## 6 Results and Discussion  
The evaluation results for the Random Forest, Gradient Boosting, and Deep Learning models show unique strengths in predicting cryptocurrency prices across various datasets. Figure 3 illustrates the RMSE and MAE outcomes for the three models--Random Forest, Deep Learning, and Gradient Boosting--applied to four cryptocurrency datasets: Binance, Bitcoin, Ethereum, and Litecoin. Since all RMSE and MAE values are below 0.15, this indicates that all models perform with a high degree of accuracy, regardless of the variations in metric values among the models. On the Binance dataset, all three models show low RMSE and MAE values, indicating precise performance. RF and GB yield nearly identical results, highlighting their effectiveness in capturing the underlying patterns of Binance's market data. Although Deep Learning presents slightly higher error values than the other models, it still attains  
Figure 3: RMSE and MAE results for all the models utilized. metrics very close to zero, affirming its capacity to model Binance's relatively stable market patterns accurately. On the Bitcoin dataset, all models delivered precise predictions, reflected in their low RMSE and MAE values. While Deep Learning exhibits higher error rates than Random Forest and GB, these errors are still minor, indicating the model's dependable performance. Random Forest and GB present slightly lower errors, highlighting their strength in grasping the complexities of Bitcoin's volatile market. Nevertheless, the substantial fact that all errors are well below 0.15 confirms that each model is capable of accurately predicting Bitcoin prices. The models for the Ethereum dataset again demonstrate high accuracy. Both Random Forest and GB achieve low, comparable RMSE and MAE values, highlighting their strong predictive power for Ethereum's market. Although Deep Learning shows slightly higher error metrics, these figures remain close to zero, indicating that the model accurately reflects Ethereum's price trends. The overall low metric values across all models signify that Ethereum's market is highly predictable, despite minor variations in error. All models exhibit consistent accuracy on the Litecoin dataset, reflected in their low RMSE and MAE values. Although Deep Learning records the highest error rates, the values are still comfortably below 0.15, indicating that the model is capable of delivering dependable predictions. Both Random Forest and GB attain low error rates and show marginally better performance, showcasing their ability to adapt to Litecoin's market trends. Despite variations in error rates, all models attain near-zero metrics, underlining their precision and reliability in forecasting Litecoin prices. Table 1 compares the performances of all the algorithms across the four datasets. Performance was evaluated using the MSE, RMSE, MAE, and (\(R^{2}\)). Below is a detailed analysis of the models for each dataset. All three models performed remarkably well on the Binance dataset, achieving MSE values that are nearly zero and \(R^{2}\) values close to 1. Both RF and GB exhibit an MSE of 0.0001 and an \(R^{2}\) of 0.9998, showing that they can predict Binance prices with nearly perfect accuracy. Meanwhile, DL has a slightly higher MSE of 0.0002 and an \(R^{2}\) of 0.9996, indicating a minor decrease in its ability to fit the data compared to RF and GB. Nevertheless, the differences are negligible, and all models exhibit outstanding accuracy for this dataset. On the Ethereum dataset, RF surpasses the other models with an MSE of 0.0002 and an \(R^{2}\) of 0.9995, highlighting its effectiveness in capturing the data's underlying patterns.