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Data Mining and Predictive Analysis

(DM-PA.AE1) / आईएसबीएन : 978-1-64459-374-5
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1

Preface

  • What is Data Mining? What is Predictive Analytics?
  • Why is this Course Needed?
  • Who Will Benefit from this Course?
  • Danger! Data Mining is Easy to do Badly
  • “White-Box” Approach
  • Algorithm Walk-Throughs
  • Exciting New Topics
  • The R Zone
  • Appendix: Data Summarization and Visualization
  • The Case Study: Bringing it all Together
  • How the Course is Structured
2

An Introduction to Data Mining and Predictive Analytics

  • What is Data Mining? What Is Predictive Analytics?
  • Wanted: Data Miners
  • The Need For Human Direction of Data Mining
  • The Cross-Industry Standard Process for Data Mining: CRISP-DM
  • Fallacies of Data Mining
  • What Tasks can Data Mining Accomplish
  • The R Zone
  • R References
  • Exercises
3

Data Preprocessing

  • Why do We Need to Preprocess the Data?
  • Data Cleaning
  • Handling Missing Data
  • Identifying Misclassifications
  • Graphical Methods for Identifying Outliers
  • Measures of Center and Spread
  • Data Transformation
  • Min–Max Normalization
  • Z-Score Standardization
  • Decimal Scaling
  • Transformations to Achieve Normality
  • Numerical Methods for Identifying Outliers
  • Flag Variables
  • Transforming Categorical Variables into Numerical Variables
  • Binning Numerical Variables
  • Reclassifying Categorical Variables
  • Adding an Index Field
  • Removing Variables that are not Useful
  • Variables that Should Probably not be Removed
  • Removal of Duplicate Records
  • A Word About ID Fields
  • The R Zone
  • R Reference
  • Exercises
4

Exploratory Data Analysis

  • Hypothesis Testing Versus Exploratory Data Analysis
  • Getting to Know The Data Set
  • Exploring Categorical Variables
  • Exploring Numeric Variables
  • Exploring Multivariate Relationships
  • Selecting Interesting Subsets of the Data for Further Investigation
  • Using EDA to Uncover Anomalous Fields
  • Binning Based on Predictive Value
  • Deriving New Variables: Flag Variables
  • Deriving New Variables: Numerical Variables
  • Using EDA to Investigate Correlated Predictor Variables
  • Summary of Our EDA
  • The R Zone
  • R References
  • Exercises
5

Dimension-Reduction Methods

  • Need for Dimension-Reduction in Data Mining
  • Principal Components Analysis
  • Applying PCA to the Houses Data Set
  • How Many Components Should We Extract?
  • Profiling the Principal Components
  • Communalities
  • Validation of the Principal Components
  • Factor Analysis
  • Applying Factor Analysis to the Adult Data Set
  • Factor Rotation
  • User-Defined Composites
  • An Example of a User-Defined Composite
  • The R Zone
  • R References
  • Exercises
6

Univariate Statistical Analysis

  • Data Mining Tasks in Discovering Knowledge in Data
  • Statistical Approaches to Estimation and Prediction
  • Statistical Inference
  • How Confident are We in Our Estimates?
  • Confidence Interval Estimation of the Mean
  • How to Reduce the Margin of Error
  • Confidence Interval Estimation of the Proportion
  • Hypothesis Testing for the Mean
  • Assessing The Strength of Evidence Against The Null Hypothesis
  • Using Confidence Intervals to Perform Hypothesis Tests
  • Hypothesis Testing for The Proportion
  • Reference
  • The R Zone
  • R Reference
  • Exercises
7

Multivariate Statistics

  • Two-Sample t-Test for Difference in Means
  • Two-Sample Z-Test for Difference in Proportions
  • Test for the Homogeneity of Proportions
  • Chi-Square Test for Goodness of Fit of Multinomial Data
  • Analysis of Variance
  • Reference
  • The R Zone
  • R Reference
  • Exercises
8

Preparing to Model the Data

  • Supervised Versus Unsupervised Methods
  • Statistical Methodology and Data Mining Methodology
  • Cross-Validation
  • Overfitting
  • Bias–Variance Trade-Off
  • Balancing The Training Data Set
  • Establishing Baseline Performance
  • The R Zone
  • R Reference
  • Exercises
9

