This dataset provides comprehensive historical price data for Ethereum (ETH) from January 1, 2018, to September 26, 2024. It contains vital trading information that can help analyze market trends, investor behavior, and potential future price movements. The dataset is structured to include daily trading statistics, making it suitable for various analyses, including time series forecasting and algorithmic trading strategies.

Column Descriptions :

Time: This column indicates the specific date for each trading session. The dates are formatted in month/day/year (MM/DD/YYYY) style, allowing for easy chronological sorting and analysis of trends over time.

Open: The opening price of Ethereum for the day. This price reflects the market's initial valuation of Ethereum at the start of the trading day and is influenced by various factors, including previous day’s performance and market sentiment.

High: The highest price reached by Ethereum during the trading day. This value shows the peak demand for Ethereum within the session, indicating significant bullish activity and investor interest at that price point.

Low: The lowest price of Ethereum during the day. This metric represents the minimum value traders were willing to accept for Ethereum and can signify bearish pressure or selling activity during that trading session.

Close: The closing price of Ethereum at the end of the trading day. This is a crucial figure, as it serves as the reference point for assessing the performance of Ethereum in subsequent days. Analysts often use this price to calculate daily returns and overall market performance.

Volume: The total trading volume for Ethereum on that day, representing the number of Ethereum units traded. High volume indicates strong market activity and can signal investor confidence or a significant shift in market dynamics. Conversely, low volume may suggest a lack of interest or uncertainty among traders.

Conclusion : This Ethereum price dataset is a valuable resource for performing technical analysis, developing trading algorithms, and conducting price predictions. By examining the patterns and relationships within the data, analysts and traders can gain insights into market behavior and make informed decisions.

Context

Bitcoin and other cryptocurrencies have captured the imagination of technologists, financiers, and economists. Digital currencies are only one application of the underlying blockchain technology. Like its predecessor, Bitcoin, the Ethereum blockchain can be described as an immutable distributed ledger. However, creator Vitalik Buterin also extended the set of capabilities by including a virtual machine that can execute arbitrary code stored on the blockchain as smart contracts.

Both Bitcoin and Ethereum are essentially OLTP databases, and provide little in the way of OLAP (analytics) functionality. However the Ethereum dataset is notably distinct from the Bitcoin dataset:

• The Ethereum blockchain has as its primary unit of value Ether, while the Bitcoin blockchain has Bitcoin. However, the majority of value transfer on the Ethereum blockchain is composed of so-called tokens. Tokens are created and managed by smart contracts.

• Ether value transfers are precise and direct, resembling accounting ledger debits and credits. This is in contrast to the Bitcoin value transfer mechanism, for which it can be difficult to determine the balance of a given wallet address.

• Addresses can be not only wallets that hold balances, but can also contain smart contract bytecode that allows the programmatic creation of agreements and automatic triggering of their execution. An aggregate of coordinated smart contracts could be used to build a decentralized autonomous organization.

Content

The Ethereum blockchain data are now available for exploration with BigQuery. All historical data are in the ethereum_blockchain dataset, which updates daily.

Our hope is that by making the data on public blockchain systems more readily available it promotes technological innovation and increases societal benefits.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.crypto_ethereum.[TABLENAME]. Fork this kernel to get started.

Acknowledgements

Cover photo by Thought Catalog on Unsplash

Inspiration

• What are the most popularly exchanged digital tokens, represented by ERC-721 and ERC-20 smart contracts?
• Compare transaction volume and transaction networks over time
• Compare transaction volume to historical prices by joining with other available data sources like Bitcoin Historical Data

Ethereum Classic is a cryptocurrency with shared history with the Ethereum cryptocurrency. On technical merits, the two cryptocurrencies are nearly identical, differing only in programming language features supported by the Ethereum Virtual machine which is used to write smart contracts. This dataset contains the blockchain data in their entirety, pre-processed to be human-friendly and to support common use cases such as auditing, investigating, and researching the economic and financial properties of the system. Interested in learning more about how Cloud Public Data is working to make data from blockchains and cryptocurrencies more accessible? Check out our blog post on the Google Cloud Big Data Blog and try the sample query below to get started. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

This dataset contains detailed daily price information for Bitcoin (BTC) and Ethereum (ETH) against USD, covering the period from 2014 to 2024. It includes key financial metrics such as Open, High, Low, Close prices, Volume, and Adjusted Close values for both cryptocurrencies. This dataset is ideal for cryptocurrency enthusiasts, financial analysts, and data scientists looking to explore trends, analyze market movements, and develop predictive models for Bitcoin’s and Ethereum’s performance over the last decade.

