Michael G. Solomon - Blockchain Data Analytics For Dummies

5 Chapter 7FIGURE 7-1: Clustered customer rating data.FIGURE 7-2: Clustered customer rating data with centroids and colors.FIGURE 7-3: Weak clustered customer rating data.FIGURE 7-4: Weak clustered customer rating data with centroids and colors.FIGURE 7-5: Loan default prediction decision tree.FIGURE 7-6: Normally distributed data with mean and 95 percent confidence inter...FIGURE 7-7: Normally distributed data with mean and 99 percent confidence inter...FIGURE 7-8: Airline passenger data.FIGURE 7-9: Airline passenger data with a trend line.

6 Chapter 9FIGURE 9-1: Scatterplot showing clustered data.FIGURE 9-2: The k-means clustering algorithm visualization.FIGURE 9-3: WSS plot showing the optimal number of clusters (four).FIGURE 9-4: Scatterplot matrix of blockchain transfer data.FIGURE 9-5: The k-means algorithm applied to blockchain supply chain ownership ...

7 Chapter 10FIGURE 10-1: Decision tree for the iris dataset.FIGURE 10-2: Bayes theorem calculation of conditional probability.FIGURE 10-3: TV product data.FIGURE 10-4: Decision tree based on supply chain blockchain data.FIGURE 10-5: Output from the decisionTreeBlockchain.pyPython program.FIGURE 10-6: Gaussian (normal) distribution.

8 Chapter 11FIGURE 11-1: Data exhibiting a linear relationship.FIGURE 11-2: Data exhibiting a categorical relationship.FIGURE 11-3: Linear regression model visualization.FIGURE 11-4: Sigmoid function.FIGURE 11-5: Logistic regression model visualization.FIGURE 11-6: Logistic regression model visualization including the confusion ma...FIGURE 11-7: Linear regression model visualization based on supply chain blockc...FIGURE 11-8: Logistic regression model visualization based on supply chain bloc...

9 Chapter 12FIGURE 12-1: AXP closing stock prices.FIGURE 12-2: AXP stock price autocorrelation.FIGURE 12-3: AXP stock price differencing values of 1, 2, and 3.FIGURE 12-4: ARIMA Model build results.FIGURE 12-5: Initial Dow Jones dataframe after loading from file.FIGURE 12-6: Imported and converted Dow Jones data.FIGURE 12-7: Imported, converted, and filtered Dow Jones data.FIGURE 12-8: Dow Jones dataset raw data and moving average.FIGURE 12-9: Dow Jones dataset raw data, moving average, and ARIMA model.

10 Chapter 15FIGURE 15-1: The TensorFlow website.FIGURE 15-2: The Keras website.FIGURE 15-3: PyTorch website.FIGURE 15-4: The fast.ai website.FIGURE 15-5: The MXNet website.FIGURE 15-6: The Caffe website.FIGURE 15-7: The Deeplearning4j website.

11 Chapter 17FIGURE 17-1: Anaconda Navigator.FIGURE 17-2: Visual Studio Code.FIGURE 17-3: Jupyter Notebook.FIGURE 17-4: JupyterLab.FIGURE 17-5: RStudio IDE.FIGURE 17-6: The web3.py website.FIGURE 17-7: Infura’s architecture.FIGURE 17-8: The Vaex website.FIGURE 17-9: Etherescan.io.FIGURE 17-10: Blockchain.com Block Explorer.FIGURE 17-11: ColussusXT cryptocurrency Block Explorer.FIGURE 17-12: The MADANA website.

12 Chapter 18FIGURE 18-1: Google’s BigQuery visualization of the Ethereum blockchain.FIGURE 18-2: Stack Overflow search results for techniques for visualizing data. FIGURE 18-3: Reddit search results for visualizing data. FIGURE 18-4: The Kaggle website.FIGURE 18-5: GIGRAPH example of a network graph from Excel spreadsheet data.FIGURE 18-6: Visualization best practices example from Tableau Gurus.FIGURE 18-7: The Ethviewer real-time Ethereum blockchain monitor.

13 Chapter 19FIGURE 19-1: Chainalysis Reactor.FIGURE 19-2: Moving toward distributed, autonomous IoT.FIGURE 19-3: The DocStamp website.FIGURE 19-4: Ethviewer Ethereum blockchain monitor.

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2 Table of Contents

3 Begin Reading Introduction Data is the driver of today’s organizations. Ignore the vast amounts of data available to you about your products, services, customers, and even competitors, and you’ll quickly fall behind. But if you embrace data and mine it like it contains valuable jewels, you could find the edge to stay ahead of your competition and keep your customers happy. And the potential value you can find in data gets even more enticing when you incorporate blockchain technology into your organization. Blockchain is a fast-growing innovation that maintains untold pieces of information you could use to decrease costs and increase revenue. Realizing blockchain data value depends on understanding how blockchain stores data and how to get to it. Blockchain Data Analytics For Dummies introduces readers to blockchain technology, how it stores data, how to identify and get to interesting data, and how to analyze that data to find meaningful information. You learn how to set up your own blockchain analytics lab and local blockchain to learn and practice blockchain analytics techniques. After you set up your analytics lab, you find out how to extract blockchain data and build popular analytics models to uncover your data’s hidden information.

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