Crypto Portfolio Clustering

Crypto Portfolio Clustering with and without optimization techniques (elbow method, PCA).

Analysis

This is an anlysis of crypto market data, specifically the price-change data, for several crypto tokens. Two different KMeans unsupervised learning approaches were used to analyze this data, and come up with two different cluster plots. The first approach uses the original data, with standard scaling, and the second approach uses PCA - Principal Component Analysis to first reduce the features and then generate a model. In both cases, the Elbow Method was used to choos a k value for the number of clusters.

See full analysis details in the notebook crypto_investments.ipynb

DataSet

A single dataset was used to anlyze this space:

• crypto_market_data.csv for price-change data over various time-periods
  • contains data: coin_id | price_change_percentage_24h | price_change_percentage_7d | price_change_percentage_14d | price_change_percentage_30d | price_change_percentage_60d | price_change_percentage_200d | price_change_percentage_1y

Assumptions

• Two KMeans algorithm analyses will occur, one with original feature data, one with PCA optimized data.
• Original: will use price_change_percentage_24h and price_change_percentage_7d for the cluster plot.
• PCA analysis will reduce to 3 components n_components = 3
• Each analysis will use the Elbow Method to arrive at the k-value for the KMeans algorithm

Summary

Original Feature Data
TBD

PCA Data
TBD

General Summary

Below are screen caps showing the comparison for the Elbow Methods and resulting cluster plots when using the original standard-scaled vs PCA optimizations.

See full analysis implementation, interactive charts, and maps in the notebook crypto_investments.ipynb

Technologies

This challenge uses python 3.7 and the following built-in modules:

• os

Additionally, it requires:

• matplotlib
• pandas
• JupyterLab
• hvplot
• scikit-learn

See installation below for specifics.

Installation

You will need Python 3.7, that supports for this application to run. An easy way to install python 3.7 is to download and install Anaconda. After installing anaconda, open a terminal/command-prompt, and setup a python 3.7 environment, and then activate it like so:

# create an anaconda python 3.7 environment
# name can be any friendly name to refer to your environment, eg 'dev'
conda create --name dev python=3.7 anaconda

# activating the environment
conda activate dev

# use pip to install the above modules, eg:
pip install python-dotenv
...etc...

Usage

The analysis is presented within a JupyterLab notebook. To launch JupyterLab, from the root of this repo dirctory:

# within repo root 
$ jupyter lab

You can now open the notebook crypto_investments.ipynb

Contributors

David Lopez

License

MIT