This notebook analyzes crypto investments and utilizes the KMeans to identify the clusters. Below are the steps that will be performed in this notebook.
- Cluster Cryptocurrencies with K-means
- Find the Best Value for k
- Optimize Clusters with Principal Component Analysis
- Visualize the Results
- This project leverages python version 3.8.5
- sklearn Library
- scikit-learn
- Project will be accomplished in JupyterLab
Installing Jupyter notebook
- On the terminal (Git Bash) under the conda dev environment, type the code below:
pip install jupyterlab
- To open the Jupyter notebook Open a new Git Bash and type the below command into your conda dev environment:
jupyter lab
- then hit the ENTER key to run
- pandas - data manipulation and analysis
- hvPlot - enables interactive plotting tools such as line and bar graphs
- KMeans - unsupervised machine learning algorith
- PCA - statistical technique to speed up machine learning algorithms when too many dimensions exist
- StandardScaler - removes the mean and scales each variable to unit variance
- 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
To use the JupyterLab notebook clone the repo and run git bash, open notebook crypto_investments.ipynb
Zach Zwiener
Email - zachzwiener3@gmail.com
MIT