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        "description": "# ML Helper\n---\nHelpers to speed up and structure machine learning projects.\n\nThe library is available in [Pypi](https://pypi.org/project/ml-helper/)\n\n### Installing\n---\n\n\nThe easiest way to install ml-helper is through ```pip```\n\n```python\npip install ml-helper\n```\n\nTo use it in your project, you must first import the library\n\n```python\nfrom ml_helper.helper import Helper\n```\n\nAnd then create a Helper object with a dictionary of keys related to your project\n\n```python\nKEYS = {\n    'SEED': 1,\n    'TARGET': 'y',\n    'METRIC': 'r2',\n    'TIMESERIES': True,\n    'SPLITS': 5\n}\n\nhp = Helper(KEYS)\n```\n\nAfter this, you may use the helper object's many functions\n\n#### Dependencies\n\nML-Helper requires:\n* Python (>3.5)\n* Numpy (>=1.16)\n* Pandas (>=0.23.4)\n* Seaborn (>=0.9)\n* Scikit-learn (>=0.20)\n* Natplotlib (>=3)\n* Scipy (>=1)\n* Imblearn\n* Vecstack\n\n### Functionality\n---\n\nThe functionality is separated into 4 groups:\n* Data Exploration\n    * Missing Data\n    * Boxplot of numerical variables\n    * Coefficient of variation\n    * Correlation (numerical and categorical)\n    * Under Represented Features\n    * Target Variable Distribution\n    * Feature Importance\n    * PCA Component Variance\n* Data Preparation\n    * Convert features to categories\n    * Drop multiple columns\n* Modeling\n    * Cross Validation (with stratified kfolds, or time series split depending on use case)\n        * Randomized Grid Search\n    * Pipeline: Collection of models and pipeline steps that get performed and scored\n    * Predict: Predict on unseen data\n    * Stack Predict: Build a stacked model and perform a prediction\n* Regression\n    * Plots for predictions\n* Classification\n    * ROC Curve\n    * Classification Report\n* Others\n    * Select features based on types\n    * Split X and y\n    * Plot models/pipelines\n\n### Working Examples\n---\nIf you wish to see the library in use, you may view the notebooks in the [examples](examples) section.\n\nAlso, you can see the implementation in their corresponding Kaggle Kernels:\n\n* [Bike Sharing in Washington D.C.: Time Series Regression](https://www.kaggle.com/akoury/bike-sharing-in-washington-d-c-using-ml-helper)\n\n* [Employee Attrition: Classification](https://www.kaggle.com/akoury/employee-attrition-basis-to-create-ml-helper-lib)\n\n### ML-Helper Coding Style\n---\nMl-Helper complies to PEP8 and uses ```black``` for coding standards\n\n### Versioning\n---\n[SemVer](http://semver.org/) is used for versioning. \n\n### License\n---\nThis project is licensed under the MIT License - see the [License](license.txt) file for details\n\n",
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