I used my knowledge of Python and unsupervised learning to predict if cryptocurrencies are affected by 24-hour or 7-day price changes.

• I used the StandardScaler() module from scikit-learn to normalize the data from the CSV file.

• I created a DataFrame with the scaled data and set the "coin_id" index from the original DataFrame as the index for the new DataFrame.

• I used the elbow method to find the best value for k using the following steps:
• Created a list with the number of k values from 1 to 11.
• Created an empty list to store the inertia values.
• Created a for loop to compute the inertia with each possible value of k.
• Created a dictionary with the data to plot the elbow curve.
• Plotted a line chart with all the inertia values computed with the different values of k to visually identify the optimal value for k.

• Answered the following question in my notebook: What is the best value for k? Answer: 4

• I used the following steps to cluster the cryptocurrencies for the best value for k on the original scaled data:
• Initialized the K-means model with the best value for k.
• Fit the K-means model using the original scaled DataFrame.
• Predicted the clusters to group the cryptocurrencies using the original scaled DataFrame.
• Created a copy of the original data and add a new column with the predicted clusters.
• Created a scatter plot using hvPlot as follows: Set the x-axis as "PC1" and the y-axis as "PC2".
• Colored the graph points with the labels found using K-means.
• Added the "coin_id" column in the hover_cols parameter to identify the cryptocurrency represented by each data point.

• I used the original scaled DataFrame, to perform a PCA and reduced the features to three principal components.
• I retrieved the explained variance to determine how much information can be attributed to each principal component
• Created a new DataFrame with the PCA data and set the "coin_id" index from the original DataFrame as the index for the new DataFrame.

• I used the elbow method on the PCA data to find the best value for k using the following steps:
• Created a list with the number of k-values from 1 to 11.
• Created an empty list to store the inertia values.
• Created a for loop to compute the inertia with each possible value of k.
• Created a dictionary with the data to plot the Elbow curve.
• Plotted a line chart with all the inertia values computed with the different values of k to visually identify the optimal value for k.

• 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?

• I used the following steps to cluster the cryptocurrencies for the best value for k on the PCA data:
• Initialized the K-means model with the best value for k.
• Fit the K-means model using the PCA data.
• Predicted the clusters to group the cryptocurrencies using the PCA data.
• Created a copy of the DataFrame with the PCA data and add a new column to store the predicted clusters.
• Created 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".
• Colored the graph points with the labels found using K-means.
• Added the "coin_id" column in the hover_cols parameter to identify the cryptocurrency represented by each data point.

• What is the impact of using fewer features to cluster the data using K-Means?

Answer: The number of clusters (k) as shown in the elbow curve plots was not affected using fewer features. For cryptocurrency clusters, group 1 and 3 are very distinct from the rest of the cryptocurrencies, while group 0 and 2 are similiar. The use of fewer features to cluster data using K-means, helped reduce the amount of noise in the cryptocurrency clusters with pca, thereby making the grouping of the data more clear and readable.