Predictive Modeling of Cryptocurrency Prices through Random Forest Regression and Sentiment Insights

Abstract

In this project, our goal is to employ machinelearning algorithms and big data analysis for forecasting and examining cryptocurrency prices. Cryptocurrencies are digital assets created to function as a means of exchange, utilizing cryptography to ensure transaction security and govern the generation of additional currency units. A key characteristic of cryptocurrencies is their decentralized nature, as they are not issued by any central authority, theoretically making them resistant to government interference or manipulation. Decentralized cryptocurrencies are generated collectively by the entire cryptocurrency system at a predefined rate established during the system’s creation, which is publicly accessible. Most cryptocurrencies utilize blockchain technology in their operations. A blockchain is an ever-expanding ledger of records, referred to as blocks, that are interconnected and safeguarded through cryptographic methods. Each block typically contains a hash pointer linking it to a prior block, a timestamp, and transaction data. By design, blockchains are highly resistant to data tampering. Blockchains serve as open, distributed ledgers capable of efficiently and verifiably recording transactions between two parties in a permanent manner.