CryptoClustering

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. The first five rows of the scaled DataFrame should appear as follows: The first five rows of the scaled 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.
• Answer the following question in your notebook: What is the best 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:

• Initialise 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 "PC1" and the y-axis as "PC2".
• Colour 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.

Optimise 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 and then answer the following question in your notebook:
• What is the total explained variance of the three principal components?
• 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.
• The first five rows of the PCA DataFrame should appear as follows: The first five rows of the PCA 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.
• Answer the following question in your notebook:
• What is the best value for k when using the PCA data?
• Does it differ from the best k value found using the original data?

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:

• Initialise 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:
• Set the x-axis as "price_change_percentage_24h" and the y-axis as "price_change_percentage_7d".
• Colour 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.
• Answer the following question:
• What is the impact of using fewer features to cluster the data using K-Means?