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    "description": "**Author**: Mark Hopkins, Erik Reeber, George Forman, Jaap Suermondt    \n**Source**: [UCI](https:\/\/archive.ics.uci.edu\/ml\/datasets\/spambase)   \n**Please cite**: [UCI](https:\/\/archive.ics.uci.edu\/ml\/citation_policy.html)\n\nSPAM E-mail Database  \nThe \"spam\" concept is diverse: advertisements for products\/websites, make money fast schemes, chain letters, pornography... Our collection of spam e-mails came from our postmaster and individuals who had filed spam.  Our collection of non-spam e-mails came from filed work and personal e-mails, and hence the word 'george' and the area code '650' are indicators of non-spam.  These are useful when constructing a personalized spam filter.  One would either have to blind such non-spam indicators or get a very wide collection of non-spam to generate a general purpose spam filter.\n \nFor background on spam:  \nCranor, Lorrie F., LaMacchia, Brian A.  Spam! Communications of the ACM, 41(8):74-83, 1998.  \n\n### Attribute Information:  \nThe last column denotes whether the e-mail was considered spam (1) or not (0), i.e. unsolicited commercial e-mail. Most of the attributes indicate whether a particular word or character was frequently occurring in the e-mail. The run-length attributes (55-57) measure the length of sequences of consecutive capital letters.  \n\nFor the statistical measures of each attribute, see the end of this file. Here are the definitions of the attributes:  \n\n48 continuous real [0,100] attributes of type  \nword_freq_WORD = percentage of words in the e-mail that match WORD,  i.e. 100 * (number of times the WORD appears in the e-mail) \/ total number of words in e-mail.  A \"word\" in this case is any string of alphanumeric characters bounded by non-alphanumeric characters or end-of-string.\n \n6 continuous real [0,100] attributes of type char_freq_CHAR = percentage of characters in the e-mail that match CHAR, i.e. 100 * (number of CHAR occurences) \/ total characters in e-mail\n \n1 continuous real [1,...] attribute of type capital_run_length_average\n = average length of uninterrupted sequences of capital letters\n \n1 continuous integer [1,...] attribute of type capital_run_length_longest\n = length of longest uninterrupted sequence of capital letters\n \n1 continuous integer [1,...] attribute of type capital_run_length_total\n = sum of length of uninterrupted sequences of capital letters\n = total number of capital letters in the e-mail\n \n1 nominal {0,1} class attribute of type spam\n = denotes whether the e-mail was considered spam (1) or not (0), \n i.e. unsolicited commercial e-mail.",
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    "uploader": "Jan van Rijn",
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    "creator": "Mark Hopkins, Erik Reeber, George Forman, Jaap Suermondt, Hewlett-Packard Labs",
    "contributor": null,
    "date": "2014-04-06 23:22:41",
    "update_comment": null,
    "last_update": "2014-04-06 23:22:41",
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    "url": "https:\/\/www.openml.org\/data\/download\/44\/dataset_44_spambase.arff",
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            "SPAM E-mail Database The \"spam\" concept is diverse: advertisements for products\/websites, make money fast schemes, chain letters, pornography... Our collection of spam e-mails came from our postmaster and individuals who had filed spam. Our collection of non-spam e-mails came from filed work and personal e-mails, and hence the word 'george' and the area code '650' are indicators of non-spam. These are useful when constructing a personalized spam filter. One would either have to blind such non-sp "
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