mlachha/cryptocurrencies

Cryptocurrencies

Resources

• Python 3.9.0,
• Anaconda Navigator 1.9.12,
• Jupyter notebook 6.0.3,
• Pandas, Sklearn.

Overview

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.

Results

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 :

Summary

• 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.