Ketan Bonde, Vina M. Lomte, Professor, Prathamesh Bhalerao, Pratik Chavan, Vrushabh Bhandalkar
RMD School College of Engineering, Pune, India.


The rapid evolution of the cryptocurrency market, mirroring traditional currency systems but operating digitally and decentralized, marks a significant paradigm shift. Despite cryptographic safeguards, the industry remains under intense scrutiny due to its nascent nature. Cryptocurrencies, emerging as a distinct asset class driven by financial technology, present a rich landscape for research. Understanding public sentiment towards cryptocurrencies is pivotal for a comprehensive understanding of this ecosystem. This study employs a Long Short-Term Memory (LSTM) neural network to predict Bitcoin’s price in US dollars, utilizing the Keras library in Python. Renowned for its accuracy and ability to capture long-term dependencies, LSTM models offer promising avenues for price forecasting. Drawing from an extensive literature review, we validate LSTM’s effectiveness in predicting cryptocurrency prices. Our goal is to devise a robust solution applicable to various cryptocurrencies, ensuring the highest possible accuracy. This research contributes to the advancement of cryptocurrency markets, empowering informed decision-making in this dynamic financial realm. Overall, this study represents a significant advancement in the prediction of cryptocurrency sentiment, offering valuable insights for investors, traders, and policymakers alike. By harnessing the power of advanced analytics