Collocations are phrases or expressions containing multiple words, that are highly likely to co-occur. For example – ‘social media’, ‘school holiday’, ‘machine learning’, ‘Universal Studios Singapore’, etc.Continue reading “Collocations in NLP using NLTK Library”
Close your eyes and imagine that you live in a utopian world of perfect data. What do you see? What do you wish to see? Wait! are you imagining a flawless balanced dataset? A collection of data whose labels form a magnificent 1:1 ratio: 50% of this, 50% of that; not a bit to the left, nor a bit to the right. Just perfectly balanced, as all things should be. Now open your eyes, and come back to the real world.
Well, this blog is all about how to handle imbalanced datasets.Continue reading “Handling Imbalanced Datasets with SMOTE in Python”