Solution Library

Ans. - xxxxxxxxx One xxxxxxxxxxxx Data xxx Identifying xxxxxx Department xx Business xxxxxxxx New xxxxxxxxx University xxx Descriptive xxxxxxxx AnalyticsUsing xxx XYZ xxx ABC xxxxxxxx data xxxx identify xx... ....... - xxxxxxxxx One xxxxxxxxxxxx Data xxx Identifying xxxxxx Department xx Business xxxxxxxx New xxxxxxxxx University xxx Descriptive xxxxxxxx AnalyticsUsing xxx XYZ xxx ABC xxxxxxxx data xxxx identify xxxxxx and xxxx in xxx data xxxxx attributes xx the xxxx sets xxxxx Identify xxxxxxx or xxx data xx either xxxxxxx or xxxxxxxxxx Explain xxx this xxxxxxx the xxxxxxxxx of xxx data xxxxx strategies xxx you xxxxxx to xxxxxxxx the xxxxxx and xxxx in xxx data xxxxxxx your xxxxxxxxx Attributes xxxx the xxxx sets xxxxx are xxxxxxxxx item xxxxxxxx coupon x n xxxxx purchase xxxxxx and xxxxxx customer xxxxx Some xxxxx that x found xxxx the xxx data xxx is xxxx thevariable xx the xxxx has xxxxxxxxx fonts xxx example xx the xxxxx number xxxxxxxx half xx the xxxx has xxxxx font xxx the xxxx is xxxxxxx font xxxx in xxx data xxx another xxxxx that xxx found xx that xxxx number xxx item xxxx are xxx same xxxxx shows xxxxx are xxxxxxxxxx In xxx ABC xxxx set xxxx of xxx data xxxx were xxxxxxx to xxx right xxx some xxxx to xxx middle xxx variable xxxxxx customers xxxx children xx for xxx XYZ xxxx it xxxx has xxx same xxxxxxx with xxx font xxxx part xx the xxxx has xxxxx font xxx the xxxxx has xxxxxxx In xxx XYZ xxxx the xxxxxxxx product xxxx is xxxxx leaving xxx data xxxxxxxxxx The xxxxxxxxx item xxxx and xxxx number xxx also xxx same xx the xxx data xxx The xxxx in xxxxxxxx quantity xx XYZ xxxx some xx the xxxxxxx aligned xxxxxx and xxxx are xx the xxxxx According xx Wen x Zhou x Using xxxxxxxxx fonts xx the xxxx set xxxx not xxxx much xx an xxxxxx on xxx technical xxxxxxxxx of xxx data xxx it xxxxx cause xxxxxxxx withdata xxxxxxx user xxxxxxxxxx and xxxxxx consistency xx contrast xxxxx variables xxxxxxx data xxxxxxxx and xxxxxxxxxxxx undermine xxxx integrity xxx quality xxxxxxxxx in xxxxxxxx issues xx data xxxxxxxxxx and xxxxxxxx The xxxxxxxx I xxxx to xxxx errors xxx gaps xx the xxxx set xx dataprofiling xxx reason x used xxxx profiling xxxxxxxxx to xxxxxxxx P xxxxxxxx C xxxx profiling xxxxxxxx the xxxxxxxxxx analysis xxx summarization xx data xx evaluate xxx quality xxxxxxxxx and xxxxxxx It xxxxxxx analyzing xxxx to xxxxxx patterns xxxxxxxx errors xxxxxxxxxxxxx relationships xxxxxxx assisting xx assessing xxx data's xxxxxxxxxxx for xxx intendedapplications xx analytics xxxxxxxxx or xxxxxxxx decision-making xxxx procedure xxxxxxxxxx an xxxxxxxxx initial xxxxx for xxxxxxxxxxxx initiatives xxxx as xxxx integration xxx data xxxxxxxxx as xx detects xxxxxxxx including xxxxxx values xxxxxxxxxxxxxxx and xxxxxxxx formats xxxxxx new xxxxxxxxx out xx existing xxxx as xxxxxx so xxxx you xxx address xxx joint xxxxxxxxxx team x questions xxx concerns xxxxxxx any xxxxxxxxxxxxx or xxxxxx identified xxxxxxx your xxxxxxxx Analyze xxx spending xxxxxx By xxxxx and xxxxxxxx gender xx regionBy xxxxxxxxx with xxx without xxxxxxxxxxxxxxx the xxxx and xxxxx selling xxxxxxxxxx regionBy xxxxx and xxxxxx By xxxxxxxx customers xxxx and xxxxxxx children xxxxxxx the xxxxxxxxxxxxx of xxxxxxxxxx regionBy xxxxx and xxxxxx By xxxxxxxx customers xxxx and xxxxxxx childrenFirst xxxx I xxxx was xx clean xxx order xxxxxx data xx ABC xxxxxxx by xxxxxxxx the xxxxx C xx various xxxx I xxx that xx first xxxxxxxx the xxxx type xx text xxxx replacing xxx C xxx then xxxxxxxxxxxx it xx whole xxxxxx Next xxxx was xx transform xxx data xx add xxxxx and xxxxxx I xxxxx month xxxxx Column xxxx Example xxx choosing xxxxx from xx After xxxx added xxxxxx using xxxxxxxxxxx Column xxxxxxxx of xxxxxxxx trendsPeak xxxxx in xxxxx Lowest xx OctoberHighest xxxx in xxxxxx and xxxxxx in xxxxxxx spending xxxx than xxxxxxxxxxxx has xxx highest xxxxxx and xxxxx has xxx lowestAlmost xxxxx total xxxx for xxxxxxxxx with xxx without xxxxxxxx Analysis xx worst xxx bestselling xxxxxxxxxxxxx there xxx states xxxx state xxx its xxx best xxx worse xxxxxxx items xxxx is xxxx for xxx four xxxxxxx and xxxxxx Even xxx data xx huge xx be xxxxxxxxx gender xx with xx without xxxxxxxx However xxx top xxxx best xxxxxxx items xxxxxxxxxx waterCincinnati xxxxxxxxxxx Alla xxxxxxxxxxx sandwichToasted xxxxxxxxxxx worst xxxxxxx items xxx Chocolate xxxxxxxxx RingsSpaghetti xxxxxxxx HighlightWhole-Wheat xxxxx Analysis xx revenueBest xxxxxx revenue-wise xxxxxxxxxx Florida xxxxx New xxxx OhioWorst xxxxxx revenue-wise xxxxx Delaware xxxxxxx North xxxxxx Puerto xxxxxxxxxxx revenue xxxxxxxxxxx in xxxxxxxxxx order xxxxxx Winter xxxxxx FallMonths xxxx highest xxxxxxx April xxxxx February xxxxxxx AugustMonths xxxx low xxxxxxx October xxxxxxxx November xxxx SeptemberMale xxx to xxxxxx revenue xxxx women xxxxxxxx with xxxxxxxx led xx slightly xxxxxx revenue xxxx customer xxxxxxx children xxxxxx various xxxxxxxxxxxxxx for xxxx variable xxxxxxxx each xxxxxxxx in xxx data xxx each xxxxxxxx create xxxxxxxxxxx visualizations xxx explain xxx they xxxxxxx the xxxxxxxxx Analyze xxx shape xx the xxxxxxxxxxxxxx Is xxx data xxxxxxx in xxx particular xxx or xx it xxxxxxxx scattered xxxxxxx Are xxxxx any xxxxxxxx If xx what xxxxx they xxxxxxxx Identify xxx explain xxx patterns xxx trends xx the xxxxxxxx base xxxx states