Spam ham detection

Objective:

• To identify text messages/sms as spam or ham(non-spam).

Challenges:

• SMSes are limited in length, number of features that can be used for classification is small.
• Text messages generally include abbreviations, informal language, text-speak, other languages written in english.

Dataset:

• UCI Machine Learning Repository has a collection of sms – SMS Spam Dataset.
• It contains: A total of 4827 ham and 747 spam = 5574 messages

Classification technique

• The NaiveBayes classifier is used here.

File Description

• 'spam.py' file has the main program. GUI is build with tkinter.
• Execute the file in the terminal with : 'python spam.py'
• 'spamDetection.ipynb' has the analysis of the dataset.