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I have been working on a Python coded priority email inbox, with the ultimate aim of using a machine learning algorithm to label (or classify) a selection of emails as either important or un-important. I will begin with some background information and then move into my question.

I have so far developed code to extract data from an email and process it to discover the most important ones. This is achieved using the following email features:

Senders Address Frequency
Thread Activity
Date Received (time between replies)
Common Words in body/subject

The code I have currently applies a ranking (or weighting) (value 0.1-1) to each email based on its importance and then applies a label of either 'important' or 'un-important' (In this case this is just 1 or 0). The status of priority is awarded if the rank is >0.5. This data is stored in a CSV file (as below).

 From           Subject       Body        Date          Rank    Priority  HelloWorld    Body Words  10/10/2012    0.67    1  ByeWorld      Body Words  10/10/2012    0.21    0  SayWorld      Body Words  10/10/2012    0.91    1  HeyWorld      Body Words  10/10/2012    0.48    0
 etc        ....................................................................................

I have two sets of email data (One Training, One Testing). The above applies to my training email data. I am now attempting to train a learning algorithm so that I can predict the importance of the testing data.

To do this I have been looking at both SCIKIT and NLTK. However, I am having trouble transferring the information I have learnt in the tutorials and implementing into my project. I have no particular requirements in regards to which learning algorithm is used. Is this as simple as applying the following? And if so how?

X, y =,

from sklearn.svm import LinearSVC
clf = LinearSVC()

clf =, y)

X_new = [Testing Email Data]