xxxx the xxxxxxx and xxxxxx profit xxxxxxx Which xxxxxxxxxx category xxx the xxxxxxx and xxx lowest xxxxxxx check xx there xxx pattern xx average xxxxxx by xxxx of xxx AM xx PM xxxx about xx month xx the xxxx Produce xxxxxxx statistics xxx each xxxxxxxx Include xxx following xxxxxxx tendencyMeasures xx dispersionShape xx the xxxx s xxxxxxxxxxxxxx there x large xxxxxx of xxxxxxx data xx so xxx does xxxx impact xxxx analysis xxxxxxx Variable xxxxxxxxxxxxxxxxxxxxx and xxxxxxxxxxxxxxxxxxxxxxxxx variables x restaurant xxxxx item xxxxxxxx gender xxxxxxxx payee xxxxxx order xxxxxxxx methodQuantitative xxxxxxxxx - xxxx total xxxx Revenue xxxxxxxx menu xxxxx order xxxxx costVisualization xxx narrativeRevenue xx State xxxxxxxxx Bar xxxxx showing xxxxxxx by xxxxxxxxxxxx footprint xxxxx merged xxxxxxx Skewed xxxxxxx few xxxxxx but xxx scattered xxxxxxx is xx outlier xx major xxxxxxx of xxx revenue xx generated xxxx Average xxxxx value xx category xxx time xx the xxx matrix xxxxxxx the xxxxxxxxxx time xx day xxx average xxxxx order xxxx Customer xxxxxx Used x stacked xxx chart xx show xxx gender xxx customer xxxx children xx identify xxx persona xx the xxxxxxxx This xxxxx an xxxxxxxxxxx trend xxxxxxxx data xx almost xxxxx for xxx genders xxx brand xxx universal xxxxxx across xxxxxxx as xxx likelihood xx a xxxxxxxx having xxxxxxxx remains xxxxxxxx regardless xx gender xxxxx shows xxxx spikes xxx October xxxx a xxx dip xxxxxxxx and xxxxxxxxxxxxx revenue xx Florida xxxxxx in xxxxxxxxxxxx average xxx item xxxx is xxx beverage xxx lowest xx for xxxxxxxxxxxxx have xxxx sales xxxx eveningsHighest xxxxxxx in xxxxxx and xxxxxx in xxxxxxxxxxxxxxx statistics x for xxxxx Order xxxxxxxxx Standard xxxxx Median xxxx Standard xxxxxxxxx Sample xxxxxxxx Kurtosis xxxxxxxx Range xxxxxxx Maximum xxx Count xxxxxxxxxxxxx K xxxx D xxxxxxxxxx career xxxxxxxx and xxxxxxxxxxxxxxx development xxx university xxxxxxxx using xxxxxxxxxx intelligence xxx machine xxxxxxxx Journal xx Computational xxxxxxx in xxxxxxxx Engineering xxxxx doi xxx Koukaras x Tjortjis x Data xxxxxxxxxxxxx and xxxxxxx Engineering xxx Data xxxxxx Techniques xxxxx and xxxx Practices xx https xxx org xx Read More

Ans. Milestone xxxxxx - xxxxxxxxxxx Business xxxxxxxx Milestone xxxxxxxx cleaning xxx transforming xxx data xx Milestone xxx I xxx move xx more xxxxxxx analytics xx Milestone xxx where x will xx identifyin... ....... Milestone xxxxxx - xxxxxxxxxxx Business xxxxxxxx Milestone xxxxxxxx cleaning xxx transforming xxx data xx Milestone xxx I xxx move xx more xxxxxxx analytics xx Milestone xxx where x will xx identifying xxx addressing xxx stakeholders xxxxxxxxxxxx These xxxxxxxx seem xx be xxxxxxxx for xxx leadership xx develop x three-year xxxx Identifying xxxxxxxxxxxx requirementsBased xx the xxxxxxxxxxx Requirements xxxxxxxx the xxxxxxxxx key xxxxxxxxxxxx have xxxx identified xxx the xxxxx merged xxxxxxx business xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx ManagersSalesRegional xxxxx PerformanceA xxxxxxxxxx of xxxxxxxx sold xxxxxx various xxxxxxxxxxxx reason xx identify xxx best xxxxxxxx selling xxxx Marketing xxxxxxxxxxxxxxxxxxxxxxxxxx Sales xxxxxxxxxxxx of xxxxxxx sale xx various xxxxxx and xxxxxxx quarters xx plan xxxxxxxxxxxxxxxxx and xxxxxxxxxxxxxxxxxxxxxx and xxxxxxxxxxxxxxxxxxxxx SegmentationTo xxxxxxxxxx the xxxxxxxx behaviour xxxxxxx the xxxxxxx as xxxx between xxxxxxx without xxxxxxxx familiesProduct xxxxxxxxxx t xxxxxxxxxxxxxxxxxxxxxxxxxxxx EvaluationAn xxxxxxxxxx of xxxxxxxxxxx betweencustomers xxxx and xxxxxxx children xx guide xxx creation xx family-oriented xxxx deals xx well xx veg xx non-veg xxxxx mealsFinanceDirectorFinanceFinancialProfitabilityRevenue xxx profit xxxxxxx under xxxxxxxxxxxxxx like xxxxx item xxxxxxxx region xxx The xxxxxxxx will xx centred xxxxxx the xxxxxxxxxxxx of xxx stakeholder xxxx therequirements xxx not xxxxxxx stated xxx analysis xxxx fail xx it xxxx not xxxx the xxx goal xxxx once xx know xxx requirements xxx data xxx be xxxxxx accordingly xxx example xx find xxxxxxxxx every xxxxx I xxxx to xxxxx create x column xxxx will xxxxxxx the xxxxx of xxxx or xx I xxxx to xxxxxxxxx the xxxx by xxxx of xxxx then x have xx have x column xxxxxxx AM xx PM xx reflect xxx time xx the xxx Descriptive xxxxxxxx methodsK-means xxxxxxx analysis xx Regression xxxxxxxx vs xxxxxxxx Tree xxx analysis xxxx be xxxx on xxxxx heads xxxxxxx Goal xxxx Usage xxx Stakeholder xxxxxxxxxxxxxxxxxxxxxxxxxxxxx AnalysisDecision xxxxxxxxxxxx GoalsDiscovery xx data xx grouping xxxx into xxxxxxxx based xx similarity xxxxxxxxxxx like xxxxxx IBM xxxxxxxxx Predicts xxx relationship xxxxxxx a xxxxxxxxx variable xxx an xxxxxxxxxxx variableClassifies x user xxxxxxx using x flow-chart xxxxxxx the xxxx a xxxxxxxx takes xx reach xxxxxxxxx Oracle x i xxxx UsageWorks xx various xxxx sets xxxxxxxx the xxxx is xx predict xx needs xxxxxx an xxxxxxx is xx be xxxxxxx thevariables xxxxxxxx or xxxxxxxxxxxxx without xxxxxxx a xxxxxxxxx data xx avariable xxxxxxx to xxxxxxx the xxxxx for xxxx month xxxxxxxxxxxx month xxxxxxxx has xx be xxxxxxxx Forexample xx figure xxx which xxxxxxxxxxxx more xxxxxxxxxxx BenefitIdentifies xxxxxxxx Personas xxxxxxx spent xx families xxxx children xxxxxxxxxx Identifies xxx much xxxxx will xxxxxx if x specific xxxxx is xxxxxxxxxxxxxxxxxxx the xxxxx of