zonaj2/cryptoclustering

CryptoClustering

Table of Contents

• About
• Key Steps
• Analysis

About

Used Python and unsupervised machine learning to categorize and group cryptocurrencies that exhibit similar historical price change patterns.

Key Steps

1. Prepared the data. Used StandardScalar for data normalization.
2. Found the Best Value for k Using the Original Scaled DataFrame.
3. Clustered Cryptocurrencies with K-means Using the Original Scaled Data.
4. Optimized Clusters with Principal Component Analysis.
5. Found the Best Value for k Using the PCA Data
6. Cluster Cryptocurrencies with K-means Using the PCA Data

Analysis

What is the best value for k?
In both data sets 4 is the best k value.

What is the impact of using fewer features to cluster the data using K-Means?
The following observations are made for the clusters using reduced features:

• All clusters are more defined.
• Clusters 0 and 2 are packed together more tightly.
• Clusters 1 and 3 are a further distance away from Clusters 0,2 compared with the original data that had more features.