https://github.com/margaretkhendre/crypto-clustering-vs-unsupervised-machine-learning-challenge

In this repository, Google Colaboratory is paired with Unsupervised Machine Learning to predict if cryptocurrencies are affected by 24-hour or 7-day price changes.
https://github.com/margaretkhendre/crypto-clustering-vs-unsupervised-machine-learning-challenge

colab-notebook holoviews hvplot jupyter-notebook pandas pathlib python sklearn-library unsupervised-machine-learning

Last synced: 4 months ago
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In this repository, Google Colaboratory is paired with Unsupervised Machine Learning to predict if cryptocurrencies are affected by 24-hour or 7-day price changes.

• Host: GitHub
• URL: https://github.com/margaretkhendre/crypto-clustering-vs-unsupervised-machine-learning-challenge
• Owner: margaretkhendre
• Created: 2023-06-04T02:24:07.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2023-06-08T23:29:56.000Z (about 2 years ago)
• Last Synced: 2025-01-09T09:27:39.948Z (6 months ago)
• Topics: colab-notebook, holoviews, hvplot, jupyter-notebook, pandas, pathlib, python, sklearn-library, unsupervised-machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 19.8 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Unsupervised Machine Learning Challenge

### Prepare the Data

- Use the StandardScaler() module from scikit-learn to normalize the data from the CSV file.

- Create a DataFrame with the scaled data and set the "coin_id" index from the original DataFrame as the index for the new DataFrame.



### Find the Best Value for k Using the Original Scaled DataFrame

Use the elbow method to find the best value for k using the following steps:

- Create a list with the number of k values from 1 to 11.

- Create an empty list to store the inertia values.

- Create a for loop to compute the inertia with each possible value of k.

- Create a dictionary with the data to plot the elbow curve.

- Plot a line chart with all the inertia values computed with the different values of k to visually identify the optimal value for k.



### Cluster Cryptocurrencies with K-means Using the Original Scaled Data

Use the following steps to cluster the cryptocurrencies for the best value for k on the original scaled data:

- Initialize the K-means model with the best value for k.

- Fit the K-means model using the original scaled DataFrame.

- Predict the clusters to group the cryptocurrencies using the original scaled DataFrame.

- Create a copy of the original data and add a new column with the predicted clusters.

- Create a scatter plot using hvPlot as follows:

  - Set the x-axis as "price_change_percentage_24h" and the y-axis as "price_change_percentage_7d".

  - Color the graph points with the labels found using K-means.

  - Add the "coin_id" column in the hover_cols parameter to identify the cryptocurrency represented by each data point.



## Optimize Clusters with Principal Component Analysis

- Using the original scaled DataFrame, perform a PCA and reduce the features to three principal components.

- Retrieve the explained variance to determine how much information can be attributed to each principal component

- Create a new DataFrame with the PCA data and set the "coin_id" index from the original DataFrame as the index for the new DataFrame.



### Find the Best Value for k Using the PCA Data

Use the elbow method on the PCA data to find the best value for k using the following steps:

- Create a list with the number of k-values from 1 to 11.

- Create an empty list to store the inertia values.

- Create a for loop to compute the inertia with each possible value of k.

- Create a dictionary with the data to plot the Elbow curve.

- Plot a line chart with all the inertia values computed with the different values of k to visually identify the optimal value for k.



### Cluster Cryptocurrencies with K-means Using the PCA Data

Use the following steps to cluster the cryptocurrencies for the best value for k on the PCA data:

- Initialize the K-means model with the best value for k.

- Fit the K-means model using the PCA data.

- Predict the clusters to group the cryptocurrencies using the PCA data.

- Create a copy of the DataFrame with the PCA data and add a new column to store the predicted clusters.

- Create a scatter plot using hvPlot as follows:

  - Color the graph points with the labels found using K-means.

  - Add the "coin_id" column in the hover_cols parameter to identify the cryptocurrency represented by each data point.