xxxxxxxxxxxxxxxxx based xx specific xxxxxx likeregion xx genderModel xxxxxxxxx for xx analysis xxxxxx cluster xxxxxxxx is xxxx suited xxxxx for xx analysisSince xxx joint xxxxxxxxxx team xxxxx to xxxxxxxxxx the xxxxxxx state xx merged xxxx from xxx newly xxxxxx business xxxx need xx independent xxxxxxxx on xxxxxxx factors xxx leadership xx currently xxx focused xx prediction xxx next xxxxx sales xx figuring xxx the xxxxxxxxxxx of xx outcome xxxx they xxxxxxxxxxxxx in xx to xxxxxxx the xxxx to xxxxxxxxxx the xxxxxxx position xxxxx states xxxxx the xxxx or xxxxx the xxxxx which xxxxxxxx works xxxxx gender xxxxx month xxx Sharda xx al xxxxxxxxxx is xxxxxxxxx used xxxx the xxxxxxxxxx team xxxxx to xxxxxxx the xxxxx or xx predict xxx next xxxxxx sale xxxxxxxx tree xxxx help xxxx understand xxx probability xx an xxxxxxx If xxxx had xx choose xxxxx project xx invest xx out xx amultitude xx options xxxx could xxx decision xxxxx and xxxxxxxxxxx models xxxxxxx today xxxx are xxxx interested xx understanding xxx combined xxxxxx and xx iron xxx issues xxxx which xxxxxxxx to xxxxx upon xxxxx state xx focus xx which xxxx to xxxxxx more xxx Only xxxxxx cluster xxxx help xxxx understand xxxx K-mean xxxxxxx AnalysisThree xxxxxxxxx cluster xxxxxx and xxxxxxxxxxx Listed xxxxx the xxxxx models xxxx and xxx reasoning xxxxxx them xxxxx For xxx first xxxxx I xxxx item xxxxx cost xxx quantity xxxxxxx count xxx automatic x was xxxxxxxx on xxxxxx vs xxxxxxxx to xxxxxxxx high- xxxxx vs xxxxxxxxxxxx customers xxxxx - xxx this xxxxx I xxxx item xxxxx cost xxxxxxxx and xxxx price xxxxxxx count xxx set xx I xxx focusing xx Price xxxxxxxxxxx to xxx the xxxxxx of xxxxxxxxx items xxxxxxxxx by xxx customers xxxxx Third xxxxx was xxxxx on xxxx total xxxx customer xxxx children xxx gender xxxxxxxx payee x was xxxxxxxx on xx Demographic xxxxxxx to xxxxxx specific xxx and xxxxxxx Development xxxxxxxxxxx needs xxxxxxx count xxx set xx Best xxxxx and xxx As xxx me xxx Model xxxxx is xxxxx on xxxxxxxxxxx data xxxxx fits xxx scenario xxxxxx being xxxx this xxxxx incorporates xxx stakeholder xxxxxxxxxxxx regarding xxxxxx and xxxxxx status xxxxxx the xxxxxx directly xxxxxxxxxx for xxx three- xxxx plan xxxxx cluster xxxxxxx the xxxx discrete xxxxxxxxx Discrete xx this xxxxxxxxxxxxxx to xxxxxxxx clusters xxxx have xxxxxx or xx overlaps xxx the xxxxxxxxxx are xxxxx Going xx this xxx first xxxxx is xxx most xxxxxxxx as xxx number xxxxxxxxxxx are xxxxxx Variables xxxx customer xxxx children xxx gender xxxxxxxx payee xxxxx was xxxx in xxxxx will xxxx overlaps xxxxx is xxxx evident xx the xxxxxxx images xxx These xxxxxxxxx Inform xxx Analysis x have xxxx the xxx data xxx transformed xx into xxxxxxxx Persona xx that xxx joint xxxxxxxxxx team xxx do xxxxxxxx marketing x The xxx Manager xxx use xxx High-Spending xxxx cluster xx market xxxxxxx individual xxxxx while xxx Value- xxxxxxx Families xxxxxxx receives xxxxxxx for xxxxxxx Menu xxxxxxxxxxxx - xxx Product xxxxxxxxxxx Manager xxx identify xxxxx products xxx favorites xxx expand xxxxx offerings xxxxxxxxx planning xxx identify xxxxxxx and xxxxxx staffing xxx inventory xxxxxxx statistics xxx each xxxxxxxxxxxxxx statistics xxxx each xxxxx from xxxxxxx is xxxxxxxxx belowModel xxxxxxx belowInputs xxx ClusteringVariables xxx of xxxx Total xxxxxxx of xxxxxxxx With xxxxxxxx Gender xxxxxxxx PayeeLevel xx Detail xxxxxx Customer xxxxxxxxxxxx NormalizedSummary xxxxxxxxxxxxxxxxx of xxxxxxxx Number xx Points xxxxxxxxxxxxx Sum xx Squares xxxxxxxxxxxx Sum xx Squares xxxxx Sum xx Squares xxxxxxxxxxx CommonClustersNumber xx ItemsSum xx Item xxxxx CostSum xx Customer xxxx ChildrenGender xxxxxxxxxxxxxxxxxxxx MCluster xxxxxxxx MCluster xxxxxxxx FNotClustered xxxxx Summary xxxxxxxxxxx for xxxxxxxxxxxxxxxxxxx Sum xx Item xxxxx CostSum xx Customer xxxx Children xxxxxx Customer xxxxxxxxxx of xxxxxx Not xxxxxxxxxxxxxxxxx NormalizedSummary xxxxxxxxxxxxxxxxx of xxxxxxxx Number xx Points xxxxxxxxxxxxx Sum xx Squares xxxxxxxxxxxx Sum xx Squares xxxxx Sum xx Squares xxxxxxxxxxx CommonClustersNumber xx ItemsSum xx Item xxxxx CostSum xx Customer xxxx ChildrenGender xxxxxxxxxxxxxxxxxxxx MCluster xxxxxxxx FCluster xxxxxxxxxxxxx Model xxxxxxx belowInputs xxx ClusteringVariables xxx of xxxx Total xxxxxxx of xxxxxxxx With xxxxxxxx Gender xxxxxxxx PayeeLevel xx Detail xxx AggregatedScaling xxxxxxxxxxxxxxxxx DiagnosticsNumber xx Clusters xxxxxx of xxxxxx Between-group xxx of xxxxxxx Within-group xxx of xxxxxxx Total xxx of xxxxxxx CentersMost xxxxxxxxxxxxxxxxxxxx of xxxxxxxx of xxxx Total xxxxxxx of xxxxxxxx With xxxxxxxxxxxxxx CustomerPayeeCluster xxxxxxxx MCluster xxxxxxxx FNotClustered xxxxxxxxxx the xxxxxxxxx Customer xxxxxxxx Based xx the xxxxxxxxx request xxx subgroups xxx as xxxxxx Subgroup x - xxxx item xxxxx cost xxx high xxxxxxxx This xxxxx consists xxxxxxxxx of xxxxxxxxx with xxxxxxxx often xxxxxxxx during xx hours xxxxxxxx B x Moderate xxxx total xxxx but xxx quantity xxxxx are xxxxxxxxx single xxxxxx often xxxx visiting xxxxxx AM xxxxx hours xxxxxxxx C x Low xxxx total xxxx and xxxx frequency xxxxx customers xxx low- xxxxxx items xxx are xxxxxx sensitive xx price xxxxxxx Compare xxx Contrast xxxxxxxxx comparing xxx subgroups xxxx us xxxx interesting xxxxxxxx Revenue xx volume x Subgroup x drives xxx highest xxxxxx size xxxxxxx Check xxx may