Data Mining in Market Research

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Data Mining in Market Research

• What is data mining?
• Methods for finding interesting structure in
  large databases
• E.g. patterns, prediction rules, unusual cases
• Focus on efficient, scalable algorithms
• Contrasts with emphasis on correct inference in
  statistics
• Related to data warehousing, machine learning
• Why is data mining important?
• Well marketed now a large industry pays well
• Handles large databases directly
• Can make data analysis more accessible to end
  users
• Semi-automation of analysis
• Results can be easier to interpret than e.g.
  regression models
• Strong focus on decisions and their implementation

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CRISP-DM Process Model
3
Data Mining Software

• Many providers of data mining software
• SAS Enterprise Miner, SPSS Clementine, Statistica
  Data Miner, MS SQL Server, Polyanalyst,
  KnowledgeSTUDIO,
• See http//www.kdnuggets.com/software/suites.html
  for a list
• Good algorithms important, but also need good
  facilities for handling data and meta-data
• Well use
• WEKA (Waikato Environment for Knowledge Analysis)
• Free (GPLed) Java package with GUI
• Online at www.cs.waikato.ac.nz/ml/weka
• Witten and Frank, 2000. Data Mining Practical
  Machine Learning Tools and Techniques with Java
  Implementations.
• R packages
• E.g. rpart, class, tree, nnet, cclust, deal,
  GeneSOM, knnTree, mlbench, randomForest, subselect

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Data Mining Terms

• Different names for familiar statistical
  concepts, from database and AI communities
• Observation case, record, instance
• Variable field, attribute
• Analysis of dependence vs interdependence
  Supervised vs unsupervised learning
• Relationship association, concept
• Dependent variable response, output
• Independent variable predictor, input

5
Common Data Mining Techniques

• Predictive modeling
• Classification
• Derive classification rules
• Decision trees
• Numeric prediction
• Regression trees, model trees
• Association rules
• Meta-learning methods
• Cross-validation, bagging, boosting
• Other data mining methods include
• artificial neural networks, genetic algorithms,
  density estimation, clustering, abstraction,
  discretisation, visualisation, detecting changes
  in data or models

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Classification

• Methods for predicting a discrete response
• One kind of supervised learning
• Note in biological and other sciences,
  classification has long had a different meaning,
  referring to cluster analysis
• Applications include
• Identifying good prospects for specific marketing
  or sales efforts
• Cross-selling, up-selling when to offer
  products
• Customers likely to be especially profitable
• Customers likely to defect
• Identifying poor credit risks
• Diagnosing customer problems

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Weather/Game-Playing Data

• Small dataset
• 14 instances
• 5 attributes
• Outlook - nominal
• Temperature - numeric
• Humidity - numeric
• Wind - nominal
• Play
• Whether or not a certain game would be played
• This is what we want to understand and predict

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ARFF file for the weather data.
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German Credit Risk Dataset

• 1000 instances (people), 21 attributes
• class attribute describes people as good or bad
  credit risks
• Other attributes include financial information
  and demographics
• E.g. checking_status, duration, credit_history,
  purpose, credit_amount, savings_status,
  employment, Age, housing, job, num_dependents,
  own_telephone, foreign_worker
• Want to predict credit risk
• Data available at UCI machine learning data
  repository
• http//www.ics.uci.edu/mlearn/MLRepository.html
• and on 747 web page
• http//www.stat.auckland.ac.nz/reilly/credit-g.ar
  ff

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Classification Algorithms

• Many methods available in WEKA
• 0R, 1R, NaiveBayes, DecisionTable, ID3, PRISM,
  Instance-based learner (IB1, IBk), C4.5 (J48),
  PART, Support vector machine (SMO)
• Usually train on part of the data, test on the
  rest
• Simple method Zero-rule, or 0R
• Predict the most common category
• Class ZeroR in WEKA
• Too simple for practical use, but a useful
  baseline for evaluating performance of more
  complex methods

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1-Rule (1R) Algorithm

• Based on single predictor
• Predict mode within each value of that predictor
• Look at error rate for each predictor on training
  dataset, and choose best predictor
• Called OneR in WEKA
• Must group numerical predictor values for this
  method
• Common method is to split at each change in the
  response
• Collapse buckets until each contains at least 6
  instances

