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MaartenGr/README.md

maartengrootendorst maartengrootendorst maartengrootendorst

I'm a psychologist turned data scientist who is passionate about using artificial intelligence to make the world a slightly better place. Typically, I like to work on projects involving machine learning, nlp or any analyses involving human behavior. In my spare time, you will often find me working on personal projects or writing articles on medium which typically reside on the intersection of AI and human behavior.

Projects
Project Tags
BERTopic: Topic Modeling with BERT
KeyBERT: Keyword Extraction with BERT
PolyFuzz: A framework for Fuzzy String Matching
SoAn: Analyzing WhatsApp Messages
VLAC: Vectors of Locally Aggregated Concepts
c-TF-IDF: Class-based TF-IDF
ReinLife: Artificial Life with Reinforcement Learning
Reviewer: Character Popularity
Board Game Exploration
Statistically Generated Disney Tournament
Pothole Detection
Optimizing Emté Routes
Exploring Explainable ML
Deploying a Machine Learning Model
Retro Games Reinforcement Learning
Statistical Cross-Validation Techniques
Cluster Analysis: Creating Customer Segments
Exploring Advanced Feature Engineering Techniques
Predicting and Optimizing Auction Prices
Statistical Analysis using the Hurdle Model
Predict and optimize demand
Analyzing Google Takeout Data
Cars Dashboard
Qwixx Visualization
Academic Journey Visualization
Predicting Housing Prices
Analyzing FitBit Data

Recent Posts

Pinned

  1. Leveraging BERT and c-TF-IDF to create easily interpretable topics.

    Python 1.3k 169

  2. Fuzzy string matching, grouping, and evaluation.

    Python 361 31

  3. Minimal keyword extraction with BERT

    Python 722 91

  4. Social Analysis based on Whatsapp data

    Python 87 41

228 contributions in the last year

Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Mon Wed Fri
Activity overview
Contributed to MaartenGr/BERTopic, MaartenGr/KeyBERT, MaartenGr/MaartenGr and 5 other repositories

Contribution activity

July 2021

Created a pull request in MaartenGr/BERTopic that received 10 comments

v0.9

Highlights Get the most representative documents per topic: topic_model.get_representative_docs(topic=1) This allows users to see which documents…

+210 −30 10 comments

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