- Python 3.9.0,
- Anaconda Navigator 1.9.12,
- Jupyter notebook 6.0.3,
- Pandas, Sklearn.
the purpose of this analysis is to help an Investment firm interested in offering a cryptocurrency portfolio set a classification system for the cryptocurrencies on the trading market, we were given data from CryptoCompare , and we will be using an unsupervised Model.
After cleaning the data we create a 3d representation of the different clusters the data groups into :
we compare the different cryptocurrencies based on total coins mined and the total coins supply :
- we can see that the cryptocurrencies break into 4 groups, with 2 of them containing most of the data.
- we can see that the other 2 groups contain 1 example each; the question is can those be considered outliers or are they significant to this analysis? I think the next step would be to further explore what makes the 2 last groups different.