Predictive Analytics
Low-Risk Strategies for High-Impact Projects
Course Outline
Introduction
Core Concepts
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Beyond Traditional Statistics
o ‘Assumptions’ of Traditional Statistics
o Shift your thinking…
Behaviors of Interest
Goal of Modeling
Modeling Human Behavior
Components of Mathematical Models
Uses of Formulas
Winning at the ‘game’ we call business
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Attributes of a game
Project Success Survey
Predictive Analytics ROI Survey
Predictive Analytics Business Goals
Analytic Goals
Why Predictive Analytics?
Lab 1: Introduction
Types of Models
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Response
Risk
Attrition
Activation
Cross-Sell and Up-Sell
Profile Analysis
Segmentation
Net Present Value
Lifetime Value
Why Predictive Analytics?
Low-Risk / High-ROI Project Design
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Low-Risk / High-ROI Projects
Phased Development Cycle
Positive Impact Behavior Modeling
Negative Impact Behavior Modeling
Conflict Resolution Modeling
Ranking Across the Continuum
Dimensionality Enhancement
Refining Precision
Forecasting
Lab 2: Opportunity Conceptualization
The CRISP-DM Process Model
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Environment Development
Data Sandbox
CRISP Development
o Business Understanding
o Relationship Solution Space
o Determine Modeling Objectives
o Data Understanding
o Data Preparation
o Modeling
 A Sampling of Commercial
Data Mining Software Products
o Validation
o Implementation
Predictive Analytics is Analysis… not Engineering
Business Understanding
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Project Team
Performance Metrics
o Determine Business Objectives
o Conversion: Objectives to Metrics
o Handling Multiple Metrics
o Lift and Gains Chart Interpretation
o Custom Performance Charts
o Enhancing Performance with Threshold Evaluation
o Calculating the Current Baseline: Uplift Analysis
Lab 3: Performance Metrics
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Modeling Objectives
o The Case for Classification
o Prioritize the Dependent Variable
o Precision Requirements
o Training for what “should be done…” not what “was done”
o Confirming Compatibility
o Defining Modeling Objectives
o Resource Availability
Experimental Design
o How Much Data is Needed
to Develop a Model?
o Training Data for Classification
o Training Data for Prediction
o How Many Variables?
o Purpose of Experimental Design
o Experimental Design: Statistics vs. Predictive Analytics
o Data Sets Used
o Type of Data Distribution
Lab 4: Experimental Design & Data Sandbox Construction
Data Understanding
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Data Set Determination
Availability
Requirements Planning
Data Quality Issues
o Data Errors
o Outliers
o Missing Data
Data Types: Behavioral Characteristics
o Demographic Data
o Behavioral Data
o Psychographic Data
Data Types: Mathematical Characteristics
o Qualitative Variables
 Categorical Data
 Nominal Data
o Quantitative Variables
 Ordinal Data
 Interval Data
 Continuous Data
Lab 5: Data Understanding
Data Preparation
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Data Representation Expectations
o Natural Values
o Binning
o Bin Boundary Determination
o Open Ended Ranges
o Collapsed Sets
o 1ofN Representations
o Thermometer Representation
o Bipolar Representation
o Fuzzy Boundaries
o Multiple Boundary Strategies
o Controlling Error
Data Transformation Expectations
o Conversion to Linear
o Converting the Shape of the Distribution
o Ratios
o Roll-ups
o Domain Specific Transformations
Data Resource Consumption Considerations
Data Extraction for Replicability
Lab 6: Data Preparation
Modeling
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Matching Techniques to the Project Goals
o Classification Modeling Techniques
o Forecasting Modeling Techniques
Variable Selection
Candidate Model Evaluation
Lab 7: Model Development
Validation & Evaluation
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Lab 8: Model Evaluation & Validation
Deployment
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End User Interface
Model Run Cycle
Model Maintenance
Summary and Next Steps
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Formal Project Assessment
o Business Understanding
o Data Understanding
o Report of Findings & Recommendations
Resources