Publication Date

Spring 2024

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)


Computer Science

First Advisor

William Andreopoulos

Second Advisor

Sayma Akther

Third Advisor

Faranak Abri


NFT Price Prediction, Machine Learning


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.

Available for download on Friday, May 23, 2025