Kenan Ekici

KU Leuven '21 | Data Scientist

[Kenan Ekici @kenanscode]

If you frequently model with TFIDF embeddings, we need to talk... https://t.co/mfoYR9T5pw

#TFIDF #ML #MachineLearning #CHI #Medium

[Kenan Ekici @kenanscode]

Visualizing multicollinearity in Python https://t.co/d1gjGAd7IV

#MachineLearning #AI #Python #fintech #stocks #graphs

[Cesar Chavez @CesarChavezP29]

https://t.co/GSYrBi5Z6t

[Richard Bamattre - @bamattre.bsky.social @rbamattre]

Representing multicollinearity as a social network instead of an incredible dense correlation table is unexpected, but actually really works https://t.co/5FMhKIDszn

[Samuel Wong @samuel_wong_]

Visualizing multicollinearity in Python https://t.co/bUAM8tYSFo

[PEREZ JR 🇪🇸 🇵🇹 🇪🇺 @JOSEPEREZJR01]

Visualizing multicollinearity in #Python by @kenancodes https://t.co/TnYOnm654p

[Elon Musk @elonmusk]

If our twitter bid succeeds, we will defeat the spam bots or die trying!

[Kenan Ekici @kenanscode]

@fularhanim @elonmusk It's the same as asking why billion dollar companies buy off smaller companies

[Kenan Ekici @kenanscode]

🤟

[Ralph Sueppel @macro_synergy]

"Visualizing multicollinearity in Python": "Heatmaps are quick and easy...For larger datasets, however, a network graph has the advantage in that it groups correlated features together, and only shows the necessary correlations." https://t.co/6qUagTcSKq 🇺🇦

[Ralph Sueppel @macro_synergy]

"Visualizing multicollinearity in Python": "Heatmaps are quick and easy...For larger datasets, however, a network graph has the advantage in that it groups correlated features together, and only shows the necessary correlations." https://t.co/6qUagTcSKq 🇺🇦

[Symplectic.Research @QuantSymplectic]

Today's reading list:
1. https://t.co/AgeVdPHsOX
2. https://t.co/ypsRqPIlcP
3. https://t.co/T0JgWbELit
4. https://t.co/VaCaLtVULN
5. https://t.co/FjmEn6qPFO
6. https://t.co/BbnLgZqNSh
7. https://t.co/yIZRUcgK9b