Data Mining and Predictive Analytics for Business Decisions 9781683926757

Table of contents :
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