Advanced Analytics for Customer Intelligence Using SAS®

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Advanced Analytics for Customer Intelligence Using SAS®

SAS Institute GmbH
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Beschreibung


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.

Voraussetzungen
Before attending this course, you should know how to
  • preprocess data (such as missing values, outliers, categorization, sampling, etc.)
  • 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 cu…

Gesamte Beschreibung lesen

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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.

Voraussetzungen
Before attending this course, you should know how to
  • preprocess data (such as missing values, outliers, categorization, sampling, etc.)
  • 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, etc.).


Zielgruppe
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.

Module
SAS Enterprise Miner, SAS/STAT, SAS/INSIGHT

Kursinhalte
  • 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-
    • 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+


Referent
Christophe Mues, Ph.D., Assistant Professors at the School of Management of the University of Southampton (UK)

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