Introduction 1 About This Book 1 Foolish Assumptions 2 Icons Used in This Book 2 Beyond the Book 2 Where to Go from Here 3 Part 1: Intro to Analytics and Blockchain 5 Chapter 1: Driving Business with Data and Analytics 7 Deriving Value from Data 8 Monetizing data 8 Exchanging data 9 Verifying data 10 Understanding and Satisfying Regulatory Requirements 11 Classifying individuals 11 Identifying criminals 11 Examining common privacy laws 12 Predicting Future Outcomes with Data 13 Classifying entities 13 Predicting behavior 14 Making decisions based on models 16 Changing Business Practices to Create Desired Outcomes 16 Defining the desired outcome 17 Building models for simulation 17 Aligning operations and assessing results 18 Chapter 2: Digging into Blockchain Technology 19 Exploring the Blockchain Landscape 20 Managing ownership transfer 20 Doing more with blockchain 21 Understanding blockchain technology 21 Reviewing blockchain''s family tree 22 Fitting blockchain into today''s businesses 25 Understanding Primary Blockchain Types 27 Categorizing blockchain implementations 27 Describing basic blockchain type features 29 Contrasting popular enterprise blockchain implementations 30 Aligning Blockchain Features with Business Requirements 31 Reviewing blockchain core features 31 Examining primary common business requirements 33 Matching blockchain features to business requirements 34 Examining Blockchain Use Cases 35 Managing physical items in cyberspace 35 Handling sensitive information 36 Conducting financial transactions 37 Chapter 3: Identifying Blockchain Data with Value 39 Exploring Blockchain Data 40 Understanding what''s stored in blockchain blocks 40 Recording transaction data 41 Dissecting the parts of a block 43 Decoding block data 47 Categorizing Common Data in a Blockchain 49 Serializing transaction data 49 Logging events on the blockchain 50 Storing value with smart contracts 52 Examining Types of Blockchain Data for Value 52 Exploring basic transaction data 53 Associating real-world meaning to events 53 Aligning Blockchain Data with Real-World Processes 54 Understanding smart contract functions 55 Assessing smart contract event logs 55 Ranking transaction and event data by its effect 55 Chapter 4: Implementing Blockchain Analytics in Business 57 Aligning Analytics with Business Goals 58 Leveraging newly accessible decentralized tools 58 Monetizing data 59 Exchanging and integrating data effectively 59 Surveying Options for Your Analytics Lab 60 Installing the Blockchain Client 61 Installing the Test Blockchain 65 Installing the Testing Environment 68 Getting ready to install Truffle 69 Downloading and installing Truffle 72 Installing the IDE 74 Chapter 5: Interacting with Blockchain Data 79 Exploring the Blockchain Analytics Ecosystem 80 Reviewing your blockchain lab 80 Identifying analytics client options 81 Choosing the best blockchain analytics client 83 Adding Anaconda and Web3.js to Your Lab 84 Verifying platform prerequisites 84 Installing the Anaconda platform 86 Installing the Web3.py library 89 Setting up your blockchain analytics project 90 Writing a Python Script to Access a Blockchain 92 Interfacing with smart contracts 93 Finding a smart contract''s ABI 94 Building a Local Blockchain to Analyze 100 Connecting to your blockchain 101 Invoking smart contract functions 101 Fetching blockchain data 102 Part 2: Fetching Blockchain Chain 105 Chapter 6: Parsing Blockchain Data and Building the Analysis Dataset 107 Comparing On-Chain and External Analysis Options 108 Considering access speed 108 Comparing one-off versus repeated analysis 109 Assessing data completeness 110 Integrating External Data 111 Determining what data you need 112 Extending identities to off-chain data 113 Finding external data 114 Identifying Features 115 Describing how features affect outcomes 116 Comparing filtering and wrapping methods 116 Building an Analysis Dataset 117 Connecting to multiple data sources 118 Building a cross-referenced dataset 118 Cleaning your data 118 Chapter 7: Building Basic Blockchain Analysis Models 121 Identifying Related Data 122 Grouping data based on features (attributes) 