Applied understanding of the K-means algorithm and principal component analysis (PCA) to classify cryptocurrencies according to their price fluctuations across various timeframes

Applies understanding of the K-means algorithm and principal component analysis (PCA) to classify cryptocurrencies according to their price flucuations across various timeframes and specifically, examines price changes over 24 hours, 7 days, 30 days, 60 days, 200 days, and 1 year. Uses PCA to determine which data attribute have the greatest influence on component analysis outcomes.

The primary skills demonstrated include the ability to:

• Use the standardScaler() module from scikit-learn to normalize CSV files
• Optimize data using principal component analysis (PCA)
• Find best k-value using scaled data and PCA data
• Cluster data using K-Means
• Find best k-value using PCA data
• Determine weighing of principal components

Python Libraries

• pandas
• KMeans (from sklearn.cluster)
• PCA (from sklearn.decomposition)
• StandardScalar (from sklearn.preprocessing)

Jamie Bond | [email protected] | Connect on Linkedin

Adapted from instructions starter files provided by The Artificial Intelligence Boot Camp at UNC Charlotte, including:

• Data: crypto_market_data.csv
• Instructions: Crypto_clustering_starter_code.ipynb