Cryptocurrency price prediction using Social Media Data Mining and Epidemic Modeling

Abstract

With the introduction of blockchain technology in recent years, there has been a mas sive increase in the use of Cryptocurrencies. In any event, due of the market’s un predictable behavior and excessive cost volatility, Cryptocurrencies are not viewed as a viable business prospect. Because of their deterministic character, the majority of the arrangements disclosed in the writing for Cryptocurrency value guaging may not be relevant for ongoing value prediction. The prior suggested models induce layer wise haphazardness into the observed, which includes brain organization enactments to recreate market unpredictability. Our project will provide a method for grouping comparable coins based on their characteristics. The fluctuations in the value of the categorized cryptocurrency are then calculated. After examining some of the most fre quently used deep learning algorithms in the presented articles, it is clear that neural network deep learning, as well as other forms of data mining, cannot handle the price prediction issue efficiently and effectively. As a result, it is critical to adopt and create new technologies in order to improve efficiency. Another approach that we may use is social media data mining and epidemic modeling. Using this, we should be able to make better predictions, given social media sites are masters at studying different peo ple’s opinions these days. In reality, it is currently being used by a significant number of organizations to forecast the value of the stock market, giving us the opportunity to improve time efficiency and provide better results.