the portugese bank’s direct marketing …...the portugese bank’s direct marketing campaign goal...

THE PORTUGESE BANK’s DIRECT MARKETING

CAMPAIGN

GOALTopredictiftheclientwillsubscribetothebank’sterm

depositthroughthecampaignbasedoncalls.

ROADMAPv  Understandingthedatav  ExploratoryDataAnalysisv  FeatureSelectionandEngineeringv  Choosingthemodelv  Evaluationmetrics

THEFIRSTLOOK20Features

•  Numberofrows=41188•  CategoricalFeatures=10•  NumericalFeatures=10•  Fewunknownvalues

ClassImbalanceintarget

INSIGHTSFROMDATA

Preferablecontacttype

Frequencyofsubscriptiondependsonjobtitle.AsAdminandTechnicalrolesarestableroles.

Monthseemstobeanimportantfeatureasthedataisevenlydistributed.Highly,likelytouseHotEncoding.

CORRELATIONNumericalversusNumerical Categorical(housing)versusCategorical

CategoricalColumns P-Value

job 0.0900

y 0.0583

marital 0.0442

education 0.0118

default 0.0103

day_of_week 0.0012

poutcome 0.0000

month 0.0000

contact 0.0000

loan 0.0000

NumericalversusCategorical

HeatMap,CrosstabandChi-Squaredmethodtoidentifycorrelationsbetweendifferentvariables

FEATURESELECTION&ENGINEERINGq  BasedonRandomForestEstimatorandDecisionTreeq  FeatureImportancepredictedq  Top7commonfeaturesareselectedfromboththemethods

q  Binscreatedonthecolumnsage,campaign.q  HandlingOutliersq  Standardizedthecolumneurobi3musingminandmaxq  Labelencodedonallthecategoricalvariablesq MissingValuesHandledq  OversamplingfortheImbalancedClassthroughrandomoversamplingandSMOTE

•  age•  euribor3m•  job•  campaign•  education•  day_of_week•  marital',housing'

RESULTSDataspitted(80–20)randomlytotrainandtestthealgorithms

Addingthecolumn‘Duration’tothemodelincreasestheefficiencyby12%butthecolumnisnotusedtopredictthesubscribersasdurationisnotknownbeforethecallisperformed.

FEATURES:07

ALGORITHM RECALL PRECISION AUCROC

LOGISTICREGRESSION 70% 23.8% 70.9%

RANDOMFORESTS 35.2% 28.8% 62.1%

ANYQUESTIONS?