The data provides insights into Bitcoin and Ethereum’s price fluctuations, from Bitcoin’s early adoption phase to Ethereum's rise as a dominant platform for decentralized applications and smart contracts. Whether you're interested in historical patterns, volatility analysis, or future price predictions, this comprehensive dataset serves as a valuable resource for your cryptocurrency research and analysis.

Ethereum is a crypto currency which leverages blockchain technology to store transactions in a distributed ledger. A blockchain is an ever-growing "tree" of blocks, where each block contains a number of transactions. To learn more, read the "Ethereum in BigQuery: a Public Dataset for smart contract analytics" blog post by Google Developer Advocate Allen Day. This dataset is part of a larger effort to make cryptocurrency data available in BigQuery through the Google Cloud Public Datasets program . The program is hosting several cryptocurrency datasets, with plans to both expand offerings to include additional cryptocurrencies and reduce the latency of updates. You can find these datasets by searching "cryptocurrency" in GCP Marketplace. For analytics interoperability, we designed a unified schema that allows all Bitcoin-like datasets to share queries. Interested in learning more about how the data from these blockchains were brought into BigQuery? Looking for more ways to analyze the data? Check out the Google Cloud Big Data blog post and try the sample queries below to get started. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Context

Real and up to date stock market exchange of cryptocurrencies can be quite expensive and are hard to get. However, historical financial data are the starting point to develop algorithm(s) to analyze market trend and why not beat the market by predicting market movement.

Content

Data provided in this dataset are historical data from the beginning of ETH-USD pair market on Kraken exchange up to the present (2021 December). This data comes frome real trades on one of the most popular cryptocurrencies exchange.

Trading history

Historical market data, also known as trading history, time and sales or tick data, provides a detailed record of every trade that happens on Kraken exchange, and includes the following information: - Timestamp - The exact date and time of each trade. - Price - The price at which each trade occurred. - Volume - The amount of volume that was traded.

OHLCVT

In addition, OHLCVT data are provided for the most common period interval: 1 min, 5 min, 15 min, 1 hour, 12 hours and 1 day. OHLCVT stands for Open, High, Low, Close, Volume and Trades and represents the following trading information for each time period: - Open - The first traded price - High - The highest traded price - Low - The lowest traded price - Close - The final traded price - Volume - The total volume traded by all trades - Trades - The number of individual trades

Don't hesitate to tell me if you need other period interval 😉 ...

Update

This dataset will be updated every quarter to add new and up to date market trend. Let me know if you need an update more frequently.

Inspiration

Can you beat the market? Let see what you can do with these data!

