DOI: 10.5281/zenodo.11285571
title: Digital Currencies 2024

description: About Dataset

This dataset consists of seven columns and 2740 rows collected from thirteen different sources for digital currencies. The dataset includes information on the opening price, closing price, highest price, lowest price, and volume, as well as the percentage change and the currencies collected in March 2024.

Here's a description of the contents based on the available columns in the data:

Last Price: The most recent recorded price of Bitcoin. Open Price: The opening price of Bitcoin at the start of the specified time period. Max: The maximum price of Bitcoin during the specified time period. Min: The minimum price of Bitcoin during the specified time period. Size: This may refer to the trading volume of Bitcoin during the specified time period, but requires further clarification to confirm its meaning. Change Persent: The percentage change in the price of Bitcoin compared to the previous time period, it seems there's a typographical error and it might mean "Change Percent". Class: The classification of the currency, in this context, all the data is classified under "Bitcoin". This data could be useful in financial market analytics, especially for those interested in cryptocurrencies and the dynamics of Bitcoin prices. It can be used to study price changes, market fluctuations, or even to develop models for predicting cryptocurrency prices.

Applications in Machine Learning and Beyond This dataset, focusing on Bitcoin prices and their fluctuations, has a wide range of applications, especially within the realm of machine learning and financial analysis:

Price Prediction: Utilizing historical data to train models that can predict future Bitcoin prices. Techniques like time series analysis, regression models, and more sophisticated neural networks (e.g., LSTM) could be applied. Volatility Modeling: Analyzing the variability in Bitcoin prices over time. Machine learning models can help understand patterns in price fluctuations, potentially leading to insights for investors about risk and volatility. Trend Analysis: Identifying long-term trends in Bitcoin's market performance. Machine learning algorithms can detect underlying patterns and trends, helping investors make informed decisions. Anomaly Detection: Spotting unusual patterns or outliers in Bitcoin prices that could indicate market manipulation, fraud, or significant market events. Machine learning models, especially unsupervised algorithms, are adept at detecting anomalies. Sentiment Analysis: By integrating this dataset with social media and news sentiment data, models can assess how public sentiment impacts Bitcoin prices. This involves natural language processing (NLP) techniques to gauge sentiment and correlate it with price movements. Portfolio Management: In the broader scope of financial management, machine learning models can use such datasets to optimize cryptocurrency portfolios, balancing risk and return based on historical performance. Risk Assessment: Analyzing the data to evaluate the financial risk associated with Bitcoin investments. Machine learning can provide probabilistic estimates of future price drops or gains, aiding in risk management strategies. Overall, the detailed data on Bitcoin's pricing and trading volume offers a rich foundation for various analytical and predictive modeling efforts in both academic research and practical financial applications.

Collected and Preprocessing: Wisam Abdullah , Dr. Modhar , and Dr. Ahmed Alsardly are lecturers in Tikrit University.