Spam-detection

Implementation of ANN for email spam-detection using TensorFlow
The idea is simple - given an email you’ve never seen before, determine whether or not that email is Spam or not.

It is simple ,but very efficient as I reached 99.6% accuracy .

The code is tested on python 2.7.11 and should work on python 2.x

Files description:

The data provided is from a kaggle competition

• TR.tar.gz FILES contains 2500 mails both in Ham(1721) labelled as 1 and Spam(779) labelled as 0
• spam-mail.tr.label is the associated training labels.
• ExtractContent.py extract the subject and body of the email.

In a python compatible environment,

1, invoke the script by command

./ExtractContent.py

2, input source directory -- where you store the source files

For exmaple C:\EMAILPro\CSDMC2010_SPAM\TEST

3, input destination directory -- where you want the extracted body to be

For example C:\EMAILPro\CSDMC2010_SPAM\TEST_NEW

4, we are done.

• email_input.py vectorize the emails text,and outputs trainX.csv, trainY.csv, testX.csv, and testY.csv
• data.tar.gz contains trainX.csv, trainY.csv, testX.csv, and testY.csv
• BagOfWords.p contains all unique words from the data to use it later
• Spam detection.ipynb Ipython notebook that train the model and call emails from ur Gmails to classify

The email format description:

The format of the .eml file is definde in RFC822, and information on recent standard of email, i.e., MIME (Multipurpose Internet Mail Extensions) can be find in RFC2045-2049.

NOTE:

In the notebook U will find how the model works , and how to authenticate ur Gmail