123 Determining group membership 126 Discovering relationships among items 129 Making Predictions of Future Outcomes 130 Selecting features that affect outcome 131 Beating the best guess 133 Building confidence 134 Analyzing Time-Series Data 135 Exploring growth and maturity 137 Identifying seasonal trends 138 Describing cycles of results 138 Chapter 8: Leveraging Advanced Blockchain Analysis Models 139 Identifying Participation Incentive Mechanisms 140 Complying with mandates 141 Playing games with partners 141 Rewarding and punishing participants 142 Managing Deployment and Maintenance Costs 143 Lowering the cost of admission 143 Leveraging participation value 145 Aligning ROI with analytics currency 146 Collaborating to Create Better Models 147 Collecting data from a cohort 148 Building models collaboratively 148 Assessing model quality as a team 149 Part 3: Analyzing and Visualizing Blockchain Analysis Data 151 Chapter 9: Identifying Clustered and Related Data 153 Analyzing Data Clustering Using Popular Models 154 Delivering valuable knowledge with cluster analysis 154 Examining popular clustering techniques 155 Understanding k-means analysis 155 Evaluating model effectiveness with diagnostics 160 Implementing Blockchain Data Clustering Algorithms in Python 160 Discovering Association Rules in Data 163 Delivering valuable knowledge with association rules analysis 163 Describing the apriori association rules algorithm 164 Evaluating model effectiveness with diagnostics 167 Determining When to Use Clustering and Association Rules 168 Chapter 10: Classifying Blockchain Data 171 Analyzing Data Classification Using Popular Models 172 Delivering valuable knowledge with classification analysis 172 Examining popular classification techniques 173 Understanding how the decision tree algorithm works 173 Understanding how the naïve Bayes algorithm works 176 Evaluating model effectiveness with diagnostics 178 Implementing Blockchain Classification Algorithms in Python 179 Defining model input data requirements 179 Building your classification model dataset 181 Developing your classification model code 184 Determining When Classification Fits Your Analytics Needs 188 Chapter 11: Predicting the Future with Regression 189 Analyzing Predictions and Relationships Using Popular Models 190 Delivering valuable knowledge with regression analysis 190 Examining popular regression techniques 191 Describing how linear regression works 195 Describing how logistic regression works 198 Evaluating model effectiveness with diagnostics 201 Implementing Regression Algorithms in Python 203 Defining model input data requirements 203 Building your regression model dataset 203 Developing your regression model code 204 Determining When Regression Fits Your Analytics Needs 207 Chapter 12: Analyzing Blockchain Data over Time 209 Analyzing Time Series Data Using Popular Models 210 Delivering valuable knowledge with time series analysis 211 Examining popular time series techniques 211 Visualizing time series results 214 Implementing Time Series Algorithms in Python 216 Defining model input data requirements 217 Developing your time series model code 219 Determining When Time Series Fits Your Analytics Needs 221 Part 4: Implementing Blockchain Analysis Models 223 Chapter 13: Writing Models from Scratch 225 Interacting with Blockchains 226 Connecting to a Blockchain 226 Using an application programming interface to interact with a blockchain 228 Reading from a blockchain 230 Updating previously read blockchain data 234 Examining Blockchain Client Languages and Approaches 236 Introducing popular blockchain client programming languages 237 Comparing popular language pros and cons 238 Deciding on the right language 238 Chapter 14: Calling on Existing Frameworks 239 Benefitting from Standardization 240 Easing the burden of compliance 240 Avoiding inefficient code 242 Raising the bar on quality 244 Focusing on Analytics, Not Utilities 245 Avoiding feature bloat 245 Setting granular goals 246 Managing post-operational models 247 Leveraging the Efforts of Others 248 Deciding between make or buy 248 Scoping your testing efforts 249