A Comparative Study of Different Machine Learning Algorithms on Bitcoin Value Prediction
Mayukh Samaddar, Rishiraj Saha Roy, Sayantani De, Raja Karmakar
Abstract
Machine learning is growing rapidly and has made many theoretical breakthroughs which find its application in many fields. Bitcoin is a very secure, decentralized, peer to peer currency with no third-party involvement. The price prediction of Bitcoin in the following years is a difficult task. The objective is to take a dig in the prediction of the future prices, dealing with real world data. A comparative study of the results produced by different machine learning models, along with graphs for epoch vs price, error and accuracy for each model using both linear and non-linear functions is done. We are using both neural network algorithms, such as artificial neural network (ANN), recurrent neural network (RNN) and convolutional neural network (CNN), as well as some famous supervised learning algorithms such as Random Forest (RF) and k-nearest neighbors (k-NN), to form the analysis. The time price prediction graphs and the epoch loss accuracy graphs are used for the analysis of each algorithm working on the same data and produces different results. Finally, the best suited algorithm are used for the prediction of future Bitcoin price.