Predictive Analytics Techniques: What to Use

For Your Big Data

March 26, 2014 Fern Halper, PhD

Presenter

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Agenda

•  Introduction to big data and predictive analytics

•  Popular predictive analytics methodologies – Examples – Guidelines

•  Deployment models

Big Data

5

Social

M2M/IoT

Text

Mobile/Location Volume

Formats

A Confusion of Words

Big Data Analytics

Big  Data  Analy,cs  

Text  analy,cs  

Predic,ve  analy,cs  

Slicing  and  dicing  

Etc.  Visual  discovery  

Link  analysis  

Stream  mining  

Predictive Analytics

A statistical or data mining solution consisting of algorithms and techniques that can be used on both structured and unstructured data to determine outcomes

A Lot of It Is Used to Predict Behavior

•  People – Churn – Marketing – Fraud detection

•  Machine – Operations maintenance

•  And much, much more! Good source for use

cases

Of Course, It Isn’t Just About Modeling

CRISP Lifecycle

A Vast Array of Techniques

Source: TDWI BPR on Predictive Analytics, 2014; n=242

Supervised

•  Use it when you know outcomes of interest – Leave vs. stay – Revenue prediction

•  Need enough data for training, testing, validation

Unsupervised

•  Does not include target information •  Looks for commonalities/hidden structures

in data •  May not produce useful insight •  Is it prediction?

Techniques

•  Supervised –  Classification –  Regression –  Neural networks

•  Unsupervised –  Clustering –  Association

•  Supervised –  Deep learning, auto-encoders –  Decision trees, random

forests, gradient boosting –  Support vector machines,

Bayesian classifiers, principal component, discriminant analysis

•  Unsupervised –  Nearest-neighbor mapping,

k-means clustering, self-organizing maps

–  Factor analysis, link analysis

Decision Trees

Good for classification and prediction with known, discrete outcomes

Linear Regression

Used to predict a continuous variable from independent variables

Artificial Neural Networks (1)

 Biological to Mathematical

Source agh.edu

Artificial Neural Networks (2)

Source: Commonsenseatheism.com

Can be used on a range of problems; good for classification and estimation

Clustering

Source: Babelomics

Used  to  group  observa,ons  by  perceived  similarity    

Association Rule Mining

Transac'on   Items  

1   milk,  leDuce  

2   leDuce,  diapers,  beer,  cookies  

3   milk,  diapers,  beer,  plas,c  bags  

4   leDuce,  milk,  diapers,  beer  

5   leDuce,  milk,  diapers,  plas,c  bags  

Diapers -> Beer

Two concepts: support and confidence

Used to find relationships

Quick Quiz

•  How much revenue will this customer bring? –  Regression

•  Who is going to take a certain action? –  Classification

•  What are my customer segments? –  Clustering

•  If a customer buys X, what else might it buy? –  Association rules

Strengths & Weaknesses: Decision Trees

Strengths •  Easy to understand

–  Rules vs. equations

•  Easy to explain •  Not a black box •  Data doesn’t have to

follow any distribution •  Can handle interactions

between variables

Weaknesses •  Continuous value

predictions •  Can be computationally

expensive to train •  Can have problems if

many classes and few training examples

•  Overfitting

Strengths & Weaknesses: Regression

Strengths •  Simple to use •  Easy to explain

through independent variables

Weaknesses •  Relationship needs to

be linear •  Hard-to-handle

categorical variables or variables that interact

•  Outliers hard to model

Strengths & Weaknesses: Neural Networks

Strengths •  Good for a specific

class of problems •  May be easy to

implement •  Non-linear/interaction

variables

Weaknesses •  Hard-to-explain

output (black box) •  Output might be

unpredictable •  Training can take a

long time

Strengths & Weaknesses: K Means

Strengths •  Good for large

datasets •  Simple •  Efficient

Weaknesses •  Need to specify K

upfront •  Sensitive to outliers,

which may result in incorrect cluster boundaries

•  Needs a mean (categorical data?)

Strengths & Weaknesses: Association Rules

Strengths •  Simple •  Text data

(categorical)

Weaknesses •  Can be

computationally expensive

•  Potential for spurious patterns

•  Rules do not mean causality

Ensemble Modeling

•  Multiple models are combined to solve a problem

Vendors Are Offering a Range of Options for Predictive Analytics

•  UI easier to use: visual vs. code based •  Automation •  Collaboration/interactivity •  Cloud options •  Operationalizing and embedding advanced

analytics

Operationalizing

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An example:

TDWI Big Data Maturity Model

QUESTIONS?