terryhill89 / CryptoClustering

CryptoClustering

In this challenge, you’ll use your knowledge of 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.

  • The first five rows of the scaled DataFrame should appear as follows:
  

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?
    • Elbow Curve: The best value for k 4. (k = 4) 4 clusters

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:

• 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 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?
    • Variance total is (0.37+0.35+0.18)*100 = 90%.
• 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:

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?
    • k = 4
  • Does it differ from the best k value found using the original data?
    • No

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:

• 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 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.
• Answer the following question:
  • What is the impact of using fewer features to cluster the data using K-Means?
    • The clusters are more clear when you reducing or using fewer features but the number of cluster do not change.

Resources

• https://stackoverflow.com/questions/69596239/how-to-avoid-memory-leak-when-dealing-with-kmeans-for-example-in-this-code-i-am
• https://realpython.com/python-pathlib/
• https://ucdvirtdatapt-gq76002.slack.com/archives/C04935D73AS/p1682568041982859
  • Examples using sklearn.cluster.Birch: Compare BIRCH and MiniBatchKMeans Compare BIRCH and MiniBatchKMeans Comparing different clustering algorithms on toy datasets Comparing different clustering
• https://bokeh.org/