Unsupervised Learning As an advisor in one of the topfive financial advisory firms in the world. Competitors are fierce, so you want to propose a novel approach to assembling investment portfolios that are based on cryptocurrencies. Instead of basing your proposal on only returns and volatility, you want to include other factors that might impact the crypto market—leading to better performance for your portfolio. You’ll create a Jupyter notebook that clusters cryptocurrencies by their performance in different time periods. You’ll then plot the results so that you can visually show the performance to the board.

Resources

• 👨🏿‍⚖️ Click here to learn more about Unsupervised Learning

• 👨🏿‍⚖️ Click here to learn more about K-means Clustering

K-means Clustering

Below is the Dataset that will be used for task

STEP 1

Prepare Data

👨🏿‍⚖️ We use Standard Scaler to not have bias in Analysis

👨🏿‍⚖️ Click here to learn more about Standard Scaler

STEP 2

Find the Best Value for k Using the Original Data

👨🏿‍⚖️Click here to learn more about Elbow Method

STEP 3

Cluster Cryptocurrencies with K-means Using the Original Data

STEP 4

Optimize Clusters with Principal Component Analysis

👨🏿‍⚖️ Aprox 90% is the total explained variance of the three principal components

👨🏿‍⚖️ Click here to learn more about PCA

STEP 5

Visualize and Compare Results

REQUIREMENTS

👨🏿‍⚖️ Click name to downlaod

Python

Jupyter Lab

Hvplot

Scikit-Learn

INSTALLATION

👨🏿‍⚖️ INSTALL ZIP FILE OR CLONE REPO

click here for zip file

click here to clone repo

LICENSE

Open to Experiment

BY:ROBERT SMITH

CREDIT: UC BERKELEY

EMAIL - [email protected] for Colloboration