1. Title: SPAM E-mail Database 2. Sources: (a) Creators: Mark Hopkins, Erik Reeber, George Forman, Jaap Suermondt Hewlett-Packard Labs, 1501 Page Mill Rd., Palo Alto, CA 94304 (b) Donor: George Forman (gforman at nospam hpl.hp.com) 650-857-7835 (c) Generated: June-July 1999 3. Past Usage: (a) Hewlett-Packard Internal-only Technical Report. External forthcoming. (b) Determine whether a given email is spam or not. (c) ~7% misclassification error. False positives (marking good mail as spam) are very undesirable. If we insist on zero false positives in the training/testing set, 20-25% of the spam passed through the filter. 4. Relevant Information: The "spam" concept is diverse: advertisements for products/web sites, 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. For background on spam: Cranor, Lorrie F., LaMacchia, Brian A. Spam! Communications of the ACM, 41(8):74-83, 1998. 5. Number of Instances: 4601 (1813 Spam = 39.4%) 6. Number of Attributes: 58 (57 continuous, 1 nominal class label) 7. Attribute Information: The last column of 'spambase.data' 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 occuring in the e-mail. The run-length attributes (55-57) measure the length of sequences of consecutive capital letters. For the statistical measures of each attribute, see the end of this file. Here are the definitions of the attributes: 48 continuous real [0,100] attributes of type word_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. 6 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 1 continuous real [1,...] attribute of type capital_run_length_average = average length of uninterrupted sequences of capital letters 1 continuous integer [1,...] attribute of type capital_run_length_longest = length of longest uninterrupted sequence of capital letters 1 continuous integer [1,...] attribute of type capital_run_length_total = sum of length of uninterrupted sequences of capital letters = total number of capital letters in the e-mail 1 nominal {0,1} class attribute of type spam = denotes whether the e-mail was considered spam (1) or not (0), i.e. unsolicited commercial e-mail. 8. Missing Attribute Values: None 9. Class Distribution: Spam 1813 (39.4%) Non-Spam 2788 (60.6%) Attribute Statistics: Min: Max: Average: Std.Dev: Coeff.Var_%: 1 0 4.54 0.10455 0.30536 292 2 0 14.28 0.21301 1.2906 606 3 0 5.1 0.28066 0.50414 180 4 0 42.81 0.065425 1.3952 2130 5 0 10 0.31222 0.67251 215 6 0 5.88 0.095901 0.27382 286 7 0 7.27 0.11421 0.39144 343 8 0 11.11 0.10529 0.40107 381 9 0 5.26 0.090067 0.27862 309 10 0 18.18 0.23941 0.64476 269 11 0 2.61 0.059824 0.20154 337 12 0 9.67 0.5417 0.8617 159 13 0 5.55 0.09393 0.30104 320 14 0 10 0.058626 0.33518 572 15 0 4.41 0.049205 0.25884 526 16 0 20 0.24885 0.82579 332 17 0 7.14 0.14259 0.44406 311 18 0 9.09 0.18474 0.53112 287 19 0 18.75 1.6621 1.7755 107 20 0 18.18 0.085577 0.50977 596 21 0 11.11 0.80976 1.2008 148 22 0 17.1 0.1212 1.0258 846 23 0 5.45 0.10165 0.35029 345 24 0 12.5 0.094269 0.44264 470 25 0 20.83 0.5495 1.6713 304 26 0 16.66 0.26538 0.88696 334 27 0 33.33 0.7673 3.3673 439 28 0 9.09 0.12484 0.53858 431 29 0 14.28 0.098915 0.59333 600 30 0 5.88 0.10285 0.45668 444 31 0 12.5 0.064753 0.40339 623 32 0 4.76 0.047048 0.32856 698 33 0 18.18 0.097229 0.55591 572 34 0 4.76 0.047835 0.32945 689 35 0 20 0.10541 0.53226 505 36 0 7.69 0.097477 0.40262 413 37 0 6.89 0.13695 0.42345 309 38 0 8.33 0.013201 0.22065 1670 39 0 11.11 0.078629 0.43467 553 40 0 4.76 0.064834 0.34992 540 41 0 7.14 0.043667 0.3612 827 42 0 14.28 0.13234 0.76682 579 43 0 3.57 0.046099 0.22381 486 44 0 20 0.079196 0.62198 785 45 0 21.42 0.30122 1.0117 336 46 0 22.05 0.17982 0.91112 507 47 0 2.17 0.0054445 0.076274 1400 48 0 10 0.031869 0.28573 897 49 0 4.385 0.038575 0.24347 631 50 0 9.752 0.13903 0.27036 194 51 0 4.081 0.016976 0.10939 644 52 0 32.478 0.26907 0.81567 303 53 0 6.003 0.075811 0.24588 324 54 0 19.829 0.044238 0.42934 971 55 1 1102.5 5.1915 31.729 611 56 1 9989 52.173 194.89 374 57 1 15841 283.29 606.35 214 58 0 1 0.39404 0.4887 124 This file: 'spambase.DOCUMENTATION' at the UCI Machine Learning Repository http://www.ics.uci.edu/~mlearn/MLRepository.html