{ "name": "spambase", "title": "Spambase", "resources": [ { "path": "spambase.arff", "pathType": "local", "name": "spambase", "format": "arff", "encoding": "ISO-8859-1" }, { "path": "spambase.csv", "pathType": "local", "name": "spambase", "format": "csv", "mediatype": "text/csv", "encoding": "ISO-8859-1", "dialect": { "delimiter": ",", "quoteChar": "\"" }, "schema": { "fields": [ { "name": "word_freq_make", "type": "number", "format": "default" }, { "name": "word_freq_address", "type": "number", "format": "default" }, { "name": "word_freq_all", "type": "any", "format": "default" }, { "name": "word_freq_3d", "type": "number", "format": "default" }, { "name": "word_freq_our", "type": "number", "format": "default" }, { "name": "word_freq_over", "type": "number", "format": "default" }, { "name": "word_freq_remove", "type": "any", "format": "default" }, { "name": "word_freq_internet", "type": "number", "format": "default" }, { "name": "word_freq_order", "type": "number", "format": "default" }, { "name": "word_freq_mail", "type": "number", "format": "default" }, { "name": "word_freq_receive", "type": "any", "format": "default" }, { "name": "word_freq_will", "type": "number", "format": "default" }, { "name": "word_freq_people", "type": "number", "format": "default" }, { "name": "word_freq_report", "type": "number", "format": "default" }, { "name": "word_freq_addresses", "type": "number", "format": "default" }, { "name": "word_freq_free", "type": "any", "format": "default" }, { "name": "word_freq_business", "type": "number", "format": "default" }, { "name": "word_freq_email", "type": "any", "format": "default" }, { "name": "word_freq_you", "type": "number", "format": "default" }, { "name": "word_freq_credit", "type": "number", "format": "default" }, { "name": "word_freq_your", "type": "number", "format": "default" }, { "name": "word_freq_font", "type": "number", "format": "default" }, { "name": "word_freq_000", "type": "number", "format": "default" }, { "name": "word_freq_money", "type": "number", "format": "default" }, { "name": "word_freq_hp", "type": "number", "format": "default" }, { "name": "word_freq_hpl", "type": "number", "format": "default" }, { "name": "word_freq_george", "type": "number", "format": "default" }, { "name": "word_freq_650", "type": "number", "format": "default" }, { "name": "word_freq_lab", "type": "number", "format": "default" }, { "name": "word_freq_labs", "type": "number", "format": "default" }, { "name": "word_freq_telnet", "type": "number", "format": "default" }, { "name": "word_freq_857", "type": "number", "format": "default" }, { "name": "word_freq_data", "type": "number", "format": "default" }, { "name": "word_freq_415", "type": "number", "format": "default" }, { "name": "word_freq_85", "type": "number", "format": "default" }, { "name": "word_freq_technology", "type": "number", "format": "default" }, { "name": "word_freq_1999", "type": "number", "format": "default" }, { "name": "word_freq_parts", "type": "number", "format": "default" }, { "name": "word_freq_pm", "type": "number", "format": "default" }, { "name": "word_freq_direct", "type": "number", "format": "default" }, { "name": "word_freq_cs", "type": "number", "format": "default" }, { "name": "word_freq_meeting", "type": "number", "format": "default" }, { "name": "word_freq_original", "type": "number", "format": "default" }, { "name": "word_freq_project", "type": "number", "format": "default" }, { "name": "word_freq_re", "type": "number", "format": "default" }, { "name": "word_freq_edu", "type": "number", "format": "default" }, { "name": "word_freq_table", "type": "number", "format": "default" }, { "name": "word_freq_conference", "type": "number", "format": "default" }, { "name": "char_freq_%3B", "type": "number", "format": "default" }, { "name": "char_freq_%28", "type": "number", "format": "default" }, { "name": "char_freq_%5B", "type": "number", "format": "default" }, { "name": "char_freq_%21", "type": "number", "format": "default" }, { "name": "char_freq_%24", "type": "any", "format": "default" }, { "name": "char_freq_%23", "type": "number", "format": "default" }, { "name": "capital_run_length_average", "type": "number", "format": "default" }, { "name": "capital_run_length_longest", "type": "number", "format": "default" }, { "name": "capital_run_length_total", "type": "number", "format": "default" }, { "name": "class", "type": "number", "format": "default" } ], "missingValues": [ "" ] } } ], "readme": "The resources for this dataset can be found at https://www.openml.org/d/44\n\nAuthor: Mark Hopkins, Erik Reeber, George Forman, Jaap Suermondt \nSource: [UCI](https://archive.ics.uci.edu/ml/datasets/spambase) \nPlease 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. ", "description": "The resources for this dataset can be found at https://www.openml.org/d/44\n\nAuthor: Mark Hopkins, Er", "licenses": [ { "name": "ODC-PDDL", "path": "http://opendatacommons.org/licenses/pddl/", "title": "Open Data Commons Public Domain Dedication and License" } ] }