In this challenge, you’ll use your knowledge of Python and unsupervised learning to predict if cryptocurrencies are affected by 24-hour or 7-day price changes.
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I used the StandardScaler() module from scikit-learn to normalize the data from the CSV file.
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I created a DataFrame with the scaled data & set the "coin_id" index from the original DataFrame as the index for the new DataFrame
- The first five rows of the scaled DataFrame appear as follows:
- The first five rows of the scaled DataFrame appear as follows:
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I created a plot to visualize the data.
- Code the elbow method algorithm to find the best value for k. Use a range from 1 to 11. (5 points)
- To visually identify the optimal value for k, plot a line chart of all the inertia values computed with the different values of k. (5 points)
- Answer the following question: What’s the best value for k? (5 points)
- Initialize the K-means model with four clusters by using the best value for k. (1 point)
- Fit the K-means model by using the original data. (1 point)
- Predict the clusters for grouping the cryptocurrencies by using the original data. Review the resulting array of cluster values. (3 points)
- Create a copy of the original data, and then add a new column of the predicted clusters. (1 point)
- Using hvPlot, create a scatter plot by setting x="price_change_percentage_24h" and y="price_change_percentage_7d". Color the graph points with the labels that you found by using K-means. Then add the crypto name to the hover_cols parameter to identify the cryptocurrency that each data point represents. (4 points)
- Create a PCA model instance, and set n_components=3. (1 point)
- Use the PCA model to reduce the features to three principal components. Then review the first five rows of the DataFrame. (2 points)
- Get the explained variance to determine how much information can be attributed to each principal component. (2 points)
- Answer the following question: What’s the total explained variance of the three principal components? (3 points)
- Create a new DataFrame with the PCA data. Be sure to set the coin_id index from the original DataFrame as the index for the new DataFrame. Review the resulting DataFrame. (2 points)
- Code the elbow method algorithm, and use the PCA data to find the best value for k. Use a range from 1 to 11. (2 points)
- To visually identify the optimal value for k, plot a line chart of all the inertia values computed with the different values of k. (5 points)
- Answer the following questions: What’s the best value for k when using the PCA data? Does it differ from the best value for k that you found by using the original data? (3 points)
- Initialize the K-means model with four clusters by using the best value for k. (1 point)
- Fit the K-means model by using the PCA data. (1 point)
- Predict the clusters for grouping the cryptocurrencies by using the PCA data. Review the resulting array of cluster values. (3 points)
- Create a copy of the DataFrame with the PCA data, and then add a new column to store the predicted clusters. (1 point)
- Using hvPlot, create a scatter plot by setting x="PC1" and y="PC2". Color the graph points with the labels that you found by using K-means. Then add the crypto name to the hover_cols parameter to identify the cryptocurrency that each data point represents. (4 points)
- Create a composite plot by using hvPlot and the plus sign (+) operator to compare the elbow curve that you created from the original data with the one that you created from the PCA data. (5 points)
- Create a composite plot by using hvPlot and the plus (+) operator to compare the cryptocurrency clusters that resulted from using the original data with those that resulted from the PCA data. (5 points)
- Answer the following question: Based on visually analyzing the cluster analysis results, what’s the impact of using fewer features to cluster the data by using K-means? (5 points)
- Place imports at the top of the file, just after any module comments and docstrings, and before module globals and constants. (3 points)
- Name functions and variables with lowercase characters, with words separated by underscores. (2 points)
- Follow DRY (Don't Repeat Yourself) principles, creating maintainable and reusable code. (3 points)
- Use concise logic and creative engineering where possible. (2 points)
- Submit a link to a GitHub repository that’s cloned to your local machine and that contains your files. (4 points)
- Use the command line to add your files to the repository. (3 points)
- Include appropriate commit messages in your files. (3 points)
- Be well commented with concise, relevant notes that other developers can understand. (10 points)