Analysis of Trending NFTs using Time-Series Data and Machine Learning
| dc.contributor.author | Kasish S V; Jashwanth M S; Umashankar Reddy M | |
| dc.date.accessioned | 2024-11-06T05:01:02Z | |
| dc.date.available | 2024-11-06T05:01:02Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Blockchain technology has reshaped the financial ecosphere. The first recorded use of blockchain may be found in a whitepaper from 2008 that was produced by a person going by the pseudonym Satoshi Nakamoto. NFTs, or Non-Fungible Tokens, are a blockchain product that has sparked a lot of interest from the general public. A NFT is a digital asset that utilizes blockchain technology. Because to its impossibility to be copied, replaced, or divided, it is used to demonstrate ownership and authenticity. The ownership of an NFT is recorded in the blockchain and transferable by the owner, making it possible to buy, sell, and trade NFTs. In this paper, we make an effort to establish a relationship between NFT value and several factors, such as social media, OpenSea data, and others. By using algorithms like Random Forest classifier and Support vector machine we have developed an algorithm which can accurately classify the price range of the NFT taking into consideration the current market trends. Through these adopted algorithms, we have obtained has better accuracy and ability to classify the NFT market. The model has been trained using a large dataset comprising around 70000 records and 130 factors. Thus, the model has shown an accuracy of over 95%, improving the accuracy of previous model already in place. Keywords: Blockchain, NFT, prediction, PCA, SVM, Random Forest classifier, Regression | |
| dc.identifier.uri | http://192.168.75.5:4000/handle/123456789/16090 | |
| dc.language.iso | en | |
| dc.publisher | NHCE | |
| dc.title | Analysis of Trending NFTs using Time-Series Data and Machine Learning | |
| dc.type | Learning Object |