xxxxx less xxxxxxxxxx than xxxxxxxx C xxxxxxxxxxx divide xxxxxxxx are xxxxxxx customers xxxxx subgroup x are xxxxxxx customers xxxxxxxxxxx Clusters xxxxxxx in xxxxxxxxxx happen xxxx data xxxxxx share xxxxxxx characteristics xx multiple xxxxxxxxx Some xxxx customers xxx have xxxx a xxxxxxxxxxx purchase xxx might xxxx been xxxxx to xxx family xxxxx Families xxxxxxxx above xxx average xxx not xx necessarily xxxxxx children xxxxxxxxxx Stakeholder xxxxx My xxxxxxxx has xxxxxxxxxxx raw-data xxxx actionable xxxxxx helping xxx stakeholders xx developing x three-year xxxxxxxx plan xx defining xxxxxxxxx and xxxxxxxxxxx personas xxx marketing xxxx can xxxxxx better xxxxxxxxx By xxxxxxxxxxxx menu xxxxx that xxxxx volume xxx margins xxxxxxxxx team xxx focus xx product xxxxxxxxxxxx and xxxxxxxxxxxxxxx volume xx different xxxx of xxx can xxxx resource xxxxxxxx ReferencesIBM xxxxxxxxx What xx clustering xxx https xxx ibm xxx topics xxxxxxxxxxxxxxxx n x What xx a xxxxxxxx tree xxxxxx Data xxxxxxx https xxx oracle xxx artificial-intelligence xxxxxxxxxxxxxxxx what-is-a-decision-tree xxxxxx R xxxxx D xxxxxx E xxxxxxxx intelligence xxxxxxxxx and xxxx science x managerial xxxxxxxxxxx th xx Pearson Read More

Ans. T-Test xxxxxxxx Report xxxxxxxx Ordering xxxxxxxxxxx report xxxxxxxx differences xx customer xxxxxxxx behavior xx time xx day xxx restaurant xxxx Independent xxxxxx t-tests xxxx conducted xx Microsoft... ....... T-Test xxxxxxxx Report xxxxxxxx Ordering xxxxxxxxxxx report xxxxxxxx differences xx customer xxxxxxxx behavior xx time xx day xxx restaurant xxxx Independent xxxxxx t-tests xxxx conducted xx Microsoft xxxxx to xxxxxxxxx whether xxxxxxxx differences xx order xxxxxx and xxxxx value xxx statistically xxxxxxxxxxx or xxx to xxxxxx chance xxxx Ramdas xxxxxxx I xxxxxxxxx whether xxxxxxxx ordering xxxxxxxx differs xx time xx day xx excel xxxxxx were xxxxxxx into xxxxxxx AM xxx afternoon xx categories xxxxx the xxxxx time xx the xxx variable xxx analysis xxxxxxxx differences xx both xxxxx volume xxx average xxxxx amount xxxx the xxxxxx we xxx say xxxx the xxxxxxx order xxxxxx AM xx and xxx average xxxxx amount xx is xx is x two xxxxxx test xxxxxxxxx the xx value xx At xxxxxxxxxxxx level xxx difference xx average xxxxx amount xxxxxxx morning xxx afternoon xxxxxx is xxx statistically xxxxxxxxxxx because xxx p-value xx greater xxxx This xxxxxxxxx that xxx small xxxxxxxxxx in xxxxxxx spending xxxxxxx AM xxx PM xxxxxx is xxxxxx due xx random xxxxxxxxx rather xxxx a xxxx behavioral xxxxxxxxxx Order xxxxxx was xxxxxxxx by xxxxxxxx the xxxxxx of xxxxxxxxxxxx in xxxx time-of-day xxxxx From xxx output xx can xxx that xxxxxxxxx orders xxx marginally xxxxxx than xxxxxxx orders xxx the xxxxxxxxxx is xxxxxxx and xxx large xxxxxx to xxxxxxxx a xxxxxx preference xxx one xxxx period x created x binary xxxxxxxx to xxxxxxxx restaurants xx either xxxxx Place xx Non xxxxx Place xxx then xxxxxxxxx Independent xxxxxxxxxx t-tests xxxxxxxx unequal xxxxxxxxx to xxxxxxx both xxxxx volume xxx average xxxxx amount xxxxxxx these xxx groups xxxx the xxxxxx we xxx say xxxx Pizza xxxxxx had xx average xxxxx total xx while xxxxxxxxx restaurants xxx a xxxxxxxx higher xxxxxxx of xxx two-tailed xxxxxxx from xxx t-test xxx is xxxxxxx than xxx significance xxxxx Therefore xxxxx is xx statistically xxxxxxxxxxx difference xx the xxxxxxx order xxxxxx between xxxxx Places xxx Non- xxxxx restaurants xxxxxxxxxx that xxxxxxxxxx type xxxx not xxxxxxxxxxxx influence xxxxxxx order xxxxx When xxxxxxxxxxx delivery xxxxxx the xxxx of xxx analysis xxxxx that xxxxxxxxxxx between xxxxxxx and xxxxxxxxx orders xxxxxx marginally xxxxxxxxxxx when xxx significancelevel xx relaxed xx This xxxxxxxxx some xxxxxxxx that xxxxxxx orders xxx be xxxxxxxx higher xxxxxxxx the xxxxxxxx of xxxx evidence xx weak xxxxxxxx deliveries xx only xxxxxxxx could xxxxxxxxxx impact xxxxxxxxxxx with xxxxxxxxxxx post-noon xxxxx particularly xxxxxxxxx restaurants xxxxx generate x higher xxxxxxx order xxxxxx Based xx these xxxxxxxx leadership xxxxxx not xxxxxx Pizza xxxxxx exclusively xxx restrict xxxxxxxxxx strictly xx mornings xxxxxxx a xxxxxxxx delivery xxxxxxxx that xxxxxxxxxxx high-volume xxxxxxxxx across xxxx morning xxx afternoon xxxxxxx is xxxxxxxxxxx to xxxxxxx operational xxxxxxxxxx with xxxxxxx retention x t-test xxx used xxxxxxx leadership xxxxxx to xxxxxxx average xxxxx values xxxxxxx two xxxxxx at x time xxxx as xxxxxxx vs xxxxxxxxx orders xxx pizza xx non-pizza xxxxxxxxxxx Test xx this xxxx it xx more xxxxxxxxxxx for xxxxxxxxxxx whether xxxxxxxxxxx between xxx group xxxxxxxx are xxxxxxxxxx whereas xxxxx tests xxx better xxxxxx for xxxxxxxxx three xx more xxxxxx The xxxxxxx showed xxxx the xxxxxxxx were xxxxxxx than xxxxxxx the xxxxxxxxxxx in xxxxxxx order xxxxxxx between xxx groups xxxx not xxxxxxxxxxxxx significant xxxxxxxxxx that xxx observed xxxxxxxxxxx could xxxx occurred xx chance xx cannot xx highly xxxxxxxxx that xxxx of xxx or xxxxxxxxxx type xxx a xxxx impact xx order xxxxx Therefore xxxxx factors xxxxx should xxx drive xxxxx delivery xx operational xxxxxxxxx ReferencesKeselman x J xxxxxx A x Wilcox x R xxxxxxxx K xxx new xxx improved xxxxxxxxxx t xxxx Psychological xxxxxxx - x ller x K x more xxxxxx t-test xxxxxx of xxxxxxxxx and xxxxxxxxxx - xxxx O x T xxxxxxxxxxxxxxxxxxx T xxxx An xxx SPSS xxxxxxxxx to xxxxxxxxx Analysis xxxx H xxxxxx A xxxxxxxxxxxxx t-tests xxx confidence xxxxxxxxx for xxxxxxxx means xxxx unknown xxxxxxxx Sequential xxxxxxxx - Read More

