SPAM E-mail Database
====================

Notes
-----
Description of the Dataset:
    :Number of Instances: 4601 (1813 Spam = 39.4%)

    :Number of Attributes: 58 (57 continuous, 1 nominal class label)

    :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.

    :Missing Attribute Values: None

    :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   0.005444  0.07627  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
       === ==== ====== ======== ======== ============

    :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


Relevant Information:
    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-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.

This file: 'spambase.DOCUMENTATION' at the UCI Machine Learning Repository
http://www.ics.uci.edu/~mlearn/MLRepository.html


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.



