This project requires Python and the following Python libraries installed:
- NumPy
- Pandas
- matplotlib
- scikit-learn
- [fastai]
- [pytorch]
You will also need to have software installed to run and execute a Jupyter Notebook
If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included.
The source code is divided into multiple sections following the machine learning design pattern of : Data Exploration, Training, Testing and Hyperparameter Optimization. You can view the precompiled version of the notebook or you can rerun the entire notebook. The datasets are made available on public S3 Buckets. Running the notebook, will automatically download the datasets for you.
In a terminal or command window, navigate to the top-level project directory boston_housing/
(that contains this README) and run one of the following commands:
ipython notebook BitcoinPredictionRNN.ipynb
or
jupyter notebook BitcoinPredictionRNN.ipynb
This will open the Jupyter Notebook software and project file in your browser.
BitCoin Price Data from Jan 2015- August 2018. The prices are as per coinbase cryptoexchange. There were many missing values and forward strategy was used to fill these missing values.
Features BitCoin Price Data from Jan 2015- August 2018
Target Variable
Close Price
: Close price of Bitcoin for each day