NLP Text Classification for text messages
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1. Acurrency
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  98.78%



2. Data description
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This corpus has been collected from free or free for research sources at the Web:

- A collection of between 425 SMS spam messages extracted manually from the Grumbletext Web site. This is a UK forum in which cell phone users make public claims about SMS spam messages, most of them without reporting the very spam message received. The identification of the text of spam messages in the claims is a very hard and time-consuming task, and it involved carefully scanning hundreds of web pages. The Grumbletext Web site is: http://www.grumbletext.co.uk/
- A list of 450 SMS ham messages collected from Caroline Tag's PhD Theses available at http://etheses.bham.ac.uk/253/1/Tagg09PhD.pdf
- A subset of 3,375 SMS ham messages of the NUS SMS Corpus (NSC), which is a corpus of about 10,000 legitimate messages collected for research at the Department of Computer Science at the National University of Singapore. The messages largely originate from Singaporeans and mostly from students attending the University. These messages were collected from volunteers who were made aware that their contributions were going to be made publicly available. The NUS SMS Corpus is avalaible at: http://www.comp.nus.edu.sg/~rpnlpir/downloads/corpora/smsCorpus/
- The amount of 1,002 SMS ham messages and 322 spam messages extracted from the SMS Spam Corpus v.0.1 Big created by José María Gómez Hidalgo and public available at: http://www.esp.uem.es/jmgomez/smsspamcorpus/


3. Models used
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['K Nearest Neighbors', 'Decision Tree','Random Forest','Logistic Regression','SGD Classifier','Navie Bayes','SVM Line']

ensemble with VotingClassifier