• Objective
• Method
• References

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

• Load the crypto_market_data.csv into a DataFrame.
• Obtain summary statistics and plot the data to assess 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.

• Use the elbow method to find the best value for k completing 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 to visually identify the optimal value for k.
  

What is the best value for k? The best value for k would be k=4.

• 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
  

• Use 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.

What is the total explained variance of the three principal components? Total variance is the sum of variances of all individual principal components.

• Use the elbow method on the PCA data to find the best value for k completing 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 to visually identify the optimal value for k.
  

What is the best value for k when using the PCA data? The best value for k when using the PCA data is k=4. Does it differ from the best k value found using the original data? No, it does not differ from the best k value using the original 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
  

• Create composite plot to contrast the Elbow Curves

• Create composite plot to contrast the Clusters

After visually analyzing the cluster analysis results, what is the impact of using fewer features to cluster the data using K-Means? When analyzing the line plots for the elbow curves, the impact of using fewer features to cluster the data, is a steeper inertia drop. When analyzing the scatter plots for the cluster predictions, using fewer features results in tighter clusters and more entries within Cluster 0 and Cluster 1.

• Dataset provided by edX UofT Data Analytics, which had been generated by Trilogy Education Services, LLC. This is intended for educational purposes only.