Description
Spam classification using logistic regression
Consider the email spam data set. This consists of 4601 email messages, from which 57
features have been extracted. These are as follows:
• 48 features, giving the percentage of words in a given message which match a given word
on the list. The list contains words such as “business”, “free”, “george”, etc. (The data was
collected by George Forman, so his name occurs quite a lot.)
• 6 features, giving the percentage of characters in the email that match a given character
on the list. The characters are ; ( [ ! $ #
• Feature 55: The average length of an uninterrupted sequence of capital letters
• Feature 56: The length of the longest uninterrupted sequence of capital
• Feature 57: The sum of the lengths of uninterrupted sequence of capital
1. Download the data at http://www.cse.scu.edu/~yfang/coen140/spambase.zip. The data is
split into a training set (of size 3065) and a test set (of size 1536).
2. Please normalize the features by standardizing the columns so they all have mean 0
and unit variance.
3. Build and fit a logistic regression model using gradient descent. Report the error rate on
the training and test sets.
