A prominent investment bank, is interested in offering a new cryptocurrency investment portfolio for its customers. The company, however, is lost in the vast universe of cryptocurrencies. So, they’ve asked me to create a report that includes what cryptocurrencies are on the trading market and how they could be grouped to create a classification system for this new investment.
The data I will be working with is not ideal, so it will need to be processed to fit the machine learning models. Since there is no known output for what the investment bank is looking for, I have decided to use unsupervised learning. To group the cryptocurrencies, I will use clustering algorithm. Then present the data visualizations to share my findings with the board.
Data Sources: Crypto_data.csv
Code Files: crypto_clustering.ipynb
Software: Numpy 1.19.2, Numby-base 1.19.2, Numpy doc 1.1.0, scipy 1.5.2, scikit-learn 0.23.2, imbalanced-learn, Visual Studio Code 1.56.0, jupyter Notebook 6.3.0, Python 3.7.6 , Anaconda 4.8.5, pltly 5.1.0, hvplot 0.7.3.
From the above visualized crypto data, I have created classification system for the new investment.
The first graph shows an outlier for crypto class 3 and class 2. This could imply a strong or weak crypto currency.
Class one and zero appears to have similar characterisitics as seen in the 3D scatter plot.
Nnaemeka Enukorah
21st August,2021