Cryptocurrency historical datasets from January 2012 (if available) to October 2021 were obtained and integrated from various sources and Application Programming Interfaces (APIs) including Yahoo Finance, Cryptodownload, CoinMarketCap, various Kaggle datasets, and multiple APIs. While these datasets used various formats of time (e.g., minutes, hours, days), in order to integrate the datasets days format was used for in this research study. The integrated cryptocurrency historical datasets for 80 cryptocurrencies including but not limited to Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), Tether (USDT), Ripple (XRP), Solana (SOL), Polkadot (DOT), USD Coin (USDC), Dogecoin (DOGE), Tron (TRX), Bitcoin Cash (BCH), Litecoin (LTC), EOS (EOS), Cosmos (ATOM), Stellar (XLM), Wrapped Bitcoin (WBTC), Uniswap (UNI), Terra (LUNA), SHIBA INU (SHIB), and 60 more cryptocurrencies were uploaded in this online Mendeley data repository. Although the primary attribute of including the mentioned cryptocurrencies was the Market Capitalization, a subject matter expert i.e., a professional trader has also guided the initial selection of the cryptocurrencies by analyzing various indicators such as Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), MYC Signals, Bollinger Bands, Fibonacci Retracement, Stochastic Oscillator and Ichimoku Cloud. The primary features of this dataset that were used as the decision-making criteria of the CLUS-MCDA II approach are Timestamps, Open, High, Low, Closed, Volume (Currency), % Change (7 days and 24 hours), Market Cap and Weighted Price values. The available excel and CSV files in this data set are just part of the integrated data and other databases, datasets and API References that was used in this study are as follows: [1] https://finance.yahoo.com/ [2] https://coinmarketcap.com/historical/ [3] https://cryptodatadownload.com/ [4] https://kaggle.com/philmohun/cryptocurrency-financial-data [5] https://kaggle.com/deepshah16/meme-cryptocurrency-historical-data [6] https://kaggle.com/sudalairajkumar/cryptocurrencypricehistory [7] https://min-api.cryptocompare.com/data/price?fsym=BTC&tsyms=USD [8] https://min-api.cryptocompare.com/ [9] https://p.nomics.com/cryptocurrency-bitcoin-api [10] https://www.coinapi.io/ [11] https://www.coingecko.com/en/api [12] https://cryptowat.ch/ [13] https://www.alphavantage.co/ This dataset is part of the CLUS-MCDA (Cluster analysis for improving Multiple Criteria Decision Analysis) and CLUS-MCDAII Project: https://aimaghsoodi.github.io/CLUSMCDA-R-Package/ https://github.com/Aimaghsoodi/CLUS-MCDA-II https://github.com/azadkavian/CLUS-MCDA

Context

This dataset contains 1-minute interval price data for Ethereum (ETH/USD), including the following columns: timestamp (Unix), open, high, low, close, and volume. The data is collected directly from the Bitstamp public API and is updated daily to provide the most recent market information.

This project was inspired by the work of the Kaggle user zielak, who created a similar dataset for Bitcoin (BTC/USD), and aims to provide a ready-to-use dataset for analysis, visualization, and machine learning applications related to Ethereum trading and market research.

Content

See https://github.com/ViniciusQroz/ethereum-1min-price-kaggle for the automation and scrapping script.

Bitcoin is a crypto currency leveraging blockchain technology to store transactions in a distributed ledger. A blockchain is an ever-growing tree of blocks. Each block contains a number of transactions. To learn more, read the Bitcoin Wiki . This dataset is part of a larger effort to make cryptocurrency data available in BigQuery through the Google Cloud Public Datasets program. The program is hosting several cryptocurrency datasets, with plans to both expand offerings to include additional cryptocurrencies and reduce the latency of updates. You can find these datasets by searching "cryptocurrency" in GCP Marketplace. For analytics interoperability, we designed a unified schema that allows all Bitcoin-like datasets to share queries. To further interoperate with Ethereum and ERC-20 token transactions, we also created some views that abstract the blockchain ledger to be presented as a double-entry accounting ledger. Interested in learning more about how the data from these blockchains were brought into BigQuery? Looking for more ways to analyze the data? Check out our blog post on the Google Cloud Big Data Blog and try the sample query below to get started. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

This dataset surfaces data from the Ethereum Goerli testnet blockchain and includes tables for blocks, transactions, logs, and more. Ethereum is a decentralized open-source blockchain system that features its own cryptocurrency, Ether. A blockchain is an ever-growing tree of blocks. Each block contains a number of transactions. The Goerli testnet is a testnet of the Ethereum network. It enables developers to deploy, test, and execute their dApps in the blockchain environment risk-free and at no cost. For more information, see the Blockchain Analytics documentation . This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Context

Ethereum Classic is an open-source, public, blockchain-based distributed computing platform featuring smart contract (scripting) functionality. It provides a decentralized Turing-complete virtual machine, the Ethereum Virtual Machine (EVM), which can execute scripts using an international network of public nodes. Ethereum Classic and Ethereum have a value token called "ether", which can be transferred between participants, stored in a cryptocurrency wallet and is used to compensate participant nodes for computations performed in the Ethereum Platform.

