Crypto Punks EDA in R

Posted on May 10, 2022

The skills the authors demonstrated here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Background:

  • Crypto Punks launched in June 2017 by Larva Labs (John Watkinson and Matt Hall), bought over by Yuga Labs
  • Collection of 10,000 unique 24x24 pixel images
  • Algorithm generated 
  • Early non-fungible tokens (NFT)

Objective:

  • Explore CryptoPunks attributes
  • What are the attributes?
  • Which attributes are common? Rare?

Visualizations:

crypto

Conclusion:

  • Having seven or zero attributes is most rare, and three attributes is most common
  • Hair attribute is most common
  • Teeth attribute is most rare
  • These attributes make the CryptoPunks unique - one of a kind - and can be used to access their rarity and value

Recommendations:

  • Collection items and speculation investments
  • Use attributes to find Punk that you like
  • Look at past transaction prices, bids, and offers
  • Use a combination of attributes, price history, and niche demand for value
  • Go for Aliens, zero attributes, special hair attributes (beanie, top hat, wild white hair)

Next Steps:

  • Link transaction history to available Punks to find relationship between attributes and price
  • Price and demand change over time
  • Feature engineer to create a “Rarity” feature - estimating the rarity of Punks
  • Natural Language Processing of sentiment on CryptoPunks and NFTs

Github: Github CryptoPunks EDA

Presentation Slides: CryptoPunks EDA Slides

About Author

Tam Trinh

Hi, I had my start in analytics with psychology and am interested in data science, particularly within social fields.
View all posts by Tam Trinh >

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