General comment: How would you link the rarity and image similarity scores into the price prediction?

A few suggestions for your article/presentation:
nit: Rarity Score: Don’t use the reciprocal of a fraction
Further Data Exploration: what method did you use for finding the correlations?
Predicting uncertainty: How would you incorporate the uncertainty prediction in your score?
Results: Show how significant the error is compared to the NFT price.
Twitter Image Search: How would you incorporate this research into your price prediction algorithm?

Good job! Looking forward to your presentation!

A few questions, I think is good to address in the article and presentation:
Rarity: Can you include your approach to the rarity score?
Features: Where do the values for ‘Ear’, ‘Hair’ etc. come from?
Results: can you do some sort of visual comparison between test prediction and ground truth? Also, how would you approach the confidence range of your predictions? How do your prediction’s confidences correlate with how good the prediction is?

Huge thumbs up! I really liked the systematic approach in choosing the “right” model.

Further suggestions: you might want to explore how different test/train splits affect your model (very similar idea to CV, but in a sense to test the actual test set which ideally should be curated.