Ethereum Classic came into existence when some members of the Ethereum community rejected the DAO hard fork on the grounds of "immutability", the principle that the blockchain cannot be changed, and decided to keep using the unforked version of Ethereum. Till this day, Etherum Classic runs the original Ethereum chain.

Content

In this dataset, you will have access to Ethereum Classic (ETC) historical block data along with transactions and traces. You can access the data from BigQuery in your notebook with bigquery-public-data.crypto_ethereum_classic dataset.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.crypto_ethereum_classic.[TABLENAME]. Fork this kernel to get started.

Acknowledgements

This dataset wouldn't be possible without the help of Allen Day, Evgeny Medvedev and Yaz Khoury. This dataset uses Blockchain ETL. Special thanks to ETC community member @donsyang for the banner image.

Inspiration

One of the main questions we wanted to answer was the Gini coefficient of ETC data. We also wanted to analyze the DAO Smart Contract before and after the DAO Hack and the resulting Hardfork. We also wanted to analyze the network during the famous 51% attack and see what sort of patterns we can spot about the attacker.

https://www.bernardmarr.com/img/What%20Is%20The%20Difference%20Between%20Bitcoin%20and%20Ethereum.png">

Context

Ethereum a decentralized, open-source blockchain featuring smart contract functionality was proposed in 2013 by programmer Vitalik Buterin. Development was crowdfunded in 2014, and the network went live on 30 July 2015, with 72 million coins premined.

Some interesting facts about Ethereum(ETH): - Ether (ETH) is the native cryptocurrency of the platform. It is the second-largest cryptocurrency by market capitalization, after Bitcoin. Ethereum is the most actively used blockchain. - Some of the world’s leading corporations joined the EEA(Ethereum Alliance, is a collaboration of many block start-ups) and supported “further development.” Some of the most famous companies are Samsung SDS, Toyota Research Institute, Banco Santander, Microsoft, J.P.Morgan, Merck GaA, Intel, Deloitte, DTCC, ING, Accenture, Consensys, Bank of Canada, and BNY Mellon.

Content

The dataset consists of ETH prices from March-2016 to the current date(1830days) and the dataset will be updated on a weekly basis.

Information regarding the data

The data totally consists of 1813 records(1813 days) with 7 columns. The description of the features is given below

| No |Columns | Descriptions | | -- | -- | -- | | 1 | Date | Date of the ETH prices | | 2 | Price | Prices of ETH(dollars) | | 3 | Open | Opening price of ETH on the respective date(Dollars) | | 4 | High | Highest price of ETH on the respective date(Dollars) | | 5 | Low | Lowest price of ETH on the respective date(Dollars) | | 6 | Vol. | Volume of ETH on the respective date(Dollars). | | 7 | Change % | Percentage of Change in ETH prices on the respective date | |

Acknowledgements

The dataset was extracted from investing.com

Inspiration

Experts say that ethereum has a huge potential in the future. Do you believe it? Well, let's find it by building our own creative models to predict if the statement is true.

Cryptocurrency trading analysis and algorithmic strategy development rely on high-quality, high-frequency historical data. This dataset provides clean, structured OHLCV data for one of the most liquid and popular trading pairs, ETH/USDT, sourced directly from the Bybit exchange. It is ideal for quantitative analysts, data scientists, and trading enthusiasts looking to backtest strategies, perform market analysis, or build predictive models across different time horizons.

Content

The dataset consists of three separate CSV files, each corresponding to a different time frame:

BYBIT_ETHUSDT_15m.csv: Historical data in 15-minute intervals. BYBIT_ETHUSDT_1h.csv: Historical data in 1-hour intervals. BYBIT_ETHUSDT_4h.csv: Historical data in 4-hour intervals.

Each file contains the same six columns:

• Datetime: The UTC timestamp for the start of the candle/bar.
• Open: The opening price of ETH at the start of the interval.
• High: The highest price reached during the interval.
• Low: The lowest price reached during the interval.
• Close: The closing price at the end of the interval.
• Volume: The trading volume in the base asset (ETH) during the interval.

