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.
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Use the StandardScaler() module from scikit-learn to normalize the data from the CSV file.
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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:
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
- Answer the following question in your notebook: What is the best value for k?
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.
- 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%.
- 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:
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
- What is the best value for k when 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.
- What is the impact of using fewer features to cluster the data using K-Means?
- 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/