Web Scraping-Based Cryptocurrency Prediction and Analysis

The cryptocurrency market is well known for its huge volatility and constant changes, making accurate predictions dependent on consistently updated data. Web scraping offers an effective way to collect large amounts of real-time data from cryptocurrency-related platforms however, this method comes with challenges, including legal restrictions, the complexity of handling dynamic websites, and scalability concerns. The paper evaluates the effectiveness of various web scraping tools, highlights key challenges and introduces a framework for using web scraping in cryptocurrency market forecasting. The study culminates in the development of a hybrid 2D CNN-LSTM model, which effectively captures both spatial patterns and temporal dependencies in cryptocurrency data. This model demonstrates significant accuracy in long-term trend prediction, though it faces limitations in addressing short-term market volatilities caused by data quality and external factors.