https://github.com/andrewjmack/cryptoclustering

The purpose of this project is to utilize knowledge of Python and unsupervised learning to predict if cryptocurrencies are affected by 24-hour or 7-day price changes. Methods for analysis include K-Means clustering and dimensional reduction through Principal Component Analysis ("PCA").
https://github.com/andrewjmack/cryptoclustering

jupyter-notebook pandas python scikit-learn

Last synced: about 2 months ago
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The purpose of this project is to utilize knowledge of Python and unsupervised learning to predict if cryptocurrencies are affected by 24-hour or 7-day price changes. Methods for analysis include K-Means clustering and dimensional reduction through Principal Component Analysis ("PCA").

• Host: GitHub
• URL: https://github.com/andrewjmack/cryptoclustering
• Owner: andrewjmack
• Created: 2024-06-29T15:47:44.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-07-23T22:49:57.000Z (6 months ago)
• Last Synced: 2024-07-24T01:52:52.859Z (6 months ago)
• Topics: jupyter-notebook, pandas, python, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 1.2 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# CryptoClustering

Univ of Denver: Data Analytics | July 2024 | Andrew Mack

### Summary

The purpose of this project is to utilize knowledge of Python and unsupervised learning to predict if cryptocurrencies are affected by 24-hour or 7-day price changes.

The following stages were involved:

- Finding the best value for k after scaling a dataframe

- Clustering the cryptocurrencies with K-means using the original scaled data

- Optimizing the clusters with Principal Component Analysis ("PCA")

- Finding the best value for k using the PCA data

- Cluster the cryptocurrencies with K-means using the PCA data

### Contents

This repository includes the original data in a .csv file and a Jupyter Notebook in which the .csv data was transformed and the analysis occurred:

-   Crypto_Clustering.ipynb

-   Resources/crypto_market_data.csv

-   README.md

### References

- Data for this dataset was generated by edX Boot Camps LLC, and is intended for educational purposes only.