AI Driven Blockchain Reward Insights Using ML

Abstract

Several industries have embraced blockchain technology due to the change it has brought, especially in the way records are kept. However, one major problem inherent in the concept of blockchain from the participants perspective is the presence of unpredictable rewards in proof-of-work and proof- of-stake models. That is why this research aims at developing a new strategy to solve this problem by incorporating artificial intelligence (AI) as well as machine learning (ML) algorithms into blockchain compensation prediction. This project seeks to analyze how artificial intelligence (AI) and machine learning (ML) methods may be incorporated into blockchain technology to solve the problem of the unpredictability of rewards. Using the EtherScan API, we have obtained 1000 timestamps and block reward values, and for prediction of rewards for certain timestamps, we use the KNN algorithm, linear regression algorithm, and random forest regressor algorithm. Thus, a What We KNN attained an accuracy of performance specifies, and a random forest regressor attained an impression accuracy range of 74% particular timestamps. These outcomes show the benefits of advanced ML to the blockchain ecosystem as a tool that helps stakeholders make informed decisions concerning activities like mining, staking, or investing in blockchains with potentially high rewards.