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1R Algorithm (continued)

• Biased towards predictors with more categories
• These can result in over-fitting to the training
  data
• But found to perform surprisingly well
• Study on 16 widely used datasets
• Holte (1993), Machine Learning 11, 63-91
• Often error rate only a few percentages points
  higher than more sophisticated methods (e.g.
  decision trees)
• Produced rules that were much simpler and more
  easily understood

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Naïve Bayes Method

• Calculates probabilities of each response value,
  assuming independence of attribute effects
• Response value with highest probability is
  predicted
• Numeric attributes are assumed to follow a normal
  distribution within each response value
• Contribution to probability calculated from
  normal density function
• Instead can use kernel density estimate, or
  simply discretise the numerical attributes

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Naïve Bayes Calculations

• Observed counts and probabilities above
• Temperature and humidity have been discretised
• Consider new day
• Outlooksunny, temperaturecool, humidityhigh,
  windytrue
• Probability(playyes) a 2/9 x 3/9 x 3/9 x 3/9 x
  9/14 0.0053
• Probability(playno) a 3/5 x 1/5 x 4/5 x 3/5 x
  5/14 0.0206
• Probability(playno) 0.0206/(0.00530.0206)
  79.5
• no four times more likely than yes

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Naïve Bayes Method

• If any of the component probabilities are zero,
  the whole probability is zero
• Effectively a veto on that response value
• Add one to each cells count to get around this
  problem
• Corresponds to weak positive prior information
• Naïve Bayes effectively assumes that attributes
  are equally important
• Several highly correlated attributes could drown
  out an important variable that would add new
  information
• However this method often works well in practice

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Decision Trees

• Classification rules can be expressed in a tree
  structure
• Move from the top of the tree, down through
  various nodes, to the leaves
• At each node, a decision is made using a simple
  test based on attribute values
• The leaf you reach holds the appropriate
  predicted value
• Decision trees are appealing and easily used
• However they can be verbose
• Depending on the tests being used, they may
  obscure rather than reveal the true pattern
• More info online at http//recursive-partitioning.
  com/

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Decision tree with a replicated subtree
If x1 and y1 then class a If z1 and w1
then class a Otherwise class b
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Problems with Univariate Splits
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Constructing Decision Trees

• Develop tree recursively
• Start with all data in one root node
• Need to choose attribute that defines first split
• For now, we assume univariate splits are used
• For accurate predictions, want leaf nodes to be
  as pure as possible
• Choose the attribute that maximises the average
  purity of the daughter nodes
• The measure of purity used is the entropy of the
  node
• This is the amount of information needed to
  specify the value of an instance in that node,
  measured in bits

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Tree stumps for the weather data
(a)
(b)
(c)
(d)
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Weather Example

• First node from outlook split is for sunny,
  with entropy 2/5 log2(2/5) 3/5 log2(3/5)
  0.971
• Average entropy of nodes from outlook split is
• 5/14 x 0.971 4/14 x 0 5/14 x 0.971 0.693
• Entropy of root node is 0.940 bits
• Gain of 0.247 bits
• Other splits yield
• Gain(temperature)0.029 bits
• Gain(humidity)0.152 bits
• Gain(windy)0.048 bits
• So outlook is the best attribute to split on

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Expanded tree stumps for weather data
(a)
(b)
(c)
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Decision tree for the weather data
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Decision Tree Algorithms

• The algorithm described in the preceding slides
  is known as ID3
• Due to Quinlan (1986)
• Tends to choose attributes with many values
• Using information gain ratio helps solve this
  problem
• Several more improvements have been made to
  handle numeric attributes (via univariate
  splits), missing values and noisy data (via
  pruning)
• Resulting algorithm known as C4.5
• Described by Quinlan (1993)
• Widely used (as is the commercial version C5.0)
• WEKA has a version called J4.8

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Classification Trees

• Described (along with regression trees) in
• L. Breiman, J.H. Friedman, R.A. Olshen, and C.J.
  Stone, 1984. Classification and Regression Trees.
• More sophisticated method than ID3
• However Quinlans (1993) C4.5 method caught up
  with CART in most areas
• CART also incorporates methods for pruning,
  missing values and numeric attributes
• Multivariate splits are possible, as well as
  univariate
• Split on linear combination Scjxj gt d
• CART typically uses Gini measure of node purity
  to determine best splits
• This is of the form Sp(1-p)
• But information/entropy measure also available

