Crypto Currency Price Prediction Using Machine Learning

In this project, we attempt to predict the Cryptocurrency price accurately taking into consideration various parameters that affect the Bitcoin value. For the first phase of our survey, we aim to understand and identify daily trends in the Bitcoin market while gaining insight into optimal features surrounding Bitcoin price. For the second phase of our survey, using the available information, we will predict the sign of the daily price change with highest possible accuracy. Bitcoin is a crypto currency which is used worldwide for digital payment or simply for investment purposes. Bitcoin is decentralized i.e. it is not owned by anyone. Transactions made by Bitcoins are easy as they are not tied to any country. Investment can be done through various marketplaces known as “bitcoin exchanges”. These allow people to sell/buy Bitcoins using different currencies. The largest Bitcoin exchange is Mt Gox. Bitcoins are stored in a digital wallet which is basically like a virtual bank account. The record of all the transactions, the timestamp data is stored in a place called Blockchain. Each record in a blockchain is called a block. Each block contains a pointer to a previous block of data. The data on blockchain is encrypted. During transactions the user’s Sample paragraph, The entire document should be in name is not revealed, but only their wallet ID is made public. At present, the prices of these cryptocurrencies do not have a significant amount of studies and research as compared to traditional trading markets. However, the number of studies is steadily increasing as the popularity of Bitcoin is surging. KEYWORDS: Bitcoin, Prediction, Transaction, Blockchain.

Authors

Publication Details

Journal
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Published
2025-03-09
DOI
https://doi.org/10.55041/ijsrem42226
Primary Topic
Stock Market Forecasting Methods
Type
article
Field-Weighted Citation Impact
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article

Crypto Currency Price Prediction Using Machine Learning

D. Anandan
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Stock Market Forecasting Methods
article

Crypto Currency Price Prediction Using Machine Learning

D. Anandan
article en

Abstract

In this project, we attempt to predict the Cryptocurrency price accurately taking into consideration various parameters that affect the Bitcoin value. For the first phase of our survey, we aim to understand and identify daily trends in the Bitcoin market while gaining insight into optimal features surrounding Bitcoin price. For the second phase of our survey, using the available information, we will predict the sign of the daily price change with highest possible accuracy. Bitcoin is a crypto currency which is used worldwide for digital payment or simply for investment purposes. Bitcoin is decentralized i.e. it is not owned by anyone. Transactions made by Bitcoins are easy as they are not tied to any country. Investment can be done through various marketplaces known as “bitcoin exchanges”. These allow people to sell/buy Bitcoins using different currencies. The largest Bitcoin exchange is Mt Gox. Bitcoins are stored in a digital wallet which is basically like a virtual bank account. The record of all the transactions, the timestamp data is stored in a place called Blockchain. Each record in a blockchain is called a block. Each block contains a pointer to a previous block of data. The data on blockchain is encrypted. During transactions the user’s Sample paragraph, The entire document should be in name is not revealed, but only their wallet ID is made public. At present, the prices of these cryptocurrencies do not have a significant amount of studies and research as compared to traditional trading markets. However, the number of studies is steadily increasing as the popularity of Bitcoin is surging. KEYWORDS: Bitcoin, Prediction, Transaction, Blockchain.

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTVol. 09(03)
Openalex Percentile: Top 95%
Stock Market Forecasting Methods
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