Systems and methods for predictive valuation of non-fungible tokens

An apparatus for generating a predictive valuation for a non-fungible token (NFT) associated with an NFT image, the apparatus comprising:

• an NFT valuation engine configured to:
• generate, using a valuation machine learning framework, a plurality of base contribution features of the NFT, wherein each base contribution feature corresponds to a feature attribute of the NFT image associated with the NFT and each base contribution feature is associated with a feature type,
• determine, using a contribution feature determination machine learning model of the valuation machine learning framework, a contribution feature based on the plurality of base contribution features,
• determine, using the valuation machine learning framework, a plurality of per-feature prediction contribution scores, wherein each per-feature prediction contribution score corresponds to a particular contribution feature, and
• determine, using the valuation machine learning framework, a predictive valuation score for the non-fungible token based on each per-feature prediction contribution score;
• and
• communications hardware configured to:
• provide a predictive valuation report for the non-fungible token based on at least one of the plurality of per-feature prediction contribution scores or the predictive valuation score.

A computer program product for generating a predictive valuation for a non-fungible token (NFT) associated with an NFT image, the computer program product comprising at least one non-transitory computer-readable storage medium storing software instructions that, when executed, cause an apparatus to:

• generate, using a valuation machine learning framework, a plurality of base contribution features of the NFT, wherein each base contribution feature corresponds to a feature attribute of the NFT image associated with the NFT and each base contribution feature is associated with a feature type;
• determine, using a contribution feature determination machine learning model of the valuation machine learning framework, a contribution feature based on the plurality of base contribution features;
• determine, using the valuation machine learning framework, a plurality of per-feature prediction contribution scores, wherein each per-feature prediction contribution score corresponds to a particular contribution feature;
• determine, using the valuation machine learning framework, a predictive valuation score for the non-fungible token based on each per-feature prediction contribution score;
• and
• provide a predictive valuation report for the non-fungible token based on at least one of the plurality of per-feature prediction contribution scores or the predictive valuation score.