akotovets1 / cryptocurrencies

Unsupervised Machine Learning and Cryptocurrencies

Cryptocurrencies

Unsupervised Machine Learning and Cryptocurrencies

Overview

The investment bank, Accountability Accounting, is interested in offering a new cryptocurrency investment portfolio to its clients. The management needs assistance in determining which cryptocurrencies should be offered. So they requested a report that includes what cryptocurrencies are on the trading market and if they could be grouped to create a classification system for this new investment.

The data getting from the company needed to be cleaned in order to fit the machine learning models, and because there isn't a known output, unsupervised machine learning was used.

For grouping the cryptocurrencies, a clustering algorithm was used and the data was visualized so the result could be shared with the management of the company.

Results

The imported DataFrame before cleaning:

The list of cryptocurrencies after cleaning:

K-means Clustering Algorithm, Elbow Curve:

An elbow curve was produced in order to find the best value for K. This would be used to determine the number of clusters that should be used in the K-Means clustering algorithm. According to the elbow curve, k=4

3D Scatterplot with Clusters, Visualizing Tradable Cryptocurrencies:

The unsupervised machine learning algorithm PCA was used to reduce the dimensions of the cryptocurrency components to three principal components. 3D scatter chart shows the cryptocurrency dataset in four clusters.

Tradable Cryptocurrencies:

2D scatter chart shows each cryptocurrency from the dataset as it related to total coins mined and total coin supply.