The files contain one message per line. Each line is composed by two columns: v1 contains the label (ham or spam) and v2 contains the raw text.
This corpus has been collected from free or free for research sources at the Internet:
A collection of 425 SMS spam messages was manually extracted 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 subset of 3,375 SMS randomly chosen ham messages of the NUS SMS Corpus (NSC), which is a dataset 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 available at http://www.comp.nus.edu.sg/~rpnlpir/downloads/corpora/smsCorpus/. -> A list of 450 SMS ham messages collected from Caroline Tag's Ph.D. Thesis.
Note: To solve this case study, I have not used ensemble-based models due to high computation time so if you want, you can try on your own to implement the ensemble models.