Brief
Non-Fungible Tokens (NFTs) have become not only one of the most popular ways for creators to sell their artistic content worldwide, but also a common investment vehicle in general. A prestigious brokerage for modern and contemporary art wants to move deeper into the digital space, and believes that NFT investments will become even more popular as the number of them proliferates in the market, despite the economic headwind likely to emerge in the near future.
That said, it's a common notion that NFT pricing is largely driven by speculation rather than an underlying value such as earnings or future cashflow in traditional investments (e.g. stocks, real estate). This unique characteristic is one of the challenges for the art brokerage in choosing the right NFTs to feature in their auctions, leading to the question of whether the fluctuations in price follow some sort of pattern or not (think about the technical analysis on cryptocurrencies). The company would like some kind of tool to evaluate the NFTs that pique their interest.
The goal of this competition is to build a model that predicts the price of NFTs based on a select group of their attributes that are publicly accessible and also based on their social media activity.
Apart from the monetary prize pool, the top 15 of the private leaderboard will receive an original NFT. In order to receive this NFT please insert your ERC-20 Wallet Address in the settings of your profile.
Prizes
1st Prize: $1,500
2nd Prize: $1,000
3rd Prize: $500
Timeline
Competition Starts: 19 December 2022
Competition Ends: 19 February 2023
Winners Announced (Subject to change based on submission results): 10 March 2023
Data Breakdown
The goal of this competition is to build a model that predicts the price of NFTs based on a select group of their attributes that are publicly accessible and also based on their social media activity.
Note that not all NFTs of a collection are necessarily present in the data.
To build your machine learning model, we have provided the following datasets:
- collections.csv: data about the collections that are present in the training and prediction datasets.
- collection_id: ID to identify the collection and the NFTs of the collection
- total_supply: total number of NFTs in the collection
- creation_date: creation date of the collection in the marketplace
- verification_status: status of verification of the collection in the marketplace
- n_of_traits: number of particular traits the NFTs in the collection can have
- contract_type: type of contract in the marketplace
- seller_fees: fees that the seller receives for transactions
- platform_fees: fees that the marketplace receives for transactions
- openrarity_enabled: whether the collection uses OpenRarity to calculate a rarity score and rank for the NFTs in the collection
- has_website: whether the collection has a website or not
- has_own_twitter: whether the collection has its own Twitter account or not
- has_discord: whether the collection has a Discord channel or not
- has_medium: whether the collection has a Medium account or not
- collections_twitter_stats.csv: simple stats about the Twitter accounts of the collections or the creators of the collections.
- collection_id: ID to identify the collection and the NFTs of the collection
- n_tweets_in_range: number of tweets in the relevant timeframe
- avg_likes: average number of likes per tweet
- avg_replies: average number of replies per tweet
- avg_retweets: average number of retweets per tweet
- min_likes: minimum number of likes per tweet
- min_replies: minimum number of replies per tweet
- min_retweets: minimum number of retweets per tweet
- max_likes: maximum number of likes per tweet
- max_replies: maximum number of replies per tweet
- max_retweets: maximum number of retweets per tweet
- nfts_train.csv: NFT data from all collections which can be, alongside the above info, used to train the price prediction model.
- global_index: global ID of the NFT to identify it considering all collections
- nft_id: ID of the NFT to identify its particular collection
- collection_id: ID of the collection the NFT belongs to as per collections.csv file
- rarity_score: estimated rarity score within the collection
- openrarity_score: OpenRarity score if it is enabled (see 1)
- openrarity_rank: OpenRarity rank within the collection if it is enabled (see 1)
- openrarity_max_rank: OpenRarity maximum rank of the collection if it is enabled (see 1)
- last_sale_date: approximated date of the last sale of the NFT
- last_sale_price: price at which the NFT was last sold for (target variable)
- nfts_predict.csv: NFT data from all collections whose price should be predicted using the trained model.
- global_index: global ID of the NFT to identify it considering all collections
- nft_id: ID of the NFT to identify its particular collection
- collection_id: ID of the collection the NFT belongs to as per collections.csv file
- rarity_score: estimated rarity score within the collection
- openrarity_score: OpenRarity score if it is enabled (see 1)
- openrarity_rank: OpenRarity rank within the collection if it is enabled (see 1)
- openrarity_max_rank: OpenRarity maximum rank of the collection if it is enabled (see 1)
- last_sale_date: approximated date of the last sale of the NFT
- submission_format.csv: Short example of the format that the submission file needs to be in to be properly scored.
- global_index: global ID of the NFT to predict
- last_sale_price: prediction of the price at which the NFT may have been sold for (target variable)
Considering that this is a kind of regression problem, the evaluation metric for the score will be based on RMSE.
In particular, the formula shall be: exp( -RMSE / 10 ) where 10 is used as a normalization factor. The maximum score thus will be 1.0 and the minimum score will be 0.0.
