Bitcoin Price Prediction Using Deep Learning and Neural Networks

This work explains the role of tree regressions, long-term short-term memory models and ARIMA models in predicting Bitcoin values. In this study, we used Bitcoin Sets data to test and train the Ml and the Ai models. The data filtration process was completed with the help of the Python Library. When we understand the data, we adjust it for using those characteristics or attributes that are best suited for model. Here this model was implemented by recording the results. The accuracy of the decision tree regression model was found to be high when comparing with other machine learning models; R flat is found that is equal to 0.67.On the other hand, the R of the LSTM model is flat 0.49, the R of the ARIMA model is flat. Ninety-four. By this, we can see that deep learning model is highly improved model when compared with machine learning model.