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        "author": "Wu Haifeng",
        "author_email": "wuhaifengdhu@163.com",
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        "description": "#MLData\nMLData, is a project to clean and normalize data for machine learning process.\n\n\n## How to install\n```pip install mldata```\n\n\n\n\n## Usage Example\nUsage Example:    \n```\n        from mldata import Processor\n        new_file_path = \"outputs/new.csv\"\n        processor = Processor(\"resource/raw_dataset.csv\", target_column=\"APPROVE/NOT\", exclude_column_list=[\"id\"],\n                              category_list=[\"Work Class\", \"FnlWgt\", \"Education\", \"Maried Status\", \"Occupation\",\n                                             \"Relationship\", \"Race\", \"Gender\", \"Native Country\", \"Flag\"],\n                              invalid_values=[\"?\", \"\", \"null\", None],\n                              positive_tag=1)\n        processor.normalize()\n        processor.save_to_file(new_file_path)\n```\n\n\n## API Description    \n1, Init function\n```\nProcessor(csv_file_path, target_column, exclude_column_list=None, category_list=None, positive_tag=1,\n                 csv_header=0, invalid_values=None)\n\n```\nParameters:     \ncsv_file_path: The origin csv file path                \ntarget_column: The column name of the target              \nexclude_column_list: Columns no need to normalize       \ncategory_list: A column name list which are category based columns       \npositive_tag: The positive tag for the target column value, default value is 1        \ninvalid_values: values in csv not valid, such as \"?\", \"\", \"null\", None     \n\n2, Norm the list\n```buildoutcfg\nProcessor.normalize()   \n``` \nThis function is used to do norm to the csv file.\n\n\n3, Save result to csv file.    \n```buildoutcfg\nProcessor.save_to_file(new_file_name)          \n``` \nThis function is used to save normalized output to csv file.            \nParameters: \nnew_file_name: The new file name to save the normalized data             \n\n",
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