course",
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