spam: Spam E-mail Database

spamR Documentation

Spam E-mail Database


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 "nonspam" or "spam", i.e. unsolicited commercial e-mail.


The data set contains 2788 e-mails classified as "nonspam" and 1813 classified as "spam".

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 650-857-7835

These data have been taken from the UCI Repository Of Machine Learning Databases at


T. Hastie, R. Tibshirani, J.H. Friedman. The Elements of Statistical Learning. Springer, 2001.

kernlab documentation built on June 9, 2022, 9:05 a.m.