Clustering for Transaction Classification

# Clustering for Transaction Classification This notebook covers use cases where your data is unlabelled but has features that can be used to cluster them into meaningful categories. The challenge with clustering is making the features that make those clusters stand out human-readable, and that is where we'll look to use GPT-3 to generate meaningful cluster descriptions for us. We can then use these to apply labels to a previously unlabelled dataset. To feed the model we use embeddings created using the approach displayed in the notebook [Multiclass classification for transactions Notebook](Multiclass_classification_for_transactions.ipynb), applied to the full 359 transactions in the dataset to give us a bigger pool for learning