NOTE: You may submit a solution file up to 3 times a day.
Final competition results are based on the Private Leaderboard results, and the winner will be the user at the top of the Private Leaderboard.
FAQs
Rules
- This competition is governed by the following Terms of Participation. Participants must agree to and comply with these Terms to participate.
- Users can make a maximum number of 3 submissions per day. If users want to submit new files after making 3 submissions in a day, they will have to wait until the following day to do so. Please keep this in mind when uploading a submission.csv file. Any attempt to circumvent stated limits will result in disqualification.
- The use of external datasets is not allowed.
- It is not allowed to upload the competition dataset to other websites. Users who do not comply with this rule will be disqualified.
- A competition monetary prize will be awarded after we have received, successfully executed, and confirmed the validity of both the code and the solution (See 6.). Once winners are announced and our team reaches out to them, the winners must provide the following by February 26, 2023, to be qualified as a competition winner and receive their monetary prize:
- All source files required to preprocess the data
- All source files required to build, train and make predictions with the model using the processed data
- A requirements.txt (or equivalent) file indicating all the required libraries and their versions as needed
- A ReadMe file containing the following:
- Clear and unambiguous instructions on how to reproduce the predictions from start to finish including data pre-processing, feature extraction, model training and predictions generation
- Environment details regarding where the model was developed and trained, including OS, memory (RAM), disk space, CPU/GPU used, and any required environment configurations required to execute the code
- Clear answers to the following questions:
- Which data files are being used?
- How are these files processed?
- What is the algorithm used and what are its main hyperparameters?
- Any other comments considered relevant to understanding and using the model
- The submitted solution should be able to generate exactly the same output that gives the corresponding score on the leaderboard. If the score obtained from the code is different from what’s shown on the leaderboard, the new score will be used for the final rankings unless a logical explanation is provided. Please make sure to set the seed or random_state etc. so we can obtain the same result from your code.
- The final submission has to be selected manually before the end of the competition (you can select up to 2), or else it will be selected automatically based on your highest public score.
- Apart from the monetary prize pool, the top 15 of the private leaderboard will receive an original NFT. In order to receive this NFT please insert your ERC-20 Wallet Address in the settings of your profile.
- Any prize awards are subject to verification of eligibility and compliance with these Terms of Participation. All decisions of bitgrit and the Competition Sponsor will be final and binding on all matters relating to this Competition.
- Payments to winners may be subject to local, state, federal and foreign tax reporting and withholding requirements.
- If two or more participants have the same score on the leaderboard, the participant who submitted the winning file first will be considered the winner.
- All submissions need to be made as an individual; no teams are allowed in this competition. Users who do not comply with this rule will be immediately disqualified in the case that we find the same or very similar scores and/or uploaded solutions.
- If you have any inquiries about this competition, please don’t hesitate to reach out to us at [email protected]. We ask that users do not contact GASHO 2.0 directly.
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Non-Disclosure Agreement (NDA)
An agreement to not reveal the information shared regarding this competition to others.
- This Non-Disclosure Agreement (“Agreement”) is hereby entered into on 4th December 2024 (“Effective Date”) between you (“Participant”), as a participant in the NFT Price Prediction Challenge (the “Competition”) hosted at bitgrit.net (the “Competition Site”), and bitgrit Inc. (“Bitgrit”).
- Purpose: This Agreement aims to protect information disclosed by Bitgrit to Participant (the “Purpose”).
- Confidential Information: (1) Confidential Information shall mean any and all information disclosed by Bitgrit to the Participant with regard to the entry and participation in the Competition, including (i) metadata, source code, object code, firmware etc. and, in addition to these, (ii) analytes, compilations or any other deliverable produced by the Participant in which such disclosed information is utilized or reflected. (2) Confidential Information shall not include information which; (a) is now or hereafter becomes, through no act or omission on the Participant, generally known or available to the public, or, in the present or into the future, enters the public domain through no act or omission by the Participant; (b) is acquired by the Participant before receiving such information from Bitgrit and such acquisition was without restriction as to the use or disclosure of the same; (c) is hereafter rightfully furnished to the participant by a third party, without restriction as to use or disclosure of the same.
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- Term: The obligations with respect to the Confidential Information under this Agreement shall survive for a period of three (3) years after the effective date. Provided however, if the Confidential Information could be considered to fall under the category of “Trade Secret” of Bitgrit or any related third parties, this Agreement is to remain effective relative to that information for as far as the said information is regarded as Trade Secret under applicable laws and regulations. If the Confidential Information contains personal information, the terms of this Agreement shall remain effective on that information permanently.
- Governing Law: This Agreement shall be governed by and construed and interpreted under the laws of Japan without reference to its principles governing conflicts of laws.
Terms & Conditions
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