Methodology & Update Schedule

• Source: The data was collected using the public API of the Bybit cryptocurrency exchange via a Python script utilizing the ccxt library.
• Data Range: The dataset currently covers the period from July 5, 2021, to June 28, 2025.
• Update Frequency: This dataset is maintained locally and will be updated on a weekly basis to include the most recent trading data, ensuring its relevance for ongoing analysis.

Acknowledgements

This dataset is made possible by the publicly available data from the Bybit exchange. Please consider this when using the data for your projects.

Inspiration (Potential Use Cases)

• Backtesting Trading Strategies: Test the performance of strategies like moving average crossovers, RSI-based signals, or MACD indicators.
• Time Series Forecasting: Build models (e.g., ARIMA, LSTM, Prophet) to predict future price movements.
• Volatility Analysis: Analyze market volatility by calculating rolling standard deviations or other risk metrics.
• Feature Engineering: Create new technical indicators and features for machine learning models.
• Market Visualization: Plot candlestick charts and overlay them with various technical analysis tools.

Context

This dataset is about the value of Bitcoin, Ethereum and Litecoin in USD and volume transacted of each currency between Jan-1-2020 and Apr-19-2021. Data is collected for ever minute between these two dates from the open of stock exchange to close.

Content

Unix Timestamp - Date represented as epoc value Date - date and time when the data point was collected Symbol - Symbol of the currency Open - Open value of the currency High - Highest value of currency in the given minute Low - Lowest value of currency in the given minute Close - Close value of the currency in the given minute Volume - Volume of the currency transacted in the given minute.

Acknowledgements

Thanks to the team @https://www.CryptoDataDownload.com for making this data available for public

Inspiration

An attempt to understand and learn timeseries data analysis.

What is Ethereum?

Ethereum is a decentralized, open-source blockchain with smart contract functionality. Ether (ETH or Ξ) is the native cryptocurrency of the platform. After Bitcoin, it is the largest cryptocurrency by market capitalization. Ethereum is the most actively used blockchain. Ethereum was proposed in 2013 by programmer Vitalik Buterin.

Data Description

This dataset provides the history of daily prices of Ethereum. The data starts from 07-Aug-2015. All the column descriptions are provided. Currency is USD.

This dataset contains historical price data for the top global cryptocurrencies, sourced from Yahoo Finance. The data spans the following time frames for each cryptocurrency:

BTC-USD (Bitcoin): From 2014 to December 2024 ETH-USD (Ethereum): From 2017 to December 2024 XRP-USD (Ripple): From 2017 to December 2024 USDT-USD (Tether): From 2017 to December 2024 SOL-USD (Solana): From 2020 to December 2024 BNB-USD (Binance Coin): From 2017 to December 2024 DOGE-USD (Dogecoin): From 2017 to December 2024 USDC-USD (USD Coin): From 2018 to December 2024 ADA-USD (Cardano): From 2017 to December 2024 STETH-USD (Staked Ethereum): From 2020 to December 2024

Key Features:

Date: The date of the record. Open: The opening price of the cryptocurrency on that day. High: The highest price during the day. Low: The lowest price during the day. Close: The closing price of the cryptocurrency on that day. Adj Close: The adjusted closing price, factoring in stock splits or dividends (for stablecoins like USDT and USDC, this value should be the same as the closing price). Volume: The trading volume for that day.

Data Source:

The dataset is sourced from Yahoo Finance and spans daily data from 2014 to December 2024, offering a rich set of data points for cryptocurrency analysis.

Use Cases:

Market Analysis: Analyze price trends and historical market behavior of leading cryptocurrencies. Price Prediction: Use the data to build predictive models, such as time-series forecasting for future price movements. Backtesting: Test trading strategies and financial models on historical data. Volatility Analysis: Assess the volatility of top cryptocurrencies to gauge market risk. Overview of the Cryptocurrencies in the Dataset: Bitcoin (BTC): The pioneer cryptocurrency, often referred to as digital gold and used as a store of value. Ethereum (ETH): A decentralized platform for building smart contracts and decentralized applications (DApps). Ripple (XRP): A payment protocol focused on enabling fast and low-cost international transfers. Tether (USDT): A popular stablecoin pegged to the US Dollar, providing price stability for trading and transactions. Solana (SOL): A high-speed blockchain known for low transaction fees and scalability, often seen as a competitor to Ethereum. Binance Coin (BNB): The native token of Binance, the world's largest cryptocurrency exchange, used for various purposes within the Binance ecosystem. Dogecoin (DOGE): Initially a meme-inspired coin, Dogecoin has gained a strong community and mainstream popularity. USD Coin (USDC): A fully-backed stablecoin pegged to the US Dollar, commonly used in decentralized finance (DeFi) applications. Cardano (ADA): A proof-of-stake blockchain focused on scalability, sustainability, and security. Staked Ethereum (STETH): A token representing Ethereum staked in the Ethereum 2.0 network, earning staking rewards.