Ans. IntroductionThe xxxxxxxxx of xxx analysis xx to xxxxxxxx potential xxxxxxxxxxxxx for xxxxxxxx expansion xxx an xxxxxxxxxxxx that xxxxxxxxxxx pizza xxxxxxxxxxx dough xxxxxx and xxxxxxxx to xxxxxxxxxxx... ....... IntroductionThe xxxxxxxxx of xxx analysis xx to xxxxxxxx potential xxxxxxxxxxxxx for xxxxxxxx expansion xxx an xxxxxxxxxxxx that xxxxxxxxxxx pizza xxxxxxxxxxx dough xxxxxx and xxxxxxxx to xxxxxxxxxxx across xxx United xxxxxx I xxxxxxx tableau xxxx using x clean xxxxxxxx sales xxxxxxx for xxxxxxxx with xxxxxxxxx income xxxx by xxxxxxxxxx location xx analyze xxxxxxxx distribution xxxxxxx generation xxx demographic xxxxxxxxx Batt xxxxxxx Harmon xxxxxxxxx Creating xxx analyzing xxxx at xxxx the xxxxx and xxx code xxxxxx the xxxxxx identifies xxxxx with xxxxxx revenue xxxxxxxxxxx evaluates xxx household xxxxxx relates xx restaurant xxxxx and xxxxxxxxxx underserved xxxxxxxxxxx markets xxxx present xxxxxxxxxxxxx for xxxxxxxxx I xxxx Restaurant xxxxx Restaurant xxx Code xxxxx Total xxxxxxxx Customer xx and xxxxxx Household xxxxxx variables xx create xxxx Firstly x created xxx to xxxx out xxx number xx customers xx each xxxxx in x S xxxx the xxx New xxxx has xxx highest xxxxxx of xxxxxxxxx with xxxxxxxx by xxxxxxxxxx with xxx map xxxxxxxxx that xxxxxx AK xxx Wyoming xx have xxx lowest xxxxxx of xxxxxxxxx with xxxx Count xx Customers xx U x StateRevenue xx US xxxxx shows xxx total xxxxxxx generated xx restaurants xx each x S xxxxx States xxxx as xxx York xxxxxxxxxx New xxxxxx generate xxx highest xxxxx revenue xxxxxx generates xxx least xxxxxxx among xxx states xxxx aligns xxxx the xxxxxxx customer xxxxx analysis xxxxx both xxxxxx also xxx the xxxxxx customers xxxxxxx by xxxxx Sum xx Order xxxxx ANext x did xxx comparision xx Revenue xxx Median xxxxxxxxx Income xx State xxx map xxxxx that xxxxxx with xxxxxx median xxxxxxxxx incomes xxxxxxxxx tend xx generate xxxxxxxxxxxxxxxx revenue xxx instance xxxxxx such xx New xxxx and xxxxxxxxxx display xxxx higher xxxxxx levels xxx stronger xxxxxxx performance xxxxxxxxxx that xxxxxxxxxxx in xxxxxxxxxxxxxx may xxxxx larger xx more xxxxxxxx ingredient xxxxxx supporting xxx idea xxxx income xx an xxxxxxxxx driver xx sales xxxxxxx the xxxxxxxxxxxx is xxx perfectly xxxxxxxxxx across xxx states xxxx states xxxx moderate xxxxxxxxx income xxxxx generate xxxxxxxxxx strong xxxxxxx while xxxxxxx higher-income xxxxxx show xxxx average xxxxxxx performance xxxxxxxxxx revenue xxxx median xxxxxxxxx income xxxx provide x useful xxxxxxxxxx of xxxxxxxxx product xxxxxxxxx success xxxxxx that xxxxxxx above-average xxxxxxxxx incomewith xxxxxxxx or xxx current xxxxxxx represent xxxxxx expansion xxxxxxxxxxxxx These xxxxx likely xxxx the xxxxxxxxxx power xx support xxxxxxxxx sales xxx may xxxxxxxxx be xxxxxxxxxxx by xxx organization xxxxx revenue xxx with xxxxxx overlayWhen xxx geographic xxxxx is xxxxxxx from xxxxx to xxx Code xxx analysis xxxxxxx that xxx code xxxxxxxxx the xxxxxxx total xxxxx value xxxx a xxx of xxxxx Total xxxxx to xx indicates xxxx restaurants xxxxxxx in xxxx zip xxxx are xxxxx the xxxxxxxxxxxxxxxxxxx in xxxxx of xxxxxxxxxx purchasing xxx zip-code-level xxxxxxxx reveals xxxx ZIP xxxx has xxx lowest xxxxx order xxxxx with x SUM xx Order xxxxx equal xx This xxxxxxxxx extremely xxxxxxx purchasing xxxxxxxx fromrestaurants xx this xxxx during xxx period xxxxxxxx Zip-code-level xxxxxxx mapThe xxxxxxxx indicates xxxxxxx areas xxxxx business xxxxxxxxx and xxxxxxxx marketing xxxxx yield xxxxxx results xxxxxxxxxxxx states xxxx as xxx York xxxxxxxxxx and xxx Jersey xxxxxxx demonstrate xxxxxxxxxxx customer xxxxxxx and xxxxxxxxxx purchases xxxxxxxxxx that xxxxx regions xxx mature xxxxxxx However xxxxx states xxxx moderate xxxxxxx but xxxx household xxxxxx represent xxxxxxxxxxx markets xxxxx restaurants xxxxxx have xxx purchasing xxxxx to xxxxx larger xx more xxxxxxxx orders xxxxxxxxx Mortimer xxxxxxxxx campaigns xx these xxxxx could xxxxxxxxx the xxxxxxx and xxxxxxx of xxxxxxxxxxx introduce xxxxxxxxxxx bundles xx provide xxxxxxxxxxxx discounts xx encourage xxx and xxxxxx orders xxxx zip xxxxx like xxxxxxx well xxx others xxxx low xxxxxxxxxx presence xxxxxxxxx into xxxxx areas xxx capture xxxxxxxx revenue xxxxxxxxx Also xxxxxxx supplier xxxxx and xxxxxxxxx helps xxxxxxxx delivery xxxxxx reduce xxxxx and xxxxxxxx opportunities xxx partnerships xx market xxxxxxxxxxx in xxxxxxxxxxxxxx regions xxxxxxx Nandeshwar xxxxxx Floyd xxxxxxx population xxxxxxx restaurant xxxx competitor xxxxxxxx and xxxxxxxx logistics xxxxx refine xxxxxxxxx strategies xxx improve xxxxxxxxx of xxx customers xxxxxxxxxxxxxx S xxxxxxx T xxxxxx O xxxxxxxxx P xxxxxxxx Tableau x data xxxxxxxxxxxxx tool xxx Journal xx economic xxxxxxxxx - x Hoelscher x Mortimer x Using xxxxxxx to xxxxxxxxx data xxx drive xxxxxxxxx making xxxxxxx of xxxxxxxxxx Education x Stirrup x Nandeshwar x Ohmann x Floyd x Tableau xxxxxxxx Interactive xxxx Visualizations xxxxx Publishing xxx Read More

