Boston Housing Dataset

1. PatternIdentificationandForecastwiththe Neural Network.Available
from:http://www.utdallas.edu/~xinchou/phys5314-Fall2010-Project8.pdf [Accessed
HARDIK PATNI
PrinciplesofData Mining
BIF-6-PDM
http://vle.lsbu.ac.uk
School ofEngineering
2015/16
Level 6
ID -3621680

2. PatternIdentificationandForecastwiththe Neural Network.Available
from:http://www.utdallas.edu/~xinchou/phys5314-Fall2010-Project8.pdf [Accessed
Boston Housing Data
BusinessUnderstandingandDataunderstanding:
BostonHousinggivesmoderate lodgingoccupantsinandaroundthe Cityof Boston.Occupantsare
helpedthroughablendof openlodgingandgovernmentandstate vouchersponsorshipprograms
that give a wide assortmentof lodgingopenings. Thisreporttries toanalyse the impactof a few
neighbourhoodpropertiesonthe costsof lodging,tryingtofindthe mostreasonable logical factors.
The neighbourhoodascribestobe consideredare nearnesstothe CharlesRiver, how farisitto the
maindistrictcentres,levelsof crime .Thoughthe firstinvestigationconcentratedonair
contaminationutilizingnitrogenoxidefocusesasan illustrative variable,thisreportanalyses
regardlessof whetherthere are other,betterinformativefactorsforthe middle estimationof
housesinBoston.
2. Sources:
(a) Origin: Thisdatasetwas takenfromthe StatLiblibrarywhichis
maintainedatCarnegie MellonUniversity.
(b) Creator: Harrison,D. and Rubinfeld,D.L.'Hedonicpricesandthe
demandfor cleanair',J. Environ.Economics& Management,
vol.5,81-102, 1978.
(c) Date: July7, 1993
5. Numberof Instances:506
6. Numberof Attributes:13 continuousattributes(including"class"
attribute "MEDV"),1 binary-valuedattribute.
7. Attribute Information:
1. CRIM : percapita crime rate by town
2. ZN : proportionof residentiallandzonedforlotsover25,000 sq.ft.
3. INDUS: proportionof non-retailbusinessacrespertown
4. CHAS : CharlesRiverdummyvariable (=1if tract bounds
5. NOX : nitricoxidesconcentration(partsper10 million)
6. RM : average numberof roomsper dwelling
7. AGE :proportionof owner-occupiedunitsbuiltpriorto1940
8. DIS :weighteddistancestofive Bostonemploymentcentres
9. RAD : index of accessibilitytoradial highways
10. TAX :full-value property-taxrate per$10,000
11. PTRATIO: pupil-teacherratiobytown

3. PatternIdentificationandForecastwiththe Neural Network.Available
from:http://www.utdallas.edu/~xinchou/phys5314-Fall2010-Project8.pdf [Accessed
12. B : duplicate
13. LSTAT : % lowerstatusof the population1
14. MEDV : Medianvalue of owner-occupiedhomesin$
Data Pre-Processing-
 Thisis the BostonHousingDatasetinwhichthere are all attributesshown,ourgoal ismake
housingeasilytolowerincome tohigherincomepeople.
1 Pattern Identification and Forecastwith the Neural Network

4. PatternIdentificationandForecastwiththe Neural Network.Available
from:http://www.utdallas.edu/~xinchou/phys5314-Fall2010-Project8.pdf [Accessed
 ThisScatter graph showsthatthe longitude andlatitude changesandcolorchanges
according to crime rate from the blue(the leastcrime rate ) to pink(moderate)andthe
green(highestcrime rate ).the lat,longitude,crim(blue color) (42.03,-70.8525,0.10659) the
leastcrime rate and the highestcrime rate accordingto latitude,longtude(42.23,-
71.046,88.916(crimerate).

Thishistogramsare a populardatareductiontechnique.Inthishistogramithasbeenshown
RM(Numberof Rooms) & MEDV(Medianvalue of owner-occupiedhomesin$) ,ithas been
shownthat the numberof roomshave increasedhigherprice personpays.