This dataset provides a comprehensive overview of key cryptocurrencies that have shaped and continue to influence the digital asset market. Whether you're conducting research, building prediction models, or analyzing trends, this dataset is an essential resource for understanding the evolution of cryptocurrencies from 2014 to December 2024.

Context

This is a primitive dataset and my first collection. The transactions in this dataset occurred over a 14hrs period prior to my scrape on 02/03/18 at approx 7:30PM GMT. Was interested in doing some exploratory analysis to see how much Ethereum was being sent to each address over this 17 hour period. So scraped 10000 page table results.

Content

There are 8 Columns:

• Record (Containing Record Number)
• TxHash (The Transaction ID)
• Block (Block number that this transaction was on)
• Age (Age from Approx 7:30PM GMT on 02/03/18)
• From (Senders Wallet Address)
• To (Recipients Wallet Address)
• Value (Transaction Value. Please Note: If 0 will have been a smart contract)
• TxFee (The gas price and cost of the transaction)

Inspiration

Whats next? Block Dataset which is much larger.

📊 Overview

This dataset contains a preview sample of Ethereum smart contracts deployed between block 0 and block 1,000,000.

Each entry includes: - 🧠 contract_address – Deployed contract address - 📦 block_number – Block number where the contract was created - ⏱️ block_timestamp – Timestamp of the block - ✅ is_verified – Whether the contract is verified on Etherscan - 💰 eth_balance – Current ETH balance of the contract

This sample gives a glimpse into a much larger dataset currently being prepared.

🧱 Dataset Size (Current Preview)

• 📦 Size: 6 GB (CSV files)
• 📄 Total contracts: 76,267,363 smart contracts
• 📉 Note: This version includes only basic metadata (without full verification or balance data for all blocks)

🛠️ Full Dataset (Coming Soon)

The full Ethereum contract dataset will include:

• All contracts deployed up to block #22,298,844
• Extended metadata for each contract:
  • ✅ Verification status (from Etherscan)
  • 💰 ETH balance
  • 🧬 Bytecode
  • 📜 ABI (for verified contracts)
  • 🔍 Contract type: ERC20 / ERC721 / Proxy / Custom
  • 🔑 Function selectors

The full metadata is being processed and will be available soon.

📩 Interested ? Let’s connect via Kaggle or @Rojan_beta on X.

🪪 License

This preview dataset is released under the Creative Commons CC BY-SA 4.0 license.
You are free to use, modify, and share it — with attribution.

The Ethereum Transaction Fraud Detection (ETFD) Dataset is a comprehensive and high-quality dataset designed to facilitate research and development in the domain of fraud transaction detection within the Ethereum blockchain. Generated by the Ethereum Transaction Data Generator (ETDG), the ETFD dataset addresses common challenges in public Ethereum fraud detection datasets, such as single cardinality, high cardinality, missing values, and data encoding issues, thereby reducing the risk of model overfitting and enhancing model performance. The details of how to generate the dataset using ETDG can be found at: https://github.com/Huned-materwala/Ethereum-Transaction-Data-Generator-ETDG

The ETFD dataset contains 85,003 transaction data,14 features, and binary class labels. 42,499 transactions belong to the ’No Fraud’ class, and 42,504 belong to the ’Fraud’ class. The features are as follows:

