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Blockchain offers a decentralized, immutable, transparent system of records.It offers a peer-to-peer network of nodes with no centralised governing entitymaking it unhackable and therefore, more secure than the traditionalpaper-based or centralised system of records like banks etc. While there arecertain advantages to the paper-based recording approach, it does not work wellwith digital relationships where the data is in constant flux. Unliketraditional channels, governed by centralized entities, blockchain offers itsusers a certain level of anonymity by providing capabilities to interactwithout disclosing their personal identities and allows them to build trustwithout a third-party governing entity. Due to the aforementionedcharacteristics of blockchain, more and more users around the globe areinclined towards making a digital transaction via blockchain than viarudimentary channels. Therefore, there is a dire need for us to gain insight onhow these transactions are processed by the blockchain and how much time it maytake for a peer to confirm a transaction and add it to the blockchain network.This paper presents a novel approach that would allow one to estimate the time,in block time or otherwise, it would take for a mining node to accept andconfirm a transaction to a block using machine learning. The paper also aims tocompare the predictive accuracy of two machine learning regression models-Random Forest Regressor and Multilayer Perceptron against previously proposedstatistical regression model under a set evaluation criterion. The objective isto determine whether machine learning offers a more accurate predictive modelthan conventional statistical models. The proposed model results in improvedaccuracy in prediction.

Transaction Confirmation Time Prediction in Ethereum Blockchain Using Machine Learning