  Blockchain offers a decentralized, immutable, transparent system of records.
It offers a peer-to-peer network of nodes with no centralised governing entity
making it unhackable and therefore, more secure than the traditional
paper-based or centralised system of records like banks etc. While there are
certain advantages to the paper-based recording approach, it does not work well
with digital relationships where the data is in constant flux. Unlike
traditional channels, governed by centralized entities, blockchain offers its
users a certain level of anonymity by providing capabilities to interact
without disclosing their personal identities and allows them to build trust
without a third-party governing entity. Due to the aforementioned
characteristics of blockchain, more and more users around the globe are
inclined towards making a digital transaction via blockchain than via
rudimentary channels. Therefore, there is a dire need for us to gain insight on
how these transactions are processed by the blockchain and how much time it may
take 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 and
confirm a transaction to a block using machine learning. The paper also aims to
compare the predictive accuracy of two machine learning regression models-
Random Forest Regressor and Multilayer Perceptron against previously proposed
statistical regression model under a set evaluation criterion. The objective is
to determine whether machine learning offers a more accurate predictive model
than conventional statistical models. The proposed model results in improved
accuracy in prediction.
