Goals • Dataset • Tools Used
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 we're 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 Martha is looking for, she has decided to use unsupervised learning. To group the cryptocurrencies, Martha decided on a clustering algorithm. She’ll use data visualizations to share her findings with the board.
In order to deliver, we took the following steps:
- Preprocessed the Data for PCA
- Reduced Data Dimensions Using PCA
- Clustered Cryptocurrencies Using K-means
- Vizualized Cryptocurrency Results
Data set retrieved from CryptoCompare, a website that contains Cryptocurrency trading data.
- Crypto Data: CSV file containing 1,252 Cryptocurrency records
- Python: Programming language used to build app to automate election audit
- Scikit Learn: Python library with classification, regression and clustering algorithms
- Plotly: Python graphing library that makes interactive, publication-quality charts
- hvPlot: High-level Python plotting library that uses HolovViews and Bokeh