This is a Python project that predicts the cryptocurrency volume using supervised learning. The data is graphed to show the predicted volume vs the previous cryptocurrency volume.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
This project requires the following Python modules:
Matplotlib,Pandas,Scikit-learn
Use the package manager pip to install the required modules:
pip install matplotlib pandas scikit-learn
To use this project, simply run the crypto.py script:
python crypto.py This will load the data, train the model, and generate the predicted vs actual volume graph.
The dataset used in this project is the Cryptocurrency Historical Prices dataset, which contains historical prices for various cryptocurrencies. Dataset source here https://www.kaggle.com/datasets/sudalairajkumar/cryptocurrencypricehistory?resource=download
The supervised learning model used in this project is the Linear Regression model from the Scikit-learn library. This model is trained on the previous cryptocurrency volume data to predict the future volume.
The graph shows the predicted volume vs the actual volume for the cryptocurrency. This graph is generated using the Matplotlib library.
Cryptocurrency Historical Prices dataset Scikit-learn library Matplotlib library Pandas library