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        "description": "[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\n# datacleanbot\nAutomated Data Cleaning Tool.\nThe main goal is to develop a Python tool ``datacleanbot`` such that:\n    Given a random parsed raw dataset representing a supervised learning problem, the Python tool is capable of automatically identifying the potential issues and reporting the results and recommendations to the end-user in an effective way.\n\n## Install\n\n```sh\n$ pip install datacleanbot\n```\n\n## QuickStart\n\nAcquire data from OpenML:\n\n    >>> import openml as oml\n    >>> data = oml.datasets.get_dataset(id) # id: openml dataset id\n    >>> X, y, features = data.get_data(target=data.default_target_attribute, return_attribute_names=True)\n    >>> Xy = data.get_data()\n\nAutoclean data with datacleanbot\n\n    >>> import datacleanbot.dataclean as dc\n    >>> Xy = dc.autoclean(Xy, data.name, features)\n\n\n## Description\n\n``datacleanbot`` is equipped with the following capabilities:\n* Present an overview report of the given dataset\n    * The most important features\n    * Statistical information (e.g., mean, max, min)\n    * **Data types of features**\n* Clean common data problems in the raw dataset\n    * Duplicated records\n    * Inconsistent column names\n    * **Missing values**\n    * **Outliers**\n\nThe three aspects ``datacleanbot`` meaningfully automates are marked in bold.\n\n## User's Guide\n\nThe user's guide can be found at [datacleanbot](https://datacleanbot.readthedocs.io/en/latest/).\n\n\n",
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