Ans. PROJECTPart xxx Transforming xxxx and xxxxxxxxxxx TrendsDepartment xx Business xxxxxxxx New xxxxxxxxx University xxx Descriptive xxxxxxxx AnalyticsThe xxx and xxx datasets xxxx common xxxxxxxxx such x... ....... PROJECTPart xxx Transforming xxxx and xxxxxxxxxxx TrendsDepartment xx Business xxxxxxxx New xxxxxxxxx University xxx Descriptive xxxxxxxx AnalyticsThe xxx and xxx datasets xxxx common xxxxxxxxx such xx item xxxxxxxx coupon xxx no xxxxx purchase xxxxxx and xxxxxx of xxx customer xxxxx However xxxx data xxxxxxxxxxx have xxxxxxx issues xxx ABC xxxxxxxxx order xxxxxx variable xxx different xxxxx Some xxxxxxx are xx Arial xxxxx others xxx in xxxxxxx Moreover xxx item xxxxxx and xxxx code xxxxxx are xxx same xxxxxxxxxxxxxxxxx The xxxxxxxx with xxxxxxxx column xxxx has xxxxx formatting xx some xxxxxx are xxxxxxx to xxx right xxx others xxx centered xxxxxxxx the xxx dataset xxx font xxxxxxxxxxxxxxx Arial xx Calibri xxx the xxxxxxx cost xxxxxxxx has xxxx blanks xxxx making xxx data xxxxxxxxxx Similar xx ABC xxx item xxxxxx and xxxx code xxxxxx are xxxxxxxxxx and xxx quantity xxxxxxxx has xxxxx formatting xxx and xxxx explain xxxx font xxxxxxxxxxx do xxx affect xxx technical xxxxxx of xxx data xxxxxxxxxxxxx but xxxx can xxxxxxxxx data xxxxxxx user xxxxxxxxxx and xxxxxx consistency xx the xxxxx hand xxxxxxx values xxxx as xxx blank xxxxxxx cost xxxxxxx in xxx are x direct xxxx integrity xxxxx that xxxxx to xxxxxxxx in xxxxxxxx and xxxxxxxx making x used xxxx profiling xx find xxxxx mistakes xxx omissions xxxx profiling xx an xxxxxxxx that xxxxxxxxxxxxxxx evaluates xxx summarizes xxxx to xxxxxxxxx its xxxxxxx structure xxx content xx Koukaras xxx Tjortjis xxxxx data xxxxxxxxx helps xxxxxx patterns xxxxxxx inconsistencies xxx assess xxxxxxxxxxxxx thus xxxxxxxx organizations xx determine xxx suitability xx the xxxx for xxxxxxxxx reporting xx business xxxxxxxxx This xxxx is x critical xxxxx phase xx any xxxx related xxxxxxxxxx such xx data xxxxxxxxxxx and xxxx cleansing xx it xxxxxx to xxx surface xxxxxx like xxxxxxx values xxxxxxxxxxxxxxx and xxxxxxxx formatting xxxxxxx the xxxxxxxx trends xx month xxx seasonThe xxxxx step x took xx preparing xxx ABC xxxxxxx for xxxxxxxx was xx clean xxx Order xxxxxx column xx Power xx Desktop xxxxx the xxxxx Query xxxxxx I xxxxxxx that xxx order xxxxxxx contained xx extra xxxxxxxxx C xxxx prevented xxxxxxxxx analysis xx I xxxxx changed xxx column x data xxxx to xxxx I xxxx removed xxx extra x using xxxxxxxxx Replace xxxxxx leaving xxxx numeric xxxxxxxxxx Once xxx cleaning xxx complete x reconverted xxx column xx a xxxxx Number xxxx type xx standardize xxx order xxxxxxx and xxxx them xxxxx for xxxxxxx filtering xxx creating xxxxxxxxxxxxx Next x created x Month xxxxxxxx from xxx Order xxxx column x used xxx Column xxxxxx from xxxxxxxx and xxxxx sample xxxxx names xxxx January xxx February xxxxxxxx Power xx to xxxxxxxxxxxxx generate xxx month xxxxxx This xxxx was xxxxxxxxx because xxxxxx a xxxxx variable xxxxxxx me xx analyze xxxxx trends xxxx time xxxxxxx I xxxxx a xxxxxx column xxxxx a xxxxxxxxxxx Column x named xxx new xxxxxx Season xxx defined xxxxxxxxxx so xxxx December xxxxxxx and xxxxxxxx were xxxxxxxx Winter xxxxx April xxx May xx Spring xxxx July xxx August xx Summer xxx all xxxxx months xx Fall xxxx allowed xx to xxxxxx each xxxxxxxxxxx a xxxxxx based xx its xxxxx month xxxxx will xxxx in xxxxxxxxx seasonal xxxxx patterns xx month xxx seasonPeak xxxxx are xx the xxxxx of xxxxx and xxxxxxxx has xxx lowest xx can xxxx observe xxxx highest xxxxx are xx spring xxx lowest xxxxx are xx Fall xx genderFrom xxx bar xxxxx we xxx say xxxx Men xxxxxx more xxxx women xx regionFrom xxx filled xxx we xxx observe xxxx florida xxx the xxxxxxx sales xxx Maine xxx the xxxxxx sales xx customers xxxx and xxxxxxx childrenFrom xxx Chart xx can xxxxxxx that xxxxx is xxxxxx equal xxxxx cost xxx customers xxxx and xxxxxxx children xxxxxxxx of xxxxx and xxxxxxxxxxx productsSince xxx dataset xxxxxxxx all xxxxxx each xxxxx has xxx own xxxx and xxxxxxxxxxxxxx products xxx same xx true xxxx looking xx seasonal xx monthly xxxxx The xxxx also xxxxxxxxxxx large xxxx we xxxxx it xxxx by xxxxxx or xx whether xxxxxxxxx have xxxxxxxx or xxx Therefore x have xxxxxxx out xxx five xxxxxxx items xxx bottom xxxx selling xxxxx as x whole xxx five xxxxxxx items xxx Reuben xxxxxxxx Cincinatti xxxxxx Penne xxxx Vodka xxxxxxx Water xxx American xxxx Suey xxxxxx five xxxxxxx items xxx Tasty xxxxxxxxx Chocolate xxxx Sphagetti xxx Whole-Wheat xxxxx and xxxxx Rings xxxxxxx the xxxxxxxxxxxxx of xxxxxxxx The xxxxxx generating xxx highest xxxxxxx are xxxxxxxxxx Florida xxxxx New xxxx and xxxx while xxx lowest xxxxxxx comes xxxx Maine xxxxxxxx Vermont xxxxx Dakota xxx Puerto xxxx Revenue xx season xx highest xx Spring xxxxxxxx by xxxxxx Summer xxx Fall xxx months xxxx the xxxxxxx revenue xxx April xxxxx February xxxxxxx and xxxxxx whereas xxxxxxx December xxxxxxxx June xxx September xxxx the xxxxxx revenue xxxx customers xxxxxxxxxxx slightly xxxx revenue xxxx female xxxxxxxxx Orders xxxx customers xxxx children xxxxxxxx in xxxxxxxxxx higher xxxxxxx compared xx those xxxxxxx children x Qualitative xxxxxxxxx - xxxxxxxxxx state xxxx category xxxxxx customer xxxxx Season xxxxx purchase xxxxxxxxxxxxxxxxxx variables x item xxxxx cost xxxxxxx quantity xxxx price xxxxx total xxxx B x created x clustered xxx chart xx display xxxxxxx across xxx merged xxxxxxx s xxxxxxxxxxxx footprint xxxxxxx is xxxxxxxxxxxx in x few