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<!DOCTYPE html>
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<title>Régression logistique : fichier ‘spam’</title>
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<h1 class="title toc-ignore">Régression logistique : fichier ‘spam’</h1>
</div>
<div class="jumbotron">
<h2>
Réalisation d’un score <br /> afin de prédire un mail comme étant un “spam”
</h2>
source : <br /> <a href="http://archive.ics.uci.edu/ml/index.php" class="uri">http://archive.ics.uci.edu/ml/index.php</a> <br /> <a href="http://archive.ics.uci.edu/ml/machine-learning-databases/spambase/" class="uri">http://archive.ics.uci.edu/ml/machine-learning-databases/spambase/</a> <br /> Extrait des informations du fichier : <br />
<ul>
<li>
Creators: Mark Hopkins, Erik Reeber, George Forman, Jaap Suermondt Hewlett-Packard Labs, 1501 Page Mill Rd., Palo Alto, CA 94304
<li>
Donor: George Forman (gforman at nospam hpl.hp.com) 650-857-7835
<li>
Generated: June-July 1999<br />
</ul>
</div>
<p><strong>Informations principales sur la structure de données :</strong></p>
<pre><code>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%)</code></pre>
<div id="analyse-descriptive" class="section level3">
<h3>Analyse descriptive :</h3>
<p><strong>Proportion Mail/Spam :</strong></p>
<p><img src="reg_log_spam_files/figure-html/unnamed-chunk-2-1.png" width="672" /></p>
<p><strong>Moyenne des frequences des mots/caracteres par type de mail spam/non spam :</strong> <img src="reg_log_spam_files/figure-html/unnamed-chunk-3-1.png" width="672" /></p>
<div class="jumbotron">
<p>On peut constater que le fichier est composé majoritairement d’emails (“non spam”, 61%)<br /> On note également l’apparition d’un caractère “!” dans les mails de type “spam”</p>
</div>
<div class="important">
<p>On peut constater que le fichier est composé majoritairement d’emails (“non spam”, 61%)<br /> On note également l’apparition d’un caractère “!” dans les mails de type “spam”</p>
</div>
</div>
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