README.md

Title: CryptoClustering

Creating a machine learning model that groups cryptocurrencies to assemble investment portfolios that are based on the profitability of those cryptocurrencies.

Installation

The following libraries and dependencies were used to successfully run this project:

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

Description: Understanding K-means Algorithm and Principal Component Analysis (PCA)

This Challenge consisted of creating a machine learning model that groups cryptocurrencies. This was done by applying the lessons learned in week 11, specifically the K-means algorithm and PCA to classify cryptocurrencies according to their price fluctuations across various timeframes; and by examining price changes over intervals spanning 24 hours, 7 days, 30 days, 60 days, 200 days, and 1 year.

Methodology

1. Preparing the Data
2. Finding the Best Value for k Using the Original Scaled DataFrame
3. Clustering Cryptocurrencies with K-Means Using the Original Scaled Data
4. Optimizing Clusters with Principal Component Analysis
5. Finding the Best Value for k Using the PCA Data
6. Clustering Cryptocurrencies with K-Means Using the PCA Data
7. Determining the Weights of Each Feature on Each Principal Component

References Used

To complete this project, I relied heavily on class notes and activities provided during week 11.