Enhancing Accuracy of Bitcoin Cost Prediction using Ridge Linear Classification over Lasso Regression

Enhancing accuracy of Bitcoin cost prediction using ridge linear classification over lasso-regression. For confirmation purposes, use the financial terms ‘sustain’ and ‘conflict’ repeatedly to compare the forecast. A simple prediction method called Lasso-regression is used to forecast the price of bit-coin. Evaluation was conducted to assess the usefulness of each model's presentation for the task, and the results were analyzed. The aim of trying so many dissimilar models was to analyze the variations in their essential hypotheses. In order to reflect on the contribution variables, we used ridge linear and the finest parameters, which is a trusted practice. Lasso-regression is a model that is derived from a T-test that has a value of p is 0.002, (p<0.05). This indicates that there is a statistically momentous difference between the two algorithms. The sample size calculation is performed using an 80 percent G-power pretest, a 0.05% threshold, and a 95% confidence interval (CI). In predicting Bitcoin, there was an improvement in accuracy of 93.06% when using ridge-linear regression with enhanced precision over lasso regression, as indicated by this research study. Conclusion: Using ridge-linear is mapped Lasso-regression with enhanced precision.