The purpose of this analysis was to use data from crypto_data.csv to provide a report and visualization of currently traded cryptocurrencies that can be grouped together to create a new classification system. This report would be used to help Accountability Accounting offer a new investment portfolio in the exciting world of cryptocurrency to its customers.
Since the data does not have any known outcome, we needed to preprocess it to fit an unsupervised Machine Learning model that will enable us to run a clustering algorithm that will allow us to group the cryptocurrencies.
In this analysis we learned and applied:
- Data Preprocessing (Selection, Transformation, Scaling) - the process of helping to prepare data for
Machine LearningAlgorithms. - Elbow Curve - method to determine the best number of clusters needed for the algorithm to group the objects by.
- Principal Component Analysis (
PCA) - statistical technique to speed up machine learning algorithms when the number of features is too high. - Clustering Algorithms(
KMeans) - the process of grouping similar objects/data points into clusters. - Visualization (
hvPlot,Plotly) - graphic libraries that allows us to create 2D and 3D graphs such as, scatter plots.
The original dataset contained 1,252 entries, however only 1,144 cryptocurrencies were currently trading. The data was further munged to remove null values and only leave cryptocurrencies that had a total number of mined coins greater than 0. The final results identified 532 tradable cryptocurrencies.
The Elbow Curve method showed the slope at 4. This is the number of clusters that was used for the KMeans algorithm.
The clusters are plotted in a 3D scatter plot for visualization.
- Dataset from crypto_data.csv
- Software: Python 3.7.9, Anaconda 4.9.2 and Jupyter Notebooks 6.1.4
- Libraries: Scikit-learn, Plotly, hvPlot, Pandas