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Potential Market-Predictive Features Based Bitcoin Price Prediction Using Machine Learning Algorithms
Abstract
Bitcoin is a type of digital currency or computerized money that is utilised for speculation around the world. Bitcoins are files that are saved in a digital wallet programme on a mobile phone or a PC. Every transaction and its timestamp data are recorded in a common list known as blockchain. In this research, the cost of bitcoin is estimated utilising data mining techniques and machine learning algorithms. The dataset is preprocessed with the use of data mining algorithms, which reduces data noise. Bitcoin's price fluctuates, and it is estimated using long short-term memory (LSTM), a type of neural networking, to extract acceptable patterns for modelling and prediction. Discovering recurring patterns in the bitcoin market is a necessary endeavour in order to achieve optimal bitcoin price functionality. The dataset consists of numerous regularly reported bitcoin price features every year. Linear regression (LR) technique is used to estimate the future cost of bitcoin. Daily price shift with the best possible precision by using the available data is also estimated.
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