Please describe your proposed solution.

Lending Pond will create NFT valuation process by using machine learning using a vector machine model to estimate NFT value based on historical data of key parameters proper to each NFT project such as rarity rank, trait sales floors etc and learn and adjust to what the brokers would estimate the listing at.

Please describe how your proposed solution will address the Challenge that you have submitted it in.

The valuation tool is a key component for educating users on all NFT platforms with the necessary tools to make informed decisions on NFT apps.

• Integration With Lending Pond: https://lendingpond.app/lend
• Plug-n-Play Integration: Lending Pond will provide APIs to ensure scalability as well as easy integration of the NFT valuation tool on other NFT finance platforms.

The valuation tool will leverage Lending Pond's rarity database, historical NFT transactions, with advanced machine learning to estimate true NFT market price, along with human feedback.

What are the main risks that could prevent you from delivering the project successfully and please explain how you will mitigate each risk?

1. A NFT 's price may be subjective because it is not fungible
2. Lack of historical data and liquidity: it is difficult for the machine to predict and estimate with little to no data because a project is new.
3. NFT diversity: NFTs do not always have a consitent way of displaying metadata

There also needs to be a condition where the machine also withholds giving estimates on NFTs that may have bad wallet distribution because of wash trading or whale manipulation on a project.

Please provide a detailed plan, including timeline and key milestones for delivering your proposal.

August 1st: Research

August 10th: Coding

August 20th: refining, and increased estimate feedback using human brokers to train the machine

September 1st: Public, Integration on lending pond and other partnerships along with making the service accessible to other platforms.

Please provide a detailed budget breakdown.

10000 will go towards development costs and salaries

2000 for marketing and promotion

8000 for brokers salaries because humans must give the machine human feedback and tweak the machine.

5000 for helping other platforms set up and integrate/ reaching out

5000 for various costs(servers, equation updates, data storage)

Please provide details of the people who will work on the project.

Lending Pond team has built and delivered many successful NFT features and services in the past:

• Lending Pond: lendingpond.app
• Loan to value ration: https://lendingpond.app/lend

Lending Pond is experienced in being able to plan, research, and implement NFT tools and services to the Cardano ecosystem at a efficient, quick, and at a high level. Our skills and abilities were best proven as Lending Pond is the first and only functional Lending Platform on Cardano to be on mainnet.

If you are funded, will you return to Catalyst in a later round for further funding? Please explain why / why not.

no, because the updates in the future will be easy and quick enough that it the cost will be negligible.

Please describe what you will measure to track your project's progress, and how will you measure these?

The algorithim will be completed and launched by at most in begining of October.

There will be a minimum of 4 apps using this machine, for both lending, and buying and selling NFTs.

• Research (Models, data, etc.) - 2 week
• Coding (Putting the model together) - 2 week
• Testing (Fine tuning the tool) - 3 week
• Developing a public API - 1 week

What does success for this project look like?

At least 4 of Cardano's biggest apps will integrate this price estimation feature on marketplaces and lending platforms.

Please provide information on whether this proposal is a continuation of a previously funded project in Catalyst or an entirely new one.

no