Simple Linear Regression

  • An Example of Simple Linear Regression
  • Dangers of Extrapolation
  • How Useful is the Regression? The Coefficient of Determination, r2
  • Standard Error of the Estimate, s
  • Correlation Coefficient r
  • Anova Table for Simple Linear Regression
  • Outliers, High Leverage Points, and Influential Observations
  • Population Regression Equation
  • Verifying The Regression Assumptions
  • Inference in Regression
  • t-Test for the Relationship Between x and y
  • Confidence Interval for the Slope of the Regression Line
  • Confidence Interval for the Correlation Coefficient ρ
  • Confidence Interval for the Mean Value of y Given x
  • Prediction Interval for a Randomly Chosen Value of y Given x
  • Transformations to Achieve Linearity
  • Box–Cox Transformations
  • The R Zone
  • R References
  • Exercises
10

Multiple Regression and Model Building

  • An Example of Multiple Regression
  • The Population Multiple Regression Equation
  • Inference in Multiple Regression
  • Regression With Categorical Predictors, Using Indicator Variables
  • Adjusting R2: Penalizing Models For Including Predictors That Are Not Useful
  • Sequential Sums of Squares
  • Multicollinearity
  • Variable Selection Methods
  • Gas Mileage Data Set
  • An Application of Variable Selection Methods
  • Using the Principal Components as Predictors in Multiple Regression
  • The R Zone
  • R References
  • Exercises
11

k-Nearest Neighbor Algorithm

  • Classification Task
  • k-Nearest Neighbor Algorithm
  • Distance Function
  • Combination Function
  • Quantifying Attribute Relevance: Stretching the Axes
  • Database Considerations
  • k-Nearest Neighbor Algorithm for Estimation and Prediction
  • Choosing k
  • Application of k-Nearest Neighbor Algorithm Using IBM/SPSS Modeler
  • The R Zone
  • R References
  • Exercises
12

Decision Trees

  • What is a Decision Tree?
  • Requirements for Using Decision Trees
  • Classification and Regression Trees
  • C4.5 Algorithm
  • Decision Rules
  • Comparison of the C5.0 and CART Algorithms Applied to Real Data
  • The R Zone
  • R References
  • Exercises
13

Neural Networks

  • Input and Output Encoding
  • Neural Networks for Estimation and Prediction
  • Simple Example of a Neural Network
  • Sigmoid Activation Function
  • Back-Propagation
  • Gradient-Descent Method
  • Back-Propagation Rules
  • Example of Back-Propagation
  • Termination Criteria
  • Learning Rate
  • Momentum Term
  • Sensitivity Analysis
  • Application of Neural Network Modeling
  • The R Zone
  • R References
  • Exercises
14

Logistic Regression

  • Simple Example of Logistic Regression
  • Maximum Likelihood Estimation
  • Interpreting Logistic Regression Output
  • Inference: Are the Predictors Significant?
  • Odds Ratio and Relative Risk
  • Interpreting Logistic Regression for a Dichotomous Predictor
  • Interpreting Logistic Regression for a Polychotomous Predictor
  • Interpreting Logistic Regression for a Continuous Predictor
  • Assumption of Linearity
  • Zero-Cell Problem
  • Multiple Logistic Regression
  • Introducing Higher Order Terms to Handle Nonlinearity
  • Validating the Logistic Regression Model
  • WEKA: Hands-On Analysis Using Logistic Regression
  • The R Zone
  • R References
  • Exercises
15

NaïVe Bayes and Bayesian Networks

  • Bayesian Approach
  • Maximum A Posteriori (MAP) Classification
  • Posterior Odds Ratio
  • Balancing The Data
  • Naïve Bayes Classification
  • Interpreting The Log Posterior Odds Ratio
  • Zero-Cell Problem
  • Numeric Predictors for Naïve Bayes Classification
  • WEKA: Hands-on Analysis Using Naïve Bayes
  • Bayesian Belief Networks
  • Clothing Purchase Example
  • Using The Bayesian Network to Find Probabilities
  • The R Zone
  • R References
  • Exercises
16

