Abstract

In this modern era of technology, the more secured ways are needed to deal with financial investments or transactions. Cryptocurrency can be named as one of the solutions for this concern. Cryptocurrency is a digital payment system that doesn’t rely on banks to verify transactions. A digital payment system called cryptocurrency doesn’t rely on banks to validate transactions. Anyone can send and receive funds using this method. Payments made using cryptocurrencies only exist as digital records in an online database that detail specific transactions. This new sort of investment is providing vast areas for research to the researchers. By predicting its price this can be as more efficient asset for investment. Much research is going on in this area. This paper proposes two different recurrent neural network (RNN) algorithms to predict prices of cryptocurrency namely Bit coin and they are Long short-term memory (LSTM) and Gated Recurrent Unit (GRU). the measures being used in this paper to assess the accuracy of the used algorithms are mean squared error (MSE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), are also used to assess different prediction algorithms. Comparisons are carried out on the basis of three datasets training, testing, and validation. The loss and evaluation functions are based on the mean squared error. The model performs better the lower the value. Based on findings the GRU model outperforms the LSTM algorithm in terms of accuracy and reliability in predicting cryptocurrency prices, but both algorithms produce excellent outcomes.