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Predicting the Price of Non-Fungible Tokens with a Machine Learning Approach

Fadaei Tafreshi, Ali | 2024

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 57525 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Rezapour Niari, Maryam
  7. Abstract:
  8. In recent years, the popularity and applications of Non-Fungible Tokens (NFTs) have increased dramatically. For this reason, especially in the current decade, many researches have been conducted in different fields about the use of these tokens at the global level. One of the important and vital topics in the field of non-exchangeable tokens is the issues related to price prediction and the factors affecting their prices, which are mainly investigated using data analysis approaches. In this research, the factors affecting the price of non-exchangeable tokens of Bored Ape Yacht Club (BAYC) have been studied. These factors include traits, transaction data, rarity scores, transaction volume, and previous transaction prices. In addition, information about Ethereum and Bitcoin digital currencies are also included in this analysis. Based on the collected data, static and dynamic price prediction models have been created. These models have been investigated and solved using regression techniques and machine learning algorithms. The results of these analyzes show an accuracy of over 90%. Finally, the results of solving these models have been carefully analyzed to gain valuable insights into the factors affecting the price of non-fungible tokens in the BAYC portfolio
  9. Keywords:
  10. Non-Fungible Tokens ; Regression Analysis ; Machine Learning ; Dynamic Price Prediction Model ; Static Price Prediction Model ; Tired Monkeys Collection

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  • 641f292d5ccf979b2f74d34f0853472178c0b7511456d910805a38481e951368.pdf