Model Evaluation Techniques

  • Model Evaluation Techniques for the Description Task
  • Model Evaluation Techniques for the Estimation and Prediction Tasks
  • Model Evaluation Measures for the Classification Task
  • Accuracy and Overall Error Rate
  • Sensitivity and Specificity
  • False-Positive Rate and False-Negative Rate
  • Proportions of True Positives, True Negatives, False Positives, and False Negatives
  • Misclassification Cost Adjustment to Reflect Real-World Concerns
  • Decision Cost/Benefit Analysis
  • Lift Charts and Gains Charts
  • Interweaving Model Evaluation with Model Building
  • Confluence of Results: Applying a Suite of Models
  • The R Zone
  • R References
  • Exercises
  • Hands-On Analysis
17

Cost-Benefit Analysis Using Data-Driven Costs

  • Decision Invariance Under Row Adjustment
  • Positive Classification Criterion
  • Demonstration Of The Positive Classification Criterion
  • Constructing The Cost Matrix
  • Decision Invariance Under Scaling
  • Direct Costs and Opportunity Costs
  • Case Study: Cost-Benefit Analysis Using Data-Driven Misclassification Costs
  • Rebalancing as a Surrogate for Misclassification Costs
  • The R Zone
  • R References
  • Exercises
18

Cost-Benefit Analysis for Trinary and -Nary Classification Models

  • Classification Evaluation Measures for a Generic Trinary Target
  • Application of Evaluation Measures for Trinary Classification to the Loan Approval Problem
  • Data-Driven Cost-Benefit Analysis for Trinary Loan Classification Problem
  • Comparing Cart Models With and Without Data-Driven Misclassification Costs
  • Classification Evaluation Measures for a Generic k-Nary Target
  • Example of Evaluation Measures and Data-Driven Misclassification Costs for k-Nary Classification
  • The R Zone
  • R References
  • Exercises
19

Graphical Evaluation of Classification Models

  • Review of Lift Charts and Gains Charts
  • Lift Charts and Gains Charts Using Misclassification Costs
  • Response Charts
  • Profits Charts
  • Return on Investment (ROI) Charts
  • The R Zone
  • R References
  • Exercises
  • Hands-On Exercises
20

Hierarchical and k-Means Clustering

  • The Clustering Task
  • Hierarchical Clustering Methods
  • Single-Linkage Clustering
  • Complete-Linkage Clustering
  • k-Means Clustering
  • Example of k-Means Clustering at Work
  • Behavior of MSB, MSE, and Pseudo-F as the k-Means Algorithm Proceeds
  • Application of k-Means Clustering Using SAS Enterprise Miner
  • Using Cluster Membership to Predict Churn
  • The R Zone
  • R References
  • Exercises
  • Hands-On Analysis
21

Kohonen Networks

  • Self-Organizing Maps
  • Kohonen Networks
  • Example of a Kohonen Network Study
  • Cluster Validity
  • Application of Clustering Using Kohonen Networks
  • Interpreting The Clusters
  • Using Cluster Membership as Input to Downstream Data Mining Models
  • The R Zone
  • R References
  • Exercises
22

BIRCH Clustering

  • Rationale for BIRCH Clustering
  • Cluster Features
  • Cluster Feature TREE
  • Phase 1: Building The CF Tree
  • Phase 2: Clustering The Sub-Clusters
  • Example of Birch Clustering, Phase 1: Building The CF Tree
  • Example of BIRCH Clustering, Phase 2: Clustering The Sub-Clusters
  • Evaluating The Candidate Cluster Solutions
  • Case Study: Applying BIRCH Clustering to The Bank Loans Data Set
  • The R Zone
  • R References
  • Exercises
23

Measuring Cluster Goodness

  • Rationale for Measuring Cluster Goodness
  • The Silhouette Method
  • Silhouette Example
  • Silhouette Analysis of the IRIS Data Set
  • The Pseudo-F Statistic
  • Example of the Pseudo-F Statistic
  • Pseudo-F Statistic Applied to the IRIS Data Set
  • Cluster Validation
  • Cluster Validation Applied to the Loans Data Set
  • The R Zone
  • R References
  • Exercises
24

