Cryptocurrencies

Unsupervised Machine Learning and Cryptocurrencies

Purpose:

A client asked for a list of tradable cryptocurrencies and wants to be able to pick them from a classification system.

1. Describe the differences between supervised and unsupervised learning, including real-world examples of each.
2. Preprocess data for unsupervised learning.
3. Cluster data using the K-means algorithm.
4. Determine the best number of centroids for K-means using the elbow curve.
5. Use PCA to limit features and speed up the model.

Results:

The imported DataFrame before cleaning, 1252 rows of data:

The list of tradable cryptocurrencies after cleaning, 532 rows of data:

K-means Clustering Algorithm, Elbow Curve:

Applying the Principal Component Analysis:

3D Scatterplot with Clusters, Visualizing Tradable Cryptocurrencies:

Number of Tradable Cryptocurrencies:

DataFrame to plot results:

Tradable Cryptocurrencies: