Advanced Analytics for Customer Intelligence Using SAS

This advanced, highly interactive course will clarify how you can adopt state-of-the-art data mining techniques for complex customer intelligence applications. You will receive a sound mix of both theoretical and technical insights as well as practical implementation details, illustrated by several real-life cases.

Learn how to

  • apply a series of powerful, recently developed, cutting-edge data mining techniques
  • ensure the practical application of these techniques to optimize strategic business decisions
  • explore a futuristic vision of how new emerging data mining techniques might change your key business processes
  • deploy, monitor, and optimally back-test data mining systems.

Who should attend: Those involved in estimating, monitoring, or maintaining predictive models for various types of customer intelligence; those involved with using data mining techniques for various types of customer intelligence

Prerequisites
Before attending this course, you should know how to
  • preprocess data (such as missing values, outliers, categorization, sampling, and so on)
  • develop predictive models using logistic regression
  • develop predictive models using decision trees
  • develop descriptive models using basic segmentation techniques
  • quantify the performance of predictive models (lift curves, ROC curves, and so on).
You can gain this experience by completing Data Mining Techniques: Theory and Practice and Decision Tree Modeling
Course Contents
Predictive Modeling for Customer Intelligence: The KDD Process Model
A Refresher on Data Preprocessing and Data Mining
Advanced Sampling Schemes
  • cross-validation (stratified, leave-one-out)
  • bootstrapping
Neural networks
  • multilayer perceptrons (MLPs)
  • MLP types (RBF, recurrent, etc.)
  • weight learning (backpropagation, conjugate gradient, etc.)
  • overfitting, early stopping, and weight regularization
  • architecture selection (grid search, SNC, etc.)
  • input selection (Hinton graphs, likelihood statistics, brute force, etc.)
  • self organizing maps (SOMs) for unsupervised learning
  • case study: SOMs for country corruption analysis
Support Vector Machines (SVMs)
  • linear programming
  • the kernel trick and Mercer theorem
  • SVMs for classification and regression
  • multiclass SVMs (one versus one, one versus all coding)
  • hyperparameter tuning using cross-validation methods
  • case study: benchmarking SVM classifiers
Opening up the Neural Network and SVM Black Box
  • rule extraction methods (pedagogical versus decompositional approaches such as neurorule, neurolinear, trepan, etc.
  • two-stage models
A Recap of Decision Trees (C4.5, CART, CHAID)
Regression Trees
  • splitting/stopping/assignment criteria
Ensemble Methods
  • bagging
  • boosting
  • stacking
  • random forests
Alternative Rule Representation Formats
  • rule types (oblique, M-of-N, fuzzy, etc.)
  • decision tables (lexicographical ordering, contraction methods, etc.)
  • decision diagrams
  • case study: decision tables and diagrams for customer scoring
Bayesian Network Classifiers
  • naive Bayes
  • tree augmented naive Bayes (TAN)
  • unrestricted Bayesian network classifiers
  • Bayesian inference
  • case study: Bayesian networks for churn prediction
Survival Analysis
  • censoring
  • Kaplan-Meier analysis
  • parametric survival analysis
  • proportional hazards regression
  • neural networks for survival analysis
  • case study: neural network survival analysis for customer scoring
Learning Using Networked Data
  • Markov random fields
  • homophily (guilt by association)
  • local classifiers
  • relational classifiers (relational neighbor, probabilistic relational neighbor, relational logistic regression, etc.)
  • collective inference (Gibbs sampling, iterative classification, etc.)
Monitoring and Backtesting Analytical Models
  • quantitative versus qualitative model monitoring
  • model backtesting (model stability, binomial/Hosmer-Lemeshow test, traffic light indicator approach, impact of macro-economic effects)
  • model benchmarking (internal versus external benchmarking, benchmarking statistics)
  • qualitative validation of analytical models (data quality, model design, documentation, involvement of management)
  • case study: backtesting a customer scoring model
Other Predictive Modeling Techniques (Short)
  • semi-supervised learning
  • genetic algorithms
  • fuzzy techniques
  • ant colony optimization
  • case study: Antminer+
Software Addressed
This course addresses the following software product(s): SAS Enterprise Miner, SAS/STAT, SAS/INSIGHT.

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