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Regression Trees

• Trees can also be used to predict numeric
  attributes
• Predict using average value of the response in
  the appropriate node
• Implemented in CART and C4.5 frameworks
• Can use a model at each node instead
• Implemented in Wekas M5 algorithm
• Harder to interpret than regression trees
• Classification and regression trees are
  implemented in Rs rpart package
• See Ch 10 in Venables and Ripley, MASS 3rd Ed.

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Problems with Trees

• Can be unnecessarily verbose
• Structure often unstable
• Greedy hierarchical algorithm
• Small variations can change chosen splits at high
  level nodes, which then changes subtree below
• Conclusions about attribute importance can be
  unreliable
• Direct methods tend to overfit training dataset
• This problem can be reduced by pruning the tree
• Another approach that often works well is to fit
  the tree, remove all training cases that are not
  correctly predicted, and refit the tree on the
  reduced dataset
• Typically gives a smaller tree
• This usually works almost as well on the training
  data
• But generalises better, e.g. works better on test
  data
• Bagging the tree algorithm also gives more stable
  results
• Will discuss bagging later

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Classification Tree Example

• Use Wekas J4.8 algorithm on German credit data
  (with default options)
• 1000 instances, 21 attributes
• Produces a pruned tree with 140 nodes, 103 leaves

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• Run information
• Scheme weka.classifiers.j48.J48 -C 0.25 -M
  2
• Relation german_credit
• Instances 1000
• Attributes 21
• Number of Leaves 103
• Size of the tree 140
• Stratified cross-validation
• Summary
• Correctly Classified Instances 739
  73.9
• Incorrectly Classified Instances 261
  26.1
• Kappa statistic 0.3153
• Mean absolute error 0.3241
• Root mean squared error 0.4604

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Cross-Validation

• Due to over-fitting, cannot estimate prediction
  error directly on the training dataset
• Cross-validation is a simple and widely used
  method for estimating prediction error
• Simple approach
• Set aside a test dataset
• Train learner on the remainder (the training
  dataset)
• Estimate prediction error by using the resulting
  prediction model on the test dataset
• This is only feasible where there is enough data
  to set aside a test dataset and still have enough
  to reliably train the learning algorithm

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k-fold Cross-Validation

• For smaller datasets, use k-fold cross-validation
• Split dataset into k roughly equal parts
• For each part, train on the other k-1 parts and
  use this part as the test dataset
• Do this for each of the k parts, and average the
  resulting prediction errors
• This method measures the prediction error when
  training the learner on a fraction (k-1)/k of the
  data
• If k is small, this will overestimate the
  prediction error
• k10 is usually enough

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Test
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Regression Tree Example

• data(car.test.frame)
• z.auto lt- rpart(Mileage Weight, car.test.frame)
• post(z.auto,FILE)
• summary(z.auto)

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• Call
• rpart(formula Mileage Weight, data
  car.test.frame)
• n 60
• CP nsplit rel error xerror
  xstd
• 1 0.59534912 0 1.0000000 1.0322233
  0.17981796
• 2 0.13452819 1 0.4046509 0.6081645
  0.11371656
• 3 0.01282843 2 0.2701227 0.4557341
  0.09178782
• 4 0.01000000 3 0.2572943 0.4659556
  0.09134201
• Node number 1 60 observations, complexity
  param0.5953491
• mean24.58333, MSE22.57639
• left son2 (45 obs) right son3 (15 obs)
• Primary splits
• Weight lt 2567.5 to the right,
  improve0.5953491, (0 missing)
• Node number 2 45 observations, complexity
  param0.1345282
• mean22.46667, MSE8.026667
• left son4 (22 obs) right son5 (23 obs)

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• Node number 3 15 observations
• mean30.93333, MSE12.46222
• Node number 4 22 observations
• mean20.40909, MSE2.78719
• Node number 5 23 observations, complexity
  param0.01282843
• mean24.43478, MSE5.115312
• left son10 (15 obs) right son11 (8 obs)
• Primary splits
• Weight lt 2747.5 to the right,
  improve0.1476996, (0 missing)
• Node number 10 15 observations
• mean23.8, MSE4.026667
• Node number 11 8 observations
• mean25.625, MSE4.984375

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Regression Tree Example (continued)

• plotcp(z.auto)
• z2.auto lt- prune(z.auto,cp0.1)
• post(z2.auto, file"", cex1)

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Complexity Parameter Plot
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Pruned Regression Tree
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Classification Methods

• Project the attribute space into decision regions
• Decision trees piecewise constant approximation
• Logistic regression linear log-odds
  approximation
• Discriminant analysis and neural nets linear
  non-linear separators
• Density estimation coupled with a decision rule
• E.g. Naïve Bayes
• Define a metric space and decide based on
  proximity
• One type of instance-based learning
• K-nearest neighbour methods
• IBk algorithm in Weka
• Would like to drop noisy and unnecessary points
• Simple algorithm based on success rate confidence
  intervals available in Weka
• Compares naïve prediction with predictions using
  that instance
• Must choose suitable acceptance and rejection
  confidence levels
• Many of these approaches can produce probability
  distributions as well as predictions
• Depending on the application, this information
  may be useful
• Such as when results reported to expert (e.g.
  loan officer) as input to their decision

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Numeric Prediction Methods

• Linear regression
• Splines, including smoothing splines and
  multivariate adaptive regression splines (MARS)
• Generalised additive models (GAM)
• Locally weighted regression (lowess, loess)
• Regression and Model Trees
• CART, C4.5, M5
• Artificial neural networks (ANNs)

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Artificial Neural Networks (ANNs)

• An ANN is a network of many simple processors (or
  units), that are connected by communication
  channels that carry numeric data
• ANNs are very flexible, encompassing nonlinear
  regression models, discriminant models, and data
  reduction models
• They do require some expertise to set up
• An appropriate architecture needs to be selected
  and tuned for each application
• They can be useful tools for learning from
  examples to find patterns in data and predict
  outputs
• However on their own, they tend to overfit the
  training data
• Meta-learning tools are needed to choose the best
  fit
• Various network architectures in common use
• Multilayer perceptron (MLR)
• Radial basis functions (RBF)
• Self-organising maps (SOM)
• ANNs have been applied to data editing and
  imputation, but not widely

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Meta-Learning Methods - Bagging

• General methods for improving the performance of
  most learning algorithms
• Bootstrap aggregation, bagging for short
• Select B bootstrap samples from the data
• Selected with replacement, same of instances
• Can use parametric or non-parametric bootstrap
• Fit the model/learner on each bootstrap sample
• The bagged estimate is the average prediction
  from all these B models
• E.g. for a tree learner, the bagged estimate is
  the average prediction from the resulting B trees
• Note that this is not a tree
• In general, bagging a model or learner does not
  produce a model or learner of the same form
• Bagging reduces the variance of unstable
  procedures like regression trees, and can greatly
  improve prediction accuracy
• However it does not always work for poor 0-1
  predictors

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Meta-Learning Methods - Boosting

• Boosting is a powerful technique for improving
  accuracy
• The AdaBoost.M1 method (for classifiers)
• Give each instance an initial weight of 1/n
• For m1 to M
• Fit model using the current weights, store
  resulting model m
• If prediction error rate err is zero or gt 0.5,
  terminate loop.
• Otherwise calculate amlog((1-err)/err)
• This is the log odds of success
• Then adjust weights for incorrectly classified
  cases by multiplying them by exp(am), and repeat
• Predict using a weighted majority vote SamGm(x),
  where Gm(x) is the prediction from model m

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Meta-Learning Methods - Boosting

• For example, for the German credit dataset
• using 100 iterations of AdaBoost.M1 with the
  DecisionStump algorithm,
• 10-fold cross-validation gives an error rate of
  24.9 (compared to 26.1 for J4.8)

