Utilizes k-means clustering, the elbow method and principal component analysis to find significant clustering in crypto currency data before and after dimensional reduction

Language: Python 3.9.12

Libraries used:

Pandas - For the creation and visualization of Data Frames

Jupyter Labs - An ipython kernel for interactive computing in python

PyViz hvPlot - A high level python library for interactive data visualization

SKLearn - Simple and effective python library for predictive data analysis

If you are using an anaconda or a conda environment chances are pandas, hvplot and jupyter labs are already installed in your virtual environment. In addition, installing scikit learn will be necessary

For a full install activate a conda development environment and run in GitBash if not already installed:

    conda install pandas
    conda install jupyterlab
    conda install -c pyviz hvplot

To install the other dependencies not included in the anaconda environment run:

    pip install -U scikit-learn

Check the to make sure everything has been installed properly

    conda list pandas
    conda list hvplot
    conda list jupyter lab
    conda list scikit-learn

To run this jupyter lab notebook you will need to use GitBash and navigate to where you have exported the files associated with this project and activate your dev environment. Next, this project can be ran by navigating to the crypto_investments.ipynb jupyter notebook file and clicking the double arrow as seen below:

This will run the jupyter notebook and each cell has proper pseudocode directing the viewer as to which analyses are being shown. Additionally, the markdown cells provided also clue the reader in to what is going on.

There are a few interactive graphs along the way.

Created by Silvano Ross while in the UW FinTech Bootcamp

Contact Info: email: [email protected] | GitHub | LinkedIn

MIT