ALovettII / 10-challenge

Jupyter notebook that clusters cryptocurrencies by their performance in different time periods & visulizes these clusters

10-challenge

Jupyter notebook that clusters cryptocurrencies by their performance in different time periods & visulizes these clusters

Technologies

• import pandas as pd
• import hvplot.pandas
• from path import Path
• from sklearn.cluster import KMeans
• from sklearn.decomposition import PCA
• from sklearn.preprocessing import StandardScaler

Installation Guide

Using the Conda package manager: My GitHub Project

You will also the following libraries:

# Activate your Conda dev environment
conda activate dev

# Install scikit-learn
pip install -U scikit-learn

# Install hvPlot
conda install -c pyviz hvplot

Usage

Running this program will allow the following:

• Import the Data (provided in the starter code)
• Prepare the Data (provided in the starter code)
• Find the Best Value for k Using the Original Data
• Cluster Cryptocurrencies with K-means Using the Original Data
• Optimize Clusters with Principal Component Analysis
• Find the Best Value for k Using the PCA Data
• Cluster the Cryptocurrencies with K-means Using the PCA Data
• Visualize and Compare the Results

The program should yield such results as:

By changing the imported CSV you will be able to run this analysis on other data sets.

Contributors

Created by Arthur Lovett