Deep Learning Approach For Predictive Analysis Of Crypto-Currency Price+

Abstract

Because cryptocurrencies are now decentralised, the degree of centralised control that they are susceptible to has significantly diminished. This has had an effect on the sector as well as on foreign relations. The bitcoin market is characterised by extremely volatile prices, making it urgently necessary to devise a mechanism that can accurately measure these prices. The long short-term memory (LSTM) and recurrent neural networks (RNN), both of whom are effective instructional models for testing phase and the LSTM is good at spotting longer-term contacts, are used in this investigation to offer novel technique for predicting the price of bitcoin by that take into account a number of features including selling price, amounts, reactor supply, and optimum supply. This research attempts to generate a method to predict the price movement by making use of these features. The methodology that has been suggested is implemented in Python and tested using sample datasets. The findings indicate that the strategy recommended for accurately predicting the price of bitcoin is a viable option.