Cryptocurrency analysis using machine learning approaches

Cryptocurrency is (CRYPTO) popularity and commercial acceptance are pivotal in reshaping the financial system. The allure of potentially high returns has especially piqued the interest of investors in CRYPTO trading. To maximize returns on Bitcoin (BTC) investments, precise price prediction (PP) becomes indispensable. Since CRYPTO PP is inherently a time series task, utilising Deep Learning (DL) models is highly advisable. In this context, our study delves into a comprehensive assessment of different DL models, focusing primarily on Artificial and Convolutional Neural Networks, to predict daily BTC prices (BP). Our initial dataset was sourced from Kaggle, a premier hub for data science endeavours. To ensure consistency during preprocessing, we employed the min-max scaler technique. Alongside the DL models, our analysis also encompassed a spectrum of Machine Learning (ML) models such as, Decision Tree Regression (DT), Linear Regression (LR), Random Forest Regression (RF), and others. This was to offer a holistic comparison across various predictive methodologies. To gauge the predictive prowess of these models, we employed key regression metrics like mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (R). It's essential to note that a lower RMSE signifies better model performance, indicating reduced prediction errors. In our results, CNN emerged as the top contender among DL models with an RMSE of 0.0543, MAE of 0.0324, and an R-value of 0.960, underscoring its exemplary capability in forecasting BP. In our evaluation of traditional ML models, the RF model showed remarkable performance with an RMSE of 0.0561 and an MAE of 0.0246. Per previous discussions, a lower RMSE value indicates better predictive accuracy, making the RF model highly effective in this context. Despite this, it's noteworthy that the RF model's performance was slightly inferior to that of the CNN model, especially regarding RMSE. Additionally, while the R2 value for the RF model was 0.958287, indicating a solid fit to the data; the correlation coefficient (R-value) was not specified for this or other ML models. Including the R value could provide a more nuanced understanding of each model's performance and should be considered in future evaluations.