xxxxxx rather xxxx evenly xxxxxxxxxxx Florida xxxxxx out xx an xxxxxxx contributing x disproportionately xxxxx portion xx the xxxxx revenue xxxxxxxx of xxxxxxx checks xx item xxxxxxxx and xxxx of xxx shows xxxx PM xxxxxx consistently xxxx higher xxxxxx than xx orders xxxxxxxx pizza xxx sandwiches xxxxxxxxxx the xxxx to xxxxxxx while xxxxxxxxx generate xxx lowest xxxxxx This xxxxxxxxx that xxxxxxxxxxxxxxxx tend xx place xxxxxx or xxxx expensive xxxxxx which xxx inform xxxx planning xxx promotion xxxxxxxxxx Next x used x stacked xxx chart xx determine xxxxxxxx demographics xx gender xxx whether xxxx have xxxxxxxx The xxxxx shows xxxx the xxxxxxxxxxxx is xxxxxx equal xxxxxx all xxxxxxx indicating xxxx the xxxxx has xxxxx appeal xxx likelihood xx a xxxxxxxx having xxxxxxxx remains xxxxxx consistent xxxxxxxxxx of xxxxxx Additionally xxx data xxxxx seasonal xxxxxx with x significant xxxxx in xxxxx and x noticeable xxx in xxxx again xxxxxxxx till xxxxxx and xxxx fell xxxx in xxxxxxx C xxxxxxxx and xxxxxxxxxxxxx by xxxxx Florida xxxxxxxxx the xxxxxxx revenue xxxxx Maine xxxxxxxxxxx the xxxxxx Average xxxx Cost xxxxxxxxx have xxx highest xxxxxxx item xxxx whereas xxxxx has xxx lowest xxxx of xxx Sales xxx higher xx the xxxxxxxx compared xx evenings xxxxxxxxxx strong xxxxxxxxx or xxxxx demand xxxxxxx Trends xxxxxx shows xxx highest xxxxxxx sales xxxxx December xxxxxxx the xxxxxx D xxxxxxx Statistics xxx Total xxxxx Value xxxx Standard xxxxx Median xxxx Standard xxxxxxxxx Sample xxxxxxxx Kurtosis xxxxxxxx Range xxxxxxx Maximum xxx Count xxxxx order xxxxx data xx right xxxxxx Skewness xxxx a xxx very xxxxx orders xx can xx seen xx the xxxx and xxxxxxx values xxxx of xxx orders xxx smaller xxxxxxxxx around xxx median xx The xxxxxxx range xx values xx large xxxxxxx that xxxxx is xxxxxxxxxxx in xxxxxxxx spending xxxxxxxxxxxxx K xxxx D xxxxxxxxxx career xxxxxxxx and xxxxxxxxxxxxxxx development xxx university xxxxxxxx using xxxxxxxxxx intelligence xxx machine xxxxxxxx Journal xx Computational xxxxxxx in xxxxxxxx Engineering xxxxx doi xxx Koukaras x Tjortjis x Data xxxxxxxxxxxxx and xxxxxxx Engineering xxx Data xxxxxx Techniques xxxxx and xxxx Practices xx https xxx org xx PROJECTPart xxx Stakeholder xxxxxxxxxxxx and xxxxxxxxxxx Analysis xxxxx ApplicationDepartment xx Business xxxxxxxx New xxxxxxxxx University xxx Descriptive xxxxxxxx AnalyticsAfter xxxxxxxx and xxxxxxxxxxxx the xxxx in xxxx One x progressed xx more xxxxxxxx analytics xx Milestone xxx by xxxxxxxxxxx and xxxxxxxxxx key xxxxxxxxxxx requirements xxx purpose xx this xxxxx is xx generate xxxxxxxxxxx insights xxxx will xxxx the xxxxx ABC xxx leadership xxxx understand xxx current xxxxx of xxx newly xxxxxx organization xxx use xxxxx insights xx develop x data-driven xxxxxxxxxx profitability xxxx Identifying xxxxxxxxxxxx requirementsUsing xxx Stakeholder xxxxxxxxxxxx Document x identified xxx primary xxxxxxxxxxxxxxxxx and xxxxxxxxxx their xxxxx into xxxxxxxx analytical xxxxxxx that xxxxxxx decision-making xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx UseRegional xxxxxxxxxxxxxxxxxxxxx Sales xxxxxxxxxxxxxxxxxxx high- xxx low-performing xxxxxxx to xxxxx staffing xxxxxxxxx and xxxxxxxxxxxxxxxxxx MarketingDirectionMarketingSeasonal xxxxxxxxxxxxxxxxxxxx promotions xxx campaigns xxxxxxx monthly xxx quarterly xxxxxx trends xxxxxxxxxxxx and xxxxxxxxxxxxxxxxxxxxx SegmentationTo xxxxxxxxxx the xxxxxxxx behaviour xxxxxxx the xxxxxxx as xxxx between xxxxxxx without xxxxxxxx familiesProduct xxxxxxxxxx t xxxxxxxxxxxxxxxxxxxxxxxxxxxx EvaluationAn xxxxxxxxxx of xxxxxxxxxxx betweencustomers xxxx and xxxxxxx children xx guide xxx creation xx family-oriented xxxx deals xx well xx veg xx non-veg xxxxx mealsFinanceDirectorFinanceProfitabilityanalysisPrioritize xxxxxxxxxxx items xxxxxxxxxx andregions xxx investment xxx analysis xxxx be xxxxxxxx on xxxxxxxxxxx requirements xx clearly xxxxxxxxxxxxxxxxxxx are xxxxxxxxx to xxx success xx any xxxxxxxx If xxx requirements xxx not xxxx understood xxxx the xxxxxxxxx the xxxxxxxx is xxxxxxxx to xxxx the xxxxxxxx objectives xxxx the xxxxxxxxxxxx are xxxxxxxxxx the xxxx can xx sliced xxx structured xxxxxxxxxxx For xxxxxxx toanalyze xxxxxxx sales xxxxxx I xxxxx first xxxx to xxxxxx a xxxxxx that xxxxxxxxxx the xxxxx of xxxx sale xxxxxxxxx if xxx goal xx to xxxxxxx sales xx time xx day x column xxxxxxxxxxxxxx AM xxx PM xxxxx be xxxxxxxxx Descriptive xxxxxxxx methodsK-means xxxxxxx analysis xx Regression xxxxxxxx vs xxxxxxxx Tree xx Random xxxxxx The xxxxxxxx will xx done xx three xxxxx Primary xxxx Data xxxxx and xxxxxxxxxxx BenefitCriteriak-meansRegressionAnalysisDecision xxxxxxxxxxxxxxxxxxxxxxxx GoalsDiscovery xxxxxxxxxx theClassifies xxxxxxxxxxxxx by xxxxxxxxxxxxxxxxxxxxxxxx journeypredictivethem xxxxxxxxxxx ausing x flow-accuracy xxxxxxxxxx based xxxxxxxxxxxxxxxx showingcombiningsimilarity xxxxxxxxxx and xxxxx path xxxxxxxxxxxxxxxxxx likeindependentcustomer xxxxxxxxxxxxx treesregion xxx Education xxxxxxxxxx reach xx outcome xxxxxx n x into xxxxxxxxxx modelData xxxxxxxxxx on xxxxxxxxxxxx the xxxx isSince xxxxxxxxxxxxxx sets xxxxx predict xxxxxxxxx is xxxxxxxxxxxxxxxxxx needs x basebe xxxxxxx thetraining xxxx together xxxxxx or xxxxx has xx beuses xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx Forsubsets xx datawithout xxxxxxxxxxxxxx toexample xxxxx variablesa xxxxxxxxxxxx the xxxxxxxxxxx outto xxxxxxxxx next xxxxx which xxxxxxxxxxxxxxxxxxxxx previousspends xxxx month xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx howVisualizes xxxxxxxxxxx highBenefit xxxxxxxxxxxx sales xxxxxxxxx ofpredictionPersonas xxxxxx if xxxxxxxxxxxxxxxxx butexample xxxxx ofspecific xxxxx isdecisionslimitedfamilies xxxxxxxxxxxxxxxxxx oninterpretabilitychildren xxxxxxxxxxx splits xxxxxx it xxxxxxxxxxx like xxxxxx orsuitable xxxxxxxxxxxxxxxxxxxx descriptivebusinessanalysisModel xxxxxxxxx for xx analysis xxxxxx cluster xxxxxxxx is xxxx suited xxxxx for xx analysisSince xxx joint xxxxxxxxxx team xxxxx to xxxxxxxxxx the xxxxxxx state xx merged xxxx from xxx newly xxxxxx business xxxx need xx independent xxxxxxxx on xxxxxxx factors xxx leadership xx currently xxx focused xx prediction xxx next xxxxx sales xx figuring xxx the xxxxxxxxxxx of xx outcome xxxx they xxxxxxxxxxxxx in xx to xxxxxxx the xxxx to xxxxxxxxxx the xxxxxxx position xxxxx states xxxxx the xxxx or xxxxx the xxxxx which xxxxxxxx works xxxxx gender xxxxx month xxx Sharda xx al xxxxxxxxxx is xxxxxxxxx used xxxx the xxxxxxxxxx team xxxxx to xxxxxxx the xxxxx or xx predict xxx next xxxxxx sale xxxxxxxx tree xxxx help xxxx understand xxx probability xx an xxxxxxx If xxxx had xx choose xxxxx project xx invest xx out xx a xxxxxxxxx of xxxxxxx they xxxxx use xxxxxxxx treen xxx probability xxxxxx However xxxxx they xxx only xxxxxxxxxx in xxxxxxxxxxxxx the xxxxxxxx entity xxx to xxxx out xxxxxx like xxxxx category xx build xxxx which xxxxx to xxxxx on xxxxx item xx market xxxx etc xxxx k-mean xxxxxxx will xxxx them xxxxxxxxxx this xxxxxx cluster xxxxxxxxxxxxx different xxxxxxx models xxx differences xxxxxx below xxx three xxxxxx used xxx the xxxxxxxxx behind xxxx Cluster xxxxxx Conducted xxxxx For xxx first xxxxx I xxxx item xxxxx cost xxx quantity xxxxxxx count xxx automatic x was xxxxxxxx on xxxxxx vs xxxxxxxx to xxxxxxxx high- xxxxx vs xxxxxxxxxxxx customers xxxxx - xxx this xxxxx I xxxx item xxxxx cost xxxxxxxx and xxxx price xxxxxxx count xxx set xx I xxx focusing xx Price xxxxxxxxxxx to xxx the xxxxxx of xxxxxxxxx items xxxxxxxxx by xxx customers xxxxx Third xxxxx was xxxxx on xxxx total xxxx customer xxxx children xxx gender xxxxxxxx payee x was xxxxxxxx on xx Demographic xxxxxxx to xxxxxx specific xxx and xxxxxxx Development xxxxxxxxxxx needs xxxxxxx count xxx set xx Best xxxxx and xxx According xx me xxx Model xxxxx is xxxxx on xxxxxxxxxxx data xxxxx fits xxx scenario xxxxxxx this xxxxx incorporates xxx stakeholder xxxxxxxxxxxx regarding xxxxxx and xxxxxx status xxxxxx the xxxxxx directly xxxxxxxxxx for xxx three- xxxx plan xxxxx cluster xxxxxxx the xxxx discrete xxxxxxxxx Discrete xx this xxxxxxxxxxxxxx to xxxxxxxx clusters xxxx have xxxxxx or xx overlaps xxx the xxxxxxxxxx are xxxxx Going xx this xxx first xxxxx is xxx most xxxxxxxx as xxx number xxxxxxxxxxx are xxxxxx Variables xxxx customer xxxx children xxx gender xxxxxxxx payee xxxxx was xxxx in xxxxx will xxxx overlaps xxxxx is xxxx evident xx the xxxxxxx images xxx These xxxxxxxxx Inform xxx Analysis x have xxxx the xxx data xxx transformed xx into xxxxxxxx Persona xx that xxx joint xxxxxxxxxx team xxx do xxxxxxxx marketing x The xxx Manager xxx use xxx High-Spending xxxx cluster xx market xxxxxxx individual xxxxx while xxx Value- xxxxxxx Families xxxxxxx receives xxxxxxx for xxxxxxx Menu xxxxxxxxxxxx - xxx Product xxxxxxxxxxx Manager xxx identify xxxxx products xxx favorites xxx expand xxxxx offerings xxxxxxxxx planning xxx identify xxxxxxx and xxxxxx staffing xxx inventory xxxxxxx statistics xxx each xxxxxxxxxxxxxx statistics xxxx each xxxxx from xxxxxxx is xxxxxxxxx belowModel xxxxxxx belowInputs xxx ClusteringVariables xxx of xxxx Total xxxxxxx of xxxxxxxx With xxxxxxxx Gender xxxxxxxx PayeeLevel xx Detail xxxxxx Customer xxxxxxxxxxxx NormalizedSummary xxxxxxxxxxxxxxxxx of xxxxxxxx Number xx Points xxxxxxxxxxxxx Sum xx Squares xxxxxxxxxxxx Sum xx Squares xxxxx Sum xx Squares xxxxxxxxxxx Common xxxxxxxxxxxxxx ofSum xx ItemSum xx CustomerGender xxxxxxxxxx CostWith xxxxxxxxxxxxxxxxxxxxxxxxxxxx MCluster xxxxxxxx MCluster xxxxxxxx FNotClustered xxxxx Summary xxxxxxxxxxx for xxxxxxxxxxxxxxxxxxx Sum xx Item xxxxx CostSum xx Customer xxxx Children xxxxxx Customer xxxxxxxxxx of xxxxxx Not xxxxxxxxxxxxxxxxx NormalizedSummary xxxxxxxxxxxxxxxxx of xxxxxxxx Number xx Points xxxxxxxxxxxxx Sum xx Squares xxxxxxxxxxxx Sum xx Squares xxxxx Sum xx Squares xxxxxxxxxxx CommonClustersNumber xx ItemsSum xx Item xxxxx CostSum xx Customer xxxx ChildrenGender xxxxxxxxxxxxxxxxxxxx MCluster xxxxxxxx FCluster xxxxxxxxxxxxx Model xxxxxxx belowInputs xxx ClusteringVariables xxx of xxxx Total xxxxxxx of xxxxxxxx With xxxxxxxx Gender xxxxxxxx PayeeLevel xx Detail xxx AggregatedScaling xxxxxxxxxxxxxxxxx DiagnosticsNumber xx Clusters xxxxxx of xxxxxx Between-group xxx of xxxxxxx Within-group xxx of xxxxxxx Total xxx of xxxxxxx CentersMost xxxxxxxxxxxxxxxxxxxx of xxxxxxxx of xxxx Total xxxxxxx of xxxxxxxx With xxxxxxxxxxxxxx CustomerPayeeCluster xxxxxxxx MCluster xxxxxxxx FNotClustered xxxxxxxxxx the xxxxxxxxx Customer xxxxxxxx Based xx the xxxxxxxxx request xxx subgroups xxx as xxxxxx Subgroup x - xxxx item xxxxx cost xxx high xxxxxxxx This xxxxx consists xxxxxxxxx of xxxxxxxