Analytical Study for Prjice Prediction of Bitcoin Using Machine Learning and Artificial Intelligence

Abstract

Abstract: Cryptocurrency transactions rely on encryption algorithms for security and decentralization. However, traditional machine learning algorithms often struggle with the dynamic cryptocurrency market, leading to inaccuracies. To address this, integrating Integrating sentiment analysis of Twitter data alongside Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) models enriches the analysis of cryptocurrency market dynamics. Twitter provides dataset from kaggle open source and the sentiment expressed by users on cryptocurrencies, enabling the prediction of tweet sentiment (positive, negative, or neutral) through Natural Language Processing (NLP). This integration enhances understanding of market sentiment's impact on cryptocurrency prices: positive sentiment may drive prices up, negative sentiment may lead to declines, and neutral sentiment may indicate stability. By analyzing sentiment alongside historical trends and emerging patterns, users gain a holistic view of cryptocurrency markets. This approach aids decision-making, improving transaction accuracy and efficiency. Ultimately, the combination of sentiment analysis with BiLSTM and BiGRU models advances the comprehension of cryptocurrency market dynamics, enhancing user insights and facilitating informed decisions in the volatile cryptocurrency ecosystem.