Using unsupervised machine learning to classify a portfolio of currently tradable cryptocurrencies

• crypto_data.csv
• Jupyter Notebook
• Libraries: pandas, numpy, hvplot, plotly, sklearn

1 - The crypto_data.csv dataset was cleaned and preprocessed in order to perform PCA (Principal Component Analysis).

2 - The resulting dataframe was reduced to 3 dimensions using PCA.

3 - K-Means analysis was performed using the PCA data to determine the number of clusters (4) and create a prediction model.

4 - Visualizations were created to show:

• K-means clustering in 3D

• a table sortable by feature

• a scatterplot of coin supply vs coins mined

The completed code crypto_clustering.ipynb is available here