• blockNumber: Number (height) of the block in which the transaction is included
• confirmations: The number of blocks that have been mined since the block containing the transactions. It indicates how many times the transaction has been confirmed
• Month: Month in which the transaction is performed
• Day: Day at which the transaction is performed
• Hour: Hour at which the transaction is performed
• mean_value_received: Average transaction amount received
• variance_value_received: Variance in the transaction amount received
• total_received: Total transaction amount received
• time_diff_first_last_received: Time difference between first and last received transactions
• total_tx_sent: Number of transactions sent
• total_tx_sent_malicious: Number of transactions sent to malicious addresses
• total_tx_sent_unique: Number of transactions sent to unique addresses
• total_tx_sent_malicious_unique: Number of transactions sent to unique malicious addresses
• total_tx_received_malicious_unique: Number of transactions received from unique malicious addresses

https://i2.wp.com/www.mon-livret.fr/wp-content/uploads/2021/10/crypto-Metaverse-696x392.png?resize=696%2C392&ssl=1" alt="">

Context

• The metaverse, a living and breathing space that blends physical and digital, is quickly evolving from a science fiction dream into a reality with endless possibilities. A world where people can interact virtually, create and exchange digital assets for real-world value, own digital land, engage with digitized real-world products and services, and much more.

• Major tech giants are beginning to recognize the viability and potential of metaverses, following Facebook’s groundbreaking Meta rebrand announcement. In addition to tech companies, entertainment brands like Disney have also announced plans to take the leap into virtual reality.

• While the media hype is deafening, your average netizen isn’t fully aware of what a metaverse is, how it operates and, most importantly—what benefits and opportunities it can offer them as a user.

https://cdn.images.express.co.uk/img/dynamic/22/590x/Metaverse-tokens-cryptocurrency-explained-ethereum-killers-new-coins-digital-currency-meta-news-1518777.jpg?r=1638256864800" alt="">

What Is The Metaverse?

• In its digital iteration, a metaverse is a virtual world based on blockchain technology. This all-encompassing space allows users to work and play in a virtual reflection of real-life and fantasy scenarios, an online reality, ranging from sci-fi and dragons to more practical and familiar settings like shopping centers, offices, and even homes.

• Users can access metaverses via computer, handheld device, or complete immersion with a VR headset. Those entering the metaverse get to experience living in a digital realm, where they will be able to work, play, shop, exercise, and socialize. Users will be able to create their own avatars based on face recognition, set up their own businesses of any kind, buy real estate, create in-world content and asset,s and attend concerts from real-world superstars—all in one virtual environment,

• With that said, a metaverse is a virtual world with a virtual economy. In most cases, it is an online reality powered by decentralized finance (DeFi), where users exchange value and assets via cryptocurrencies and Non-Fungible Tokens.

What Are Metaverse Tokens?

• Metaverse tokens are a unit of virtual currency used to make digital transactions within the metaverse. Since metaverses are built on the blockchain, transactions on underlying networks are near-instant. Blockchains are designed to ensure trust and security, making the metaverse the perfect environment for an economy free of corruption and financial fraud.

• Holders of metaverse tokens can access multiple services and applications inside the virtual space. Some tokens give special in-game abilities. Other tokens represent unique items, like clothing for virtual avatars or membership for a community. If you’ve played MMO games like World of Warcraft, the concept of in-game items and currencies are very familiar. However, unlike your traditional virtual world games, metaverse tokens have value inside and outside the virtual worlds. Metaverse tokens in the form of cryptocurrency can be exchanged for fiat currencies. Or if they’re an NFT, they can be used to authenticate ownership to tethered real-world assets like collectibles, works or art, or even cups of coffee.

• Some examples of metaverse tokens include SAND of the immensely popular Sandbox metaverse. In The Sandbox, users can create a virtual world driven by NFTs. Another token is MANA of the Decentraland project, where users can use MANA to purchase plots of digital real estate called “LAND”. It is even possible to monetize the plots of LAND purchased by renting them to other users for fixed fees. The ENJ token of the Enjin metaverse is the native asset of an ecosystem with the world’s largest game/app NFT networks.

Dataset Information

• The dataset brings 198 metaverse cryptos. Pls refer to the file Metaverse coins.csv to find the list of metaverse crypto coins.

• The dataset will be updated on a weekly basis with more and more additional metaverse tokens, Stay tuned ⏳