Volatility Prediction in Cryptocurrency Using NFTs
Keywords:
Cryptocurrency, Machine Learning, NFT, Price Prediction, Volatility.Abstract
The cryptocurrency market has evolved in unprecedented ways over the past decade. However, due to the high price volatility associated with cryptocurrencies, predicting their prices remains an attractive research topic. While many researchers have focused on predicting cryptocurrency prices, there has been relatively little attention given to the latest trend in blockchain applications, specifically non-fungible tokens (NFTs). In this study, we have prepared a dataset comprising NFT sales and transaction data, along with information from other cryptocurrencies. This dataset is utilized to forecast the future price of Bitcoin using several machines learning models, including Linear Regression, Random Forest, and XG Boost. The results highlight the prediction accuracy of these models. Among the three, the Random Forest regressor demonstrates the highest accuracy, followed closely by the XG Boost regressor and Linear Regression. These findings may assist investors in making informed decisions when investing in cryptocurrencies.
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