Data Mining and Predictive Analytics: A Case Study Approach

Acknowledgments
 Chapter 1: Data Mining and Business
 Data Mining Algorithms and Activities
 Data is the New Oil
 Data-Driven Decision-Making
 Business Analytics and Business Intelligence
 Algorithmic Technologies Associated with Data Mining
 Data Mining and Data Warehousing
 Case Study 1.1: Business Applications of Data Mining
 Case A – Classification
 Case B – Regression
 Case C – Anomaly Detection
 Case D – Time Series
 Case E – Clustering
 Reference
 Chapter 2: The Data Mining Process
 Data Mining as a Process
 Exploration
 Analysis
 Interpretation
 Exploitation
 Selecting a Data Mining Process
 The CRISP-DM Process Model
 Business Understanding
 Data Understanding
 Data Preparation
 Modeling
 Evaluation
 Deployment
 Selecting Data Analytics Languages
 The Choices for Languages
 References
 Chapter 3: Framing Analytical Questions
 How Does CRISP-DM Define the Business and Data Understanding Step?
 The World of the Business Data Analyst
 How Does Data Analysis Relate to Business Decision-Making?
 How Do We Frame Analytical Questions?
 What Are the Characteristics of Well-framed Analytical Questions?
 Exercise 3.1 – Framed Questions About the Titanic Disaster
 Case Study 3.1 – The San Francisco Airport Survey
 Case Study 3.2 – Small Business Administration Loans
References
 Chapter 4: Data Preparation
 How Does CRISP-DM Define Data Preparation?
 Steps in Preparing the Data Set for Analysis
 Data Sources and Formats
 What is Data Shaping?
 The Flat-File Format
 Application of Tools for Data Acquisition and Preparation
 Exercise 4.1 – Shaping the Data File
 Exercise 4.2 – Cleaning the Data File
 Ensuring the Right Variables are Included
 Using SQL to Extract the Right Data Set from Data Warehouses
 Case Study 4.1: Cleaning and Shaping the SFO Survey Data Set
 Case Study 4.2: Shaping the SBA Loans Data Set
 Case Study 4.3: Additional SQL Queries
 Reference
 Chapter 5: Descriptive Analysis
 Getting a Sense of the Data Set
 Describe the Data Set
 Explore the Data Set
 Verify the Quality of the Data Set
 Analysis Techniques to Describe the Variables
 Exercise 5.1 – Descriptive Statistics
 Distributions of Numeric Variables
 Correlation
 Exercise 5.2 – Descriptive Analysis of the Titanic Disaster Data
 Case Study 5.1: Describing the SFO Survey Data Set
 Solution Using R
 Solution Using Python
 Case Study 5.2: Describing the SBA Loans Data Set
 Solution Using R
 Solution Using Python
 Reference
 Chapter 6: Modeling
 What is a Model?
 How Does CRISP-DM Define Modeling?
 Selecting the Modeling Technique
 Modeling Assumptions
 Generate Test Design
 Design of Model Testing
 Build the Model
 Parameter Setting
 Models
 Model Assessment
Where Do Models Reside in a Computer?
 The Data Mining Engine
 The Model
 Data Sources and Outputs
 Traditional Data Sources
 Static Data Sources
 Real-Time Data Sources
 Analytic Outputs
 Model Building
 Step 1: Framing Questions
 Step 2: Selecting the Machine
 Step 3: Selecting Known Data
 Step 4: Training the Machine
 Step 5: Testing the Model
 Step 6: Deploying the Model
 Step 7: Collecting New Data
 Step 8: Updating the Model
 Step 9: Learning – Repeat Steps 7 and 8
 Step 10: Recommending Answers to the User
 Reference
 Chapter 7: Predictive Analytics with Regression Models
 What is Supervised Learning?
 Regression to the Mean
 Linear Regression
 Simple Linear Regression
 The R-squared Coefficient
 The Use of the p-value of the Coefficients
 Strength of the Correlation Between Two Variables
 Exercise 7.1 – Using SLR Analysis to Understand Franchise Advertising
 Multivariate Linear Regression
 Preparing to Build the Multivariate Model
 Exercise 7.2 – Using Multivariate Linear Regression to Model Franchise Sales
 Logistic Regression
 What is Logistic Regression?