Association Rules

  • Affinity Analysis and Market Basket Analysis
  • Support, Confidence, Frequent Itemsets, and the A Priori Property
  • How Does The A Priori Algorithm Work (Part 1)? Generating Frequent Itemsets
  • How Does The A Priori Algorithm Work (Part 2)? Generating Association Rules
  • Extension From Flag Data to General Categorical Data
  • Information-Theoretic Approach: Generalized Rule Induction Method
  • Association Rules are Easy to do Badly
  • How Can We Measure the Usefulness of Association Rules?
  • Do Association Rules Represent Supervised or Unsupervised Learning?
  • Local Patterns Versus Global Models
  • The R Zone
  • R References
  • Exercises
25

Segmentation Models

  • The Segmentation Modeling Process
  • Segmentation Modeling Using EDA to Identify the Segments
  • Segmentation Modeling using Clustering to Identify the Segments
  • The R Zone
  • R References
  • Exercises
26

Ensemble Methods: Bagging and Boosting

  • Rationale for Using an Ensemble of Classification Models
  • Bias, Variance, and Noise
  • When to Apply, and not to apply, Bagging
  • Bagging
  • Boosting
  • Application of Bagging and Boosting Using IBM/SPSS Modeler
  • References
  • The R Zone
  • R Reference
  • Exercises
27

Model Voting and Propensity Averaging

  • Simple Model Voting
  • Alternative Voting Methods
  • Model Voting Process
  • An Application of Model Voting
  • What is Propensity Averaging?
  • Propensity Averaging Process
  • An Application of Propensity Averaging
  • The R Zone
  • R References
  • Exercises
  • Hands-On Analysis
28

Genetic Algorithms

  • Introduction To Genetic Algorithms
  • Basic Framework of a Genetic Algorithm
  • Simple Example of a Genetic Algorithm at Work
  • Modifications and Enhancements: Selection
  • Modifications and Enhancements: Crossover
  • Genetic Algorithms for Real-Valued Variables
  • Using Genetic Algorithms to Train a Neural Network
  • WEKA: Hands-On Analysis Using Genetic Algorithms
  • The R Zone
  • R References
  • Exercises
29

Imputation of Missing Data

  • Need for Imputation of Missing Data
  • Imputation of Missing Data: Continuous Variables
  • Standard Error of the Imputation
  • Imputation of Missing Data: Categorical Variables
  • Handling Patterns in Missingness
  • Reference
  • The R Zone
  • R References
30

Case Study, Part 1: Business Understanding, Data Preparation, and EDA

  • Cross-Industry Standard Practice for Data Mining
  • Business Understanding Phase
  • Data Understanding Phase, Part 1: Getting a Feel for the Data Set
  • Data Preparation Phase
  • Data Understanding Phase, Part 2: Exploratory Data Analysis
31

Case Study, Part 2: Clustering and Principal Components Analysis

  • Partitioning the Data
  • Developing the Principal Components
  • Validating the Principal Components
  • Profiling the Principal Components
  • Choosing the Optimal Number of Clusters Using Birch Clustering
  • Choosing the Optimal Number of Clusters Using k-Means Clustering
  • Application of k-Means Clustering
  • Validating the Clusters
  • Profiling the Clusters
32

Case Study, Part 3: Modeling And Evaluation For Performance And Interpretability

  • Do You Prefer The Best Model Performance, Or A Combination Of Performance And Interpretability?
  • Modeling And Evaluation Overview
  • Cost-Benefit Analysis Using Data-Driven Costs
  • Variables to be Input To The Models
  • Establishing The Baseline Model Performance
  • Models That Use Misclassification Costs
  • Models That Need Rebalancing as a Surrogate for Misclassification Costs
  • Combining Models Using Voting and Propensity Averaging
  • Interpreting The Most Profitable Model
33

Case Study, Part 4: Modeling and Evaluation for High Performance Only

  • Variables to be Input to the Models
  • Models that use Misclassification Costs
  • Models that Need Rebalancing as a Surrogate for Misclassification Costs
  • Combining Models using Voting and Propensity Averaging
  • Lessons Learned
  • Conclusions

