Published Paper Details:

Cryptocurrency price prediction is a complex yet essential task in financial markets due to the extreme volatility and dynamic nature of digital assets like Bitcoin, Ethereum, and Ripple. As decentralized assets, cryptocurrencies are influenced by various factors, including market sentiment, trading volume s, technological developments, and macroeconomic trends. Traditional forecasting methods often struggle to capture these complexities, making advanced computational approaches such as machine learning (ML) and deep learning (DL) valuable for this purpose. This study aims to develop a robust system to predict cryptocurrency prices for the next 30 days by combining the strengths of ML and DL models, which are known for their ability to analyses large datasets and identify intricate patterns. The foundation of this project lies in collecting and pre-processing historical cryptocurrency data. The dataset includes key features such as open, high, low, and close (OHLC) prices, trading volumes, and technical indicators like Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD).