This project delves into machine learning with crypto. The project has multiple components where models are setup and are fit. In essance, we apply the unsupervised learning technique of K-Means clustering to group cryptocurrencies by their performance in an effort to create profitable portfolio recommendations. You are able to see the different elbow plots, which are used to then find the k value for the KMeans scatter plots for original and PCA models respectively.
Original:
Then, using the k value from the original elbow plot, we use KMeans model to produce a scatter plot and visualize the inertia.
PCA:
Then, using the k value from the PCA elbow plot, we use KMeans model to produce a scatter plot and visualize the inertia.
From the scatter plots, we can see that the inertia using PCA is lesser than with the original dataset.
If you would like to clone the repository, type "git clone https://github.com/kheller18/crypto-machine-learning.git". In the terminal, with the conda dev environment activated, install the following packages and dependencies before running the crime analysis application. To understand how to install these, refer to the Usage
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csv - Used to store all of our SPD data
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Jupyter Lab - version 3.4.4 - Used to create and share documents that contain live code, equations, visualizations and narrative text.
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matplotlib - For the visualization of crime data.
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pandas - For the analysis of crime data.
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pathlib - version 1.0.1 - This was used to locate through the directory or file path.
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PyVizlot - Python visualization package that provides a single platform for accessing multiple visualization libraries. Our most heavily used library is:
- hvplot.pandas - version 0.7.2 - For the interactive visualization of the crime data.
After cloning the repository locally, you'll need to have the packages listed in Installation installed on your machine. To do so, you'll need to activate your conda dev environment and running the following commands:
```
pip install pandas
pip install hvplot
pip install jupyterlab
conda install -c pyviz hvplot
```
After all of these are installed, please refer to the Deployment section for instructions on how to view or edit the notebook.
MIT License
Copyright (c) 2022 Keenan Heller
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- There are currently no tests associated with this project.
- There is currently no live deployment of this notebook on a common server, but the user has the ability to run this notebook locally on their machine via two different ways:
Jupyter Lab: Navigate to the root directory and type "jupyter lab crypto_investments.ipynb"