Columbia AI Module 11 Challenge: CryptoClustering Unsupervised

Requirements

Find the Best Value for k Using the Original Scaled DataFrame (15 points)

• Code the elbow method algorithm to find the best value for k. Use a range from 1 to 11. (5 points)
• Visually identify the optimal value for k by plotting 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)

Cluster Cryptocurrencies with K-Means Using the Original Scaled Data (10 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 pandas’ plot, create a scatter plot by setting x="price_change_percentage_24h" and y="price_change_percentage_7d". (4 points)

Optimize the Clusters with Principal Component Analysis (10 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)

Find the Best Value for k by Using the PCA Data (10 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)
• Visually identify the optimal value for k by plotting 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)

Cluster the Cryptocurrencies with K-Means by Using the PCA Data (10 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 pandas’ plot, create a scatter plot by setting x="PC1" and y="PC2". (4 points)

Determine the Weights of Each Feature on Each Principal Component (15 points)

• Create a DataFrame that shows the weights of each feature (column) for each principal component by using the columns from the original scaled DataFrame as the index. (10 points)
• Answer the following question: Which features have the strongest positive or negative influence on each component? (5 points)

Coding Conventions and Formatting (10 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)

Deployment and Submission (10 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)

Code Comments (10 points)

• Be well commented with concise, relevant notes that other developers can understand. (10 points)