Publication Date
Spring 2024
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
William Andreopoulos
Second Advisor
Sayma Akther
Third Advisor
Faranak Abri
Keywords
NFT Price Prediction, Machine Learning
Abstract
In the evolving cryptocurrency marketplace, Non Fungible Tokens (NFT) pose a unique challenge when it comes to predicting the prices due to their high volatility and fluctuating nature. This project aims to create a model that utilizes deep learning techniques to accurately forecast NFT prices. Based on the real time data and the transaction history, the model uses Convolutional Neural Networks (CNNs) and Long Term Short Memory Networks (LSTM) to analyze and make effective predictions about future prices. The methodology involves gathering data from two online marketplaces, Dune and Opensea to create a dataset that enhances the model’s predictive capabilities. This research includes data collection, data preprocessing, and model evaluation ensuring a validation process for the proposed predictive system. The primary objective of the project is to provide analytics to the investors and the analysts for making informed decisions, within the NFT market.
Recommended Citation
Bagili, Akhil Patil, "NFT Price Prediction using Machine Learning" (2024). Master's Projects. 1390.
DOI: https://doi.org/10.31979/etd.4wkb-chqk
https://scholarworks.sjsu.edu/etd_projects/1390
