BERTopic is a novel topic modeling technique that leverages BERT embeddings and c-TF-IDF to create dense clusters allowing for easily interpretable topics.

PolyFuzz performs fuzzy string matching, string grouping, and contains extensive evaluation functions. PolyFuzz is meant to bring fuzzy string matching techniques together within a single framework.

KeyBERT is a minimal and easy-to-use keyword extraction technique that leverages BERT embeddings to create keywords and keyphrases that are most similar to a document.

Concept introduces the concept of Concept Modeling. It takes inspiration from topic modeling techniques to cluster images, find commonalities (i.e. concepts) and create a multimodal representation.

Created a package that allows in-depth analyses (sentiment analysis, topic modelling, etc.) on whatsapp conversations.

Using Reinforcement Learning, entities learn to survive, reproduce, and make sure to maximize the fitness of their kin.

c-TF-IDF is a class-based TF-IDF procedure that can be used to generate features from textual documents based on the class they are in.

Leveraging clusters of word embeddings to create features from a collection of documents allowing for classification of documents.

A package for scraping user reviews from IMDB, generate C-TF-IDF based word clouds, and extract popular characters from reviews.

Tournament brackets are generated based on a seed score calculated through scraping data from IMDB and RottenTomatoes.

Used Apple Store data to analyze which business model aspects (entry timing and technological innovation) influence performance of mobile games.

Created an application for exploring board game matches that I tracked over the last year. Streamlit and Heroku were used for deployment.