System for intelligent assessment models for non-fungible electronic resources

A computer program product for intelligent assessment models for non-fungible electronic resources, the computer program product comprising a non-transitory computer-readable medium comprising code causing a first apparatus to:

• receive, from a user input device, a request to value a non-fungible token (NFT) of a resource, wherein the NFT is recorded in a distributed ledger;
• retrieve a unique identification code of the NFT from the request;
• crawl the distributed ledger to identify the NFT using the unique identification code;
• retrieve, using a valuation engine, one or more attributes associated with the NFT from one or more metadata layers of the NFT;
• crawl one or more distributed ledgers for one or more NFTs with one or more attributes that are same or similar to the one or more attributes of the NFT;
• retrieve the one or more NFTs with the one or more attributes that are same or similar to the one or more attributes of the NFT;
• retrieve, from a metadata layer of each of the one or more NFTs, a value of each of the one or more NFTs;
• generate a training dataset using at least the one or more attributes of the one or more NFTs and their corresponding value;
• initiate a machine learning algorithm on the training dataset;
• train, using the machine learning algorithm, a machine learning model, wherein training further comprises determining one or more classification parameters for the machine learning model;
• initiate the machine learning model on the one or more attributes associated with the NFT;
• predict, using the one or more classification parameters of the machine learning model, a value of the NFT;
• and
• transmit control signals configured to cause the user input device to display the value of the NFT.

A method for intelligent assessment models for non-fungible electronic resources, the method comprising:

• receiving, from a user input device, a request to value a non-fungible token (NFT) of a resource, wherein the NFT is recorded in a distributed ledger;
• retrieving a unique identification code of the NFT from the request;
• crawling the distributed ledger to identify the NFT using the unique identification code;
• retrieving, using a valuation engine, one or more attributes associated with the NFT from one or more metadata layers of the NFT;
• crawling one or more distributed ledgers for one or more NFTs with one or more attributes that are same or similar to the one or more attributes of the NFT;
• retrieving the one or more NFTs with the one or more attributes that are same or similar to the one or more attributes of the NFT;
• retrieving, from a metadata layer of each of the one or more NFTs, a value of each of the one or more NFTs;
• generating a training dataset using at least the one or more attributes of the one or more NFTs and their corresponding value;
• initiating a machine learning algorithm on the training dataset;
• training, using the machine learning algorithm, a machine learning model, wherein training further comprises determining one or more classification parameters for the machine learning model;
• initiating the machine learning model on the one or more attributes associated with the NFT;
• predicting, using the one or more classification parameters of the machine learning model, a value of the NFT;
• and
• transmitting control signals configured to cause the user input device to display the value of the NFT.