Predictive Modelling and Analysis of Cryptocurrency Daily Returns using Ensemble Model

The revolutionary technology and financial prospects of cryptocurrencies have made them a transformative force in the world of finance. In this research study, the authors explored the complex field of cryptocurrency forecasting. For the examination and forecasting of daily returns for various cryptocurrencies, this proposed manuscript offers a thorough framework. The work makes use of cutting-edge deep learning and machine learning methods, including feature engineering, linear regression, Random Forest Regression, and the novel application of Long Short-Term Memory (LSTM)-based deep learning models. The accuracy of prediction is further improved using an ensemble strategy. Lately, the focus is falling more and more behind the industry's dynamic advances, like new coin emergence, technical breakthroughs, and expanding regulatory regulations. Although research is challenged by the industry's frequent developments, a thorough evaluation of the cryptocurrency market as a whole is crucial. The research provides useful tools for data-driven decision-making in the dynamic world of cryptocurrency markets for researchers, data scientists, investors, and financial analysts. There is still a dearth of research on the subject, much of which focuses just on Bitcoin rather than a wider variety of cryptocurrencies.