5. PatternIdentificationandForecastwiththe Neural Network.Available
from:http://www.utdallas.edu/~xinchou/phys5314-Fall2010-Project8.pdf [Accessed
The decisiontree model issuperiorthanregressiontree .The bestisdecisiontree 2inaverage
squarederrorwhichis 1.178624 anddecisiontree 1 is1.874894.Decison tree 1 hasa countof train
data 166 andvalidation124 .Decisiontree 2has a countof traindata whichis202 and validationis
152.
• RegressionAnalysis
– Modellingandanalysingthe relationshipbetweenadependentvariableandone or
more independentvariables
– Predictinganumericquantity
• LinearRegression
– Representsalinerrelationship
– Dependentisalinearcombinationof independentattributes
The CorrelationPlot:Pearsonshowsthatthe targetattribute isMEDV andit shows a positive
correlationtonumberof rooms.The higherthe price the numberof roomsishigher.
kk
xwxwxwwy  ...22110

6. PatternIdentificationandForecastwiththe Neural Network.Available
from:http://www.utdallas.edu/~xinchou/phys5314-Fall2010-Project8.pdf [Accessed
Stat explore
• The Chi-square plotfeaturesinputsthatare relatedwiththe target.manyof the binned
persistentsourcesof infohave the biggestCramer'sesteems.The pearson'srelationshipcoefficients
are shownif the objective isacontinousvariable.

7. PatternIdentificationandForecastwiththe Neural Network.Available
from:http://www.utdallas.edu/~xinchou/phys5314-Fall2010-Project8.pdf [Accessed
Graph explore showsthe attribute 5to50 whichItargetedisMEDV inwhichthe leastis
MEDV(36.5,41) & Frequency(8).The highestattribute of Medv(18.5,23) & Frequency(154).The
meanis 22.53,The histogramisskewedtothe right.
Model Building:
SASMODEL showsthe appropriate workingtoshow the knowledgeof sasenterprise miner.
Model evaluationandIntepretation:

8. PatternIdentificationandForecastwiththe Neural Network.Available
from:http://www.utdallas.edu/~xinchou/phys5314-Fall2010-Project8.pdf [Accessed
Data MiningModels
1.predictive modelling-
Predictive modellingisthe modellinginwhichwe derive modelsthatcan predictthe value of a
particularattribute basedonthe valuesof otherattributes.The attribute tobe predictedisthe
target.
2.descriptive modelling-Itistoderive patternsthatsummarise the underlyingrelationshipsindata
,e.g.correlations,trendsandanomalieswithoutnotargetvariablwes.theirmaintaskistodo cluster
analysis,associationanalysis&anomalydetection.
Decision treesare createdbycalculations thatdistinguish differentwaysof parta data setinto
branch-like portions.These fragmentsshape amodified Decisiontree thatoriginateswitha
root hubat the bestof the tree.The question of examination isreflectedinthisroot hubas
a straightforward,one-dimensional show inthe Decisiontree interface
The DecisionTree withoutfiltershowswiththe trainaverage is21.8312 and validationis22.6250
and countdata for trainingis202 andvalidationis152.
Node rulesdecisiontreewithoutfilter
Node rules decisiontree withfilter

9. PatternIdentificationandForecastwiththe Neural Network.Available
from:http://www.utdallas.edu/~xinchou/phys5314-Fall2010-Project8.pdf [Accessed
The decisiontree withfiltershowsthatthe trainaverage is21.9482 andValidationis22.3968.Count
is166 intrainingdata and Validationitis124.Then itsplitsintonodes.One onthe leftnode its
shows<5.565 and the rightside node shows>=5.565 or Missing.
Stat explore output showsthe trainingoutputinwhichitshowsthe frequencycount andnumberof
thingstargetedandrejected.Statexplorehasalsoshownthe variable,role,mean,standarddeviation
,nonmissingvalues,missingvalues,median,maximum,skewnessandkurtosis

10. PatternIdentificationandForecastwiththe Neural Network.Available
from:http://www.utdallas.edu/~xinchou/phys5314-Fall2010-Project8.pdf [Accessed
Comparisonbetweenregressionanddecisiontree(expandit) decisiontree isbetterThanregression
tree.

11. PatternIdentificationandForecastwiththe Neural Network.Available
from:http://www.utdallas.edu/~xinchou/phys5314-Fall2010-Project8.pdf [Accessed
Conclusionandsummary:
The conclusionof Bostonhousingdatais thatthere are differentincome peopleandaccordinglythe
pricesdifferon the houses.Lowerthe price of the properlyhigherthe crime rate.If it’sinthe
downtownareaof Bostonthe numberof roomswouldbe lessbecause of the location, althoughif
it’sinthe SuburbsAreasof Bostonthe numberof rooms couldbe highas the locationis notPorsche.

12. PatternIdentificationandForecastwiththe Neural Network.Available
from:http://www.utdallas.edu/~xinchou/phys5314-Fall2010-Project8.pdf [Accessed
References –
PatternIdentificationandForecastwiththe Neural Network.Available
from:http://www.utdallas.edu/~xinchou/phys5314-Fall2010-Project8.pdf [Accessed