Prediction of transaction confirmation time in ethereum blockchain using machine learning

Abstract

An Ethereum transaction is defined as the method by which the external world interacts with Ethereum. More and more users are getting involved in cryptocurrencies like Ethereum and Bitcoin. With a sudden increase in the number of transactions happening every second and the capital involved in those transactions, there is a need for the users to able to predict whether a transaction would be confirmed and if yes, then how much time would it take for it to be confirmed. This paper aims to use modern machine learning techniques to propose a model that would be able to predict the time frame within which a miner node will accept and include a transaction to a block. The paper also explores the impact of imbalanced data on our chosen classifiers-Bayes, Random Forest and Multi-Layer Perceptron (MLP) with SoftMax output and the alternative performance measures to optimally handle the imbalanced nature of the dataset.