CryptoClustering

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

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.

• Find the Best Value for k Using the 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 Scaled DataFrame

• Use the following steps to cluster the cryptocurrencies for the best value for k on the scaled DataFrame:
  • Initialize the K-means model with the best value for k.
  • Fit the K-means model using the scaled DataFrame.
  • Predict the clusters to group the cryptocurrencies using the scaled DataFrame.
  • Create a copy of the scaled DataFrame and add a new column with 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".
    • Color 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.

Optimize 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 scaled PCA data and set the "coin_id" index from the original DataFrame as the index for the new DataFrame.

• Find the Best Value for k Using the PCA DataFrame

• Use the elbow method on the scaled PCA DataFrame 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 scaled PCA DataFrame?
    • Does it differ from the best k value found using the original scaled DataFrame?

Cluster Cryptocurrencies with K-means Using the PCA DataFrame

• Use the following steps to cluster the cryptocurrencies for the best value for k on the PCA DataFrame:
  • Initialize the K-means model with the best value for k.
  • Fit the K-means model using the scaled PCA DataFrame.
  • Predict the clusters to group the cryptocurrencies using the scaled PCA DataFrame.
  • Create a copy of the scaled PCA DataFrame and add a new column to store the predicted clusters.
  • Create a scatter plot using hvPlot as follows:
  • Set the x-axis as "PC1" and the y-axis as "PC2".
  • Color 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?