Appendix A

  • Data Summarization and Visualization
  • Part 1: Summarization 1: Building Blocks Of Data Analysis
  • Part 2: Visualization: Graphs and Tables For Summarizing And Organizing Data
  • Part 3: Summarization 2: Measures Of Center, Variability, and Position
  • Part 4: Summarization And Visualization Of Bivariate Relationships

1

An Introduction to Data Mining and Predictive Analytics

  • Analyzing a Dataset
2

Data Preprocessing

  • Handling Missing Data
  • Creating a Histogram
  • Creating a Scatterplot
  • Creating a Normal Q-Q Plot
  • Creating Indicator Variables
3

Exploratory Data Analysis

  • Analyzing the churn Dataset
  • Exploring Categorical Variables
  • Exploring Numeric Variables
  • Exploring Multivariate Relationships
  • Investigating Correlation Values and p-values in Matrix Form
4

Dimension-Reduction Methods

  • Creating a Scree Plot
  • Profiling the Principal Components
  • Calculating Communalities
  • Validating the Principal Components
  • Applying Factor Analysis to a Dataset
5

Univariate Statistical Analysis

  • Estimating the Confidence Interval for the Mean
  • Estimating the Confidence Interval of the Population Proportion
6

Multivariate Statistics

  • Performing a t-test for Finding the Difference in Means
  • Performing a z-test for Finding the Difference in Proportions
  • Performing a Chi-Square Test for Homogeneity of Proportions
  • Performing a Chi-Square Test for Goodness of Fit of Multinomial Data
  • Analyzing a Variance
7

Preparing to Model the Data

  • Balancing the Training and Testing Datasets
8

Simple Linear Regression

  • Plotting Data with a Regression Line
  • Measuring the Goodness of Fit of the Regression
  • Performing Regression with Other Hikers
  • Verifying the Regression Assumptions
  • Determining Prediction and Confidence Intervals
  • Assessing Normality in Scrabble
  • Applying Box-Cox Transformations
9

Multiple Regression and Model Building

  • Approximating the Relationship between the Variables in a Scatterplot
  • Identifying Confidence Intervals
  • Creating a Dot Plot
  • Determining the Sequential Sums of Squares
  • Analyzing Multicollinearity
  • Applying the Best Subsets Procedure in a Regression Model
  • Applying the Stepwise Selection Procedure in a Regression Model
  • Applying the Backward Elimination Procedure
  • Applying Forward Selection Procedure
  • Using the Principal Components as Predictors in Multiple Regression
10

k-Nearest Neighbor Algorithm

  • Running KNN
  • Calculating the Euclidean Distance
11

Decision Trees

  • Plotting a Classification Tree
12

Neural Networks

  • Running a Neural Network
13

Logistic Regression

  • Creating a Plot for Logistic Regression
  • Interpreting Logistic Regression and Odds Ratio for a Dichotomous Predictor
14

NaïVe Bayes and Bayesian Networks

  • Calculating Posterior Odds Ratio
  • Calculating the Log Posterior Odds Ratio
  • Calculating the Numeric Predictors for Naive Bayes Classification
15

Model Evaluation Techniques

  • Estimating Costs for Benefit Analysis
16

Cost-Benefit Analysis Using Data-Driven Costs

  • Analyzing Cost-benefit Using Data-driven Misclassification Costs
17

Cost-Benefit Analysis for Trinary and -Nary Classification Models

  • Analyzing the Cost-Benefit for the Trinary Loan Classification Problem
19

Hierarchical and k-Means Clustering

  • Using Single-linkage Clustering
  • Using Complete-linkage Clustering
  • Finding Clusters in Data
20

Kohonen Networks

  • Using a 3x2 Kohonen Network
  • Interpreting Clusters
22

Measuring Cluster Goodness

  • Plotting Silhouette Values of a Dataset
  • Applying Cluster Validation to a Dataset
23

Association Rules

  • Viewing the Output Sorted by Support
24

Segmentation Models

  • Predicting Income Using Caps and No Caps Groups
27

Genetic Algorithms

  • Using Genetic Algorithms to Train a Neural Network

Data Mining and Predictive Analysis

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