This project attempts to use Supervised Learning Classifier model to predict the price of Bitcoin.
The datasets used for this project can be downloaded from Blockchain
The following individual csv files were downloaded housing the datasets: Volume, Volatility, Hash Rate, Mining Difficulty, Average Number of Transactions, Average Block Size, Market Capitalization, Bid/Ask Spread.
Training set has 1436 samples. Testing set has 360 samples.
The libraries used for this project are os, time, functools, numpy, pandas, matplotlib, a multiplicity of sklearn modules,
Several Python scripts are available to train the model:
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preprocess.py: script containing the function that imports the csv files, formats and returns a dataframe. -
visuals.py: script containing various plot functions -
walk_forward.py: script that contains the walk_forward function -
pred.py: script that contains the pred_dict, acc_test, f_test, and pred function -
capstone_btc_project_121417: All development Python code
Report
A final report explaining this project and the surrounding problem domain is available as * report.pdf.
The code for this project is open source and available under the terms of the license in this repository.