A data set collected at Hewlett-Packard Labs, that classifies 4601 e-mails as spam or non-spam. In addition to this class label there are 57 variables indicating the frequency of certain words and characters in the e-mail.
A data frame with 4601 observations and 58 variables.
The first 48 variables contain the frequency of the variable name
(e.g., business) in the e-mail. If the variable name starts with num (e.g.,
num650) the it indicates the frequency of the corresponding number (e.g., 650).
The variables 49-54 indicate the frequency of the characters ‘;’, ‘(’, ‘[’, ‘!’,
‘$’, and ‘#’. The variables 55-57 contain the average, longest
and total run-length of capital letters. Variable 58 indicates the type of the
mail and is either
"spam", i.e. unsolicited
The data set contains 2788 e-mails classified as
"nonspam" and 1813
The “spam” concept is diverse: advertisements for products/web sites, make money fast schemes, chain letters, pornography... This collection of spam e-mails came from the collectors' postmaster and individuals who had filed spam. The 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.
Creators: Mark Hopkins, Erik Reeber, George Forman, Jaap Suermondt at Hewlett-Packard Labs, 1501 Page Mill Rd., Palo Alto, CA 94304
Donor: George Forman (gforman at nospam hpl.hp.com) 650-857-7835
These data have been taken from the UCI Repository Of Machine Learning Databases at http://www.ics.uci.edu/~mlearn/MLRepository.html
T. Hastie, R. Tibshirani, J.H. Friedman. The Elements of Statistical Learning. Springer, 2001.
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