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A data analysis and clustering process for a cryptocurrency market dataset using Python and unsupervised learning to predict if cryptocurrencies are affected by 24-hour or 7-day price changes.
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Prepare the Data Use the StandardScaler() module from scikit-learn to normalize the data from the CSV file.
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Find the Best Value for k Using the Original Scaled DataFrame. Use the elbow method to find the best value for k.
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Cluster Cryptocurrencies with K-means Using the Original Scaled Data.
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Optimize Clusters with Principal Component Analysis.
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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.
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Cluster Cryptocurrencies with K-means Using the PCA Data.
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Visualize and Compare the Results.