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Association Rules

• Data on n purchase baskets in form (id, item1,
  item2, , itemk)
• For example, purchases from a supermarket
• Association rules are statements of the form
• When people buy tea, they also often buy
  coffee.
• May be useful for product placement decisions or
  cross-selling recommendations
• We say there is an association rule i1 -gti2 if
• i1 and i2 occur together in at least s of the n
  baskets (the support)
• And at least c of the baskets containing item i1
  also contain i2 (the confidence)
• The confidence criterion ensures that often is
  a large enough proportion of the antecedent cases
  to be interesting
• The support criterion should be large enough that
  the resulting rules have practical importance
• Also helps to ensure reliability of the
  conclusions

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Association rules

• The support/confidence approach is widely used
• Efficiently implemented in the Apriori algorithm
• First identify item sets with sufficient support
• Then turn each item set into sets of rules with
  sufficient confidence
• This method was originally developed in the
  database community, so there has been a focus on
  efficient methods for large databases
• Large means up to around 100 million instances,
  and about ten thousand binary attributes
• However this approach can find a vast number of
  rules, and it can be difficult to make sense of
  these
• One useful extension is to identify only the
  rules with high enough lift (or odds ratio)

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Classification vs Association Rules

• Classification rules predict the value of a
  pre-specified attribute, e.g.
• If outlooksunny and humidityhigh then play no
• Association rules predict the value of an
  arbitrary attribute (or combination of
  attributes)
• E.g. If temperaturecool then humiditynormal
• If humiditynormal and playno then windytrue
• If temperaturehigh and humidityhigh then playno

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Clustering EM Algorithm

• Assume that the data is from a mixture of normal
  distributions
• I.e. one normal component for each cluster
• For simplicity, consider one attribute x and two
  components or clusters
• Model has five parameters (p, µ1, s1, µ2, s2)
  ?
• Log-likelihood
• This is hard to maximise directly
• Use the expectation-maximisation (EM) algorithm
  instead

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Clustering EM Algorithm

• Think of data as being augmented by a latent 0/1
  variable di indicating membership of cluster 1
• If the values of this variable were known, the
  log-likelihood would be
• Starting with initial values for the parameters,
  calculate the expected value of di
• Then substitute this into the above
  log-likelihood and maximise to obtain new
  parameter values
• This will have increased the log-likelihood
• Repeat until the log-likelihood converges

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Clustering EM Algorithm

• Resulting estimates may only be a local maximum
• Run several times with different starting points
  to find global maximum (hopefully)
• With parameter estimates, can calculate segment
  membership probabilities for each case

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Clustering EM Algorithm

• Extending to more latent classes is easy
• Information criteria such as AIC and BIC are
  often used to decide how many are appropriate
• Extending to multiple attributes is easy if we
  assume they are independent, at least
  conditioning on segment membership
• It is possible to introduce associations, but
  this can rapidly increase the number of
  parameters required
• Nominal attributes can be accommodated by
  allowing different discrete distributions in each
  latent class, and assuming conditional
  independence between attributes
• Can extend this approach to a handle joint
  clustering and prediction models, as mentioned in
  the MVA lectures

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Clustering - Scalability Issues

• k-means algorithm is also widely used
• However this and the EM-algorithm are slow on
  large databases
• So is hierarchical clustering - requires O(n2)
  time
• Iterative clustering methods require full DB scan
  at each iteration
• Scalable clustering algorithms are an area of
  active research
• A few recent algorithms
• Distance-based/k-Means
• Multi-Resolution kd-Tree for K-Means PM99
• CLIQUE AGGR98
• Scalable K-Means BFR98a
• CLARANS NH94
• Probabilistic/EM
• Multi-Resolution kd-Tree for EM Moore99
• Scalable EM BRF98b
• CF Kernel Density Estimation ZRL99

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Ethics of Data Mining

• Data mining and data warehousing raise ethical
  and legal issues
• Combining information via data warehousing could
  violate Privacy Act
• Must tell people how their information will be
  used when the data is obtained
• Data mining raises ethical issues mainly during
  application of results
• E.g. using ethnicity as a factor in loan approval
  decisions
• E.g. screening job applications based on age or
  sex (where not directly relevant)
• E.g. declining insurance coverage based on
  neighbourhood if this is related to race
  (red-lining is illegal in much of the US)
• Whether something is ethical depends on the
  application
• E.g. probably ethical to use ethnicity to
  diagnose and choose treatments for a medical
  problem, but not to decline medical insurance

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