KPMG-MBS Natural Language Processing Challenge (Prize Winner)

KPMG and Melbourne Business School held a data science challenge with a topic in Natural Language Processing (NLP). The task was to explore complaints data from banks and analyse how NLP techniques can be leveraged. In this project, I explored and compared a number of NLP techniques, including tokenising, stemming, bag-of-words, word embedding etc, and designed a model to automatically classify complaint categories, so banks can use the model to increase workflow efficiency. The project won the 1st place with a $4000 prize.