In this assignment, I'll assume the role of an advisor in one of the top five financial advisory firms in the world. Competitors are fierce, so I want to propose a novelapproach to assembling investment portfolios that are based on cryptocurrencies. Instead of basing my proposal on only returns and volatility, I want to include other factors that might impact the crypto market—leading to better performance for my portfolio. When I presented the idea, my manager loves it! So, I am asked to create a prototype for submitting my crypto portfolio proposal to the companyboard of directors. I'll work primarily with the K-means algorithm, one of the most popular unsupervised learning algorithms that groups similar data into clusters. I'll build on this by speeding up the process using principal component analysis (PCA), which employs many different features.
I’ll combine my financial Python programming skills with the new unsupervised learning skills that I've acquired in this module. I’ll create a Jupyter notebook that clusters cryptocurrencies by their performance in different time periods. I’ll then plot the results so that youcan visually show the performance to the board. The provided CSV file contains the price change data of cryptocurrencies in different periods.
#From Module 4 Challenge "Instructions"
- Find the Best Value for k by Using the Original Data
- Cluster the Cryptocurrencies with K-Means by Using the Original Data
- Optimize the Clusters with Principal Component Analysis
- Find the Best Value for k by Using the PCA Data
- Cluster the Cryptocurrencies with K-means by Using the PCA Data
- Visualize and Compare the Results
- Run through each line of code to view the output
