Unsupervised Machine Learning: A novel approach to investment portfolios based on cryptocurrencies
![<img src="[http://url/image.png" style=](https://github.com/DigitalGoldRush/Module-5-Challenge-/blob/main/money-tree%20jpeg%20from%20unsplash.com.jpeg width:10px ; height:10px " >](https://github.com/DigitalGoldRush/Module_10_Challenge/raw/main/Resources/guerrillabuzz-crypto-pr-p9DzBzWhMls-unsplash.jpg)
Building a crypto-based financial prototype using:
Evaluate crypto performance over different periods
-
Data Normalization with Standard Scaler
-
Clustering with K-Means & PCA
-
HV PLot for Comparative Visualizations
![Python](https://camo.githubusercontent.com/da61644bd3eaab259301d07ac5db40362ae2a890427d61745ba3264063af1c37/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f507974686f6e2d332e392e31322d626c7565)
![Anaconda](https://camo.githubusercontent.com/ca8ee16f3ff90cf3349be99b021b0b2a366cd71499ef20bcdc2b43dbc2668483/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f416e61636f6e64612d2532333434413833332e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d616e61636f6e6461266c6f676f436f6c6f723d7768697465)
![Jupyter Notebook](https://camo.githubusercontent.com/d49a17996c258332a76b3f31dab7eb818985ba4d3359c519a3a24a5122c72eda/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6a7570797465722d2532334633373632362e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d6a757079746572266c6f676f436f6c6f723d7768697465)
![DjangoREST](https://camo.githubusercontent.com/d33cec6fdebc03de59cd58755f1d4e6d6092ef1412dd5fde6fc1775911f5044b/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f5079446174612d6876506c6f742d6666313730393f7374796c653d666f722d7468652d6261646765266c6f676f436f6c6f723d776869746526636f6c6f723d666631373039266c6162656c436f6c6f723d67726179)
![DjangoREST](https://camo.githubusercontent.com/aa3fb149bcf93af5451c3860e277ae2f892bf81b8777bce5be32689e538c61d5/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7363696b69742d6c6561726e2d6636623236623f7374796c653d666f722d7468652d6261646765266c6f676f436f6c6f723d776869746526636f6c6f723d666631373039266c6162656c436f6c6f723d67726179)
![Pandas](https://camo.githubusercontent.com/05cab52d05663cecbe47a23ca71075ba81b9080dd50561d0f76eb46e902cfef8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f70616e6461732d2532333135303435382e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d70616e646173266c6f676f436f6c6f723d7768697465)
![GitHub](https://camo.githubusercontent.com/410d86e43f847d3f6e3027fa6f0c2fb7641d893fa601d863a943eac968c41890/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6769746875622d2532333132313031312e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d676974687562266c6f676f436f6c6f723d7768697465)
1. The easiest way to install JupyterLab is to download the Anaconda application.
![Anaconda-Server Badge](https://camo.githubusercontent.com/06277085caa77d196c05b76608d1e62d76f13282c4d8770592fd07dac48e2b0d/68747470733a2f2f616e61636f6e64612e6f72672f636f6e64612d666f7267652f6d61726b646f776e2d696e636c7564652f6261646765732f696e7374616c6c65722f636f6e64612e737667)
-
To install Juypter Lab with pip:
-
Then install Jupyter Notebook with pip:
2. SciKit can be installed with pip:
$ pip install -U scikit-learn
-
Or using the conda command
$ conda create -n sklearn-env -c conda-forge scikit-learn
$ conda activate sklearn-env
3. To install Pandas create a new (Python 3) notebook in Jupyter Notebook and then in the prompt enter:
4. Recommended way to install hvPLot is using the conda command
conda install -c pyviz hvplot
Pandas has many features, which was used in the code creation, and can be found at the link above.
hvPlot has many features, which was used in the code creation, and can be found at the link above.
![Python](https://camo.githubusercontent.com/a069d9678a7db9ba3f8eac51320a10deeb493189f2ddc2627b51e06a5c524258/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4d69636861656c5f44696f6e6e652d4c696e6b6564496e2d626c7565)