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Cryptocurrency Time-Series Forecasting Project
Crypto & Blockchain Transactions
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About
This collection of files offers a project focused on forecasting the price of the ETH/USDT cryptocurrency pair using a Recurrent Neural Network (RNN) with artificial intelligence. Developed by Senior Artificial Intelligence Engineer Emirhan BULUT, the software successfully predicted a price fall on 24 February 2022, achieving high accuracy. The project is open source and includes the Python notebook, software codes, and the data used for the prediction, which was originally sourced from Binance.
Columns
The primary data file,
ETH-USD_data.csv, contains the historical price information for the ETH/USDT pair. While the exact column names are not specified in the description, a typical financial time-series dataset like this would likely include columns such as:- Date: The specific date of the record.
- Open: The opening price for that day.
- High: The highest price reached during the day.
- Low: The lowest price reached during the day.
- Close: The closing price for that day.
- Volume: The amount of the asset traded during the day.
Distribution
The project is distributed as a collection of files, including:
Cryptocurrency_[ETH_USDT]_prediction_with_RNN_Neural_Network_Artificial_Intelligence_Project.ipynb: The main Jupyter Notebook containing the Python code.ETH-USD_data.csv: The dataset used for training and prediction.- Supporting
.pngimage files and aREADME.md. The size of the README file is noted as 1.88 kB.
Usage
This project is ideal for exploring time-series forecasting in the domain of cryptocurrency markets. Key applications include:
- Developing and testing price prediction models for volatile assets like ETH/USDT.
- Serving as a practical example of applying RNNs and deep learning libraries such as Tensorflow and Keras.
- Understanding the end-to-end process of a machine learning project, from data handling with Pandas to model evaluation with Scikit-learn.
- Visualising financial data and model predictions using Matplotlib.
Coverage
The data focuses on the ETH/USDT trading pair. The provided prediction example specifically references 24 February 2022, using historical data sourced from the Binance cryptocurrency exchange. The temporal scope of the full
ETH-USD_data.csv file is not specified.License
Attribution 4.0 International
Who Can Use It
- Data Scientists and AI Engineers: Can use the code as a foundation for building more advanced financial prediction models or experimenting with different neural network architectures.
- Students and Academics: Can study the project as a case study in applying deep learning to real-world financial data.
- Python Developers: Can learn how to integrate libraries like Tensorflow, Pandas, and Scikit-learn for a machine learning task.
- Cryptocurrency Analysts: Can explore programmatic approaches to market forecasting and test quantitative strategies.
Dataset Name Suggestions
- ETH/USDT Price Prediction with RNN
- Cryptocurrency Time-Series Forecasting Project
- AI-Based ETH/USDT Price Prediction Model
- RNN for Cryptocurrency Market Analysis
Attributes
Original Data Source: Cryptocurrency Time-Series Forecasting Project
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