 Exercise 7.3 – PassClass Case Study
 Multivariate Logistic Regression
 Exercise 7.4 – MLR Used to Analyze the Results of a Database Marketing Initiative
 Where is Logistic Regression Used?
 Comparing Linear and Logistic Regressions for Binary Outcomes
 Case Study 7.1: Linear Regression Using the SFO Survey Data Set
 Solution in R
 Solution in Python
 Case Study 7.2: Linear Regression Using the SBA Loans Data Set
 Solution in R
Solution in Python
 Case Study 7.3: Logistic Regression Using the SFO Survey Data Set
 Solution in R
 Solution in Python
 Case Study 7.4: Logistic Regression Using the SBA Loans Data Set
 Solution in R
 Solution in Python
 Chapter 8: Classification
 Classification with Decision Trees
 Building a Decision Tree
 Exercise 8.1 – The Iris Data Set
 The Problem with Decision Trees
 Classification with Random Forest
 Using a Random Forest Model
 Exercise 8.2 – The Iris Data Set
 Classification with Naïve Bayes
 Exercise 8.3 – The HIKING Data Set
 Computing the Conditional Probabilities
 Case Study 8.1: Classification with the SFO Survey Data Set
 Solution in R
 Solution in Python
 Case Study 8.2: Classification with the SBA Loans Data Set
 Solution in R
 Solution in Python
 Case Study 8.3: Classification with the Florence Nightingale Data Set
 Solution in Python
 Reference
 Chapter 9: Clustering
 What is Unsupervised Machine Learning?
 What is Clustering Analysis?
 Applying Clustering to Old Faithful Eruptions
 Examples of Applications of Clustering Analysis
 A Simple Clustering Example Using Regression
 Hierarchical Clustering
 Applying Hierarchical Clustering to Old Faithful Eruptions
 Exercise 9.1 – Hierarchical Clustering and the Iris Data Set
 K-Means Clustering
 How Does the K-Means Algorithm Compute Cluster Centroids?
 Applying K-Means Clustering to Old Faithful Eruptions
 Exercise 9.2 – K-Means Clustering and the Iris Data Set
 Hierarchical vs. K-Means Clustering
 Case Study 9.1: Clustering with the SFO Survey Data Set
 Solution in R
 Solution in Python
Case Study 9.2: Clustering with the SBA Loans Data Set
 Solution in R
 Solution in Python
 Chapter 10: Time Series Forecasting
 What is a Time Series?
 Time Series Analysis
 Types of Time Series Analysis
 What is Forecasting?
 Exercise 10.1 – Analysis of the US and China GDP Data Set
 Case Studies
 Case Study 10.1: Time Series Analysis of the SFO Survey Data Set
 Solution in Excel
 Case Study 10.2: Time Series Analysis of the SBA Loans Data set
 Solution in R
 Solution in Python
 Case Study 10.3: Time Series Analysis of a Nest Data Set
 Solution in Python
 Reference
 Chapter 11: Feature Selection
 Using the Covariance Matrix
 Factor Analysis
 When to Use Factor Analysis
 First Step in FA – Correlation
 FA for Exploratory Analysis
 Selecting the Number of Factors – The Scree Plot
 Example 11.1: Restaurant Feedback
 Factor Interpretation
 Summary Activities to Perform a Factor Analysis
 Case Study 11.1: Variable Reduction with the SFO Survey Data Set
 Solution in R
 Solution in Python
 Case Study 11.2: Hunting Diamonds
 Solution in R
 Solution in Python
 Chapter 12: Anomaly Detection
 What is an Anomaly?
 What is an Outlier?
 The Case Studies for the Exercises in Anomaly Detection
 Anomaly Detection by Standardization – A Single Numerical Variable
 Exercise 12.1 – Outliers in the Airline Delays Data Set – Z-Score
 Anomaly Detection by Quartiles – Tukey Fences – With a Single Variable
 Comparing Z-scores and Tukey Fences
 Exercise 12.2 – Outliers in the Airline Delays Data Set – Tukey Fences
 Anomaly Detection by Category – A Single Variable
Exercise 12.3 – Outliers in the Airline Delays Data Set – Categorical
 Anomaly Detection by Clustering – Multiple Variables
 Exercise 12.4 – Outliers in the Airline Delays Data Set – Clustering
 Anomaly Detection Using Linear Regression by Residuals – Multiple Variables
 Exercise 12.5 – Outliers in the Airline Delays Data Set – Residuals
 Case Study 12.1: Outliers in the SFO Survey Data Set
 Solution in R
 Solution in Python
 Case Study 12.2: Outliers in the SBA Loans Data Set
 Solution in R
 Solution in Python
 References
 Chapter 13: Text Data Mining
 What is Text Data Mining?
 What are Some Examples of Text-Based Analytical Questions?
 Tools for Text Data Mining
 Sources and Formats of Text Data
 Term Frequency Analysis
 How Does It Apply to Text Business Data Analysis?
 Exercise 13.1 – Case Study Using a Training Survey Data Set
 Word Frequency Analysis Using R
 Keyword Analysis
 Exercise 13.2 – Case Study Using Data Set D: Résumé and Job Description
 Keyword Word Analysis in Voyant
 Term Frequency Analysis in R
 Visualizing Text Data
 Exercise 13.3 – Case Study Using the Training Survey Data Set
 Visualizing the Text Using Excel
 Visualizing the Text Using Voyant
 Visualizing the Text Using R
 Text Similarity Scoring
 What is Text Similarity Scoring?
 Exercise 13.4 – Case Study Using the Occupation Description Data Set
 Analysis Using an Online Text Similarity Scoring Tool
 Similarity Scoring Analysis Using R
 Exercise 13.5 – Résumé and Job Descriptions Similarly Scoring Using R
 Case Study 13.1 – Term Frequency Analysis of Product Reviews
 Term Frequency Analysis Using Voyant
 Term Frequency Analysis Using R
 References
 Chapter 14: Working with Large Data Sets
 Using Sampling to Work with Large Data Files
 Exercise 14.1 – Big Data Analysis
 Case Study 14.1 Using the BankComplaints Big Data File
Exercise 12.3 – Outliers in the Airline Delays Data Set – Categorical
 Anomaly Detection by Clustering – Multiple Variables
 Exercise 12.4 – Outliers in the Airline Delays Data Set – Clustering
 Anomaly Detection Using Linear Regression by Residuals – Multiple Variables
 Exercise 12.5 – Outliers in the Airline Delays Data Set – Residuals
 Case Study 12.1: Outliers in the SFO Survey Data Set
 Solution in R
 Solution in Python
 Case Study 12.2: Outliers in the SBA Loans Data Set
 Solution in R
 Solution in Python
 References
 Chapter 13: Text Data Mining
 What is Text Data Mining?
 What are Some Examples of Text-Based Analytical Questions?
 Tools for Text Data Mining
 Sources and Formats of Text Data
 Term Frequency Analysis
 How Does It Apply to Text Business Data Analysis?
 Exercise 13.1 – Case Study Using a Training Survey Data Set
 Word Frequency Analysis Using R
 Keyword Analysis
 Exercise 13.2 – Case Study Using Data Set D: Résumé and Job Description
 Keyword Word Analysis in Voyant
 Term Frequency Analysis in R
 Visualizing Text Data
 Exercise 13.3 – Case Study Using the Training Survey Data Set
 Visualizing the Text Using Excel
 Visualizing the Text Using Voyant
 Visualizing the Text Using R
 Text Similarity Scoring
 What is Text Similarity Scoring?
 Exercise 13.4 – Case Study Using the Occupation Description Data Set
 Analysis Using an Online Text Similarity Scoring Tool
 Similarity Scoring Analysis Using R
 Exercise 13.5 – Résumé and Job Descriptions Similarly Scoring Using R
 Case Study 13.1 – Term Frequency Analysis of Product Reviews
 Term Frequency Analysis Using Voyant
 Term Frequency Analysis Using R
 References
 Chapter 14: Working with Large Data Sets
 Using Sampling to Work with Large Data Files
 Exercise 14.1 – Big Data Analysis
 Case Study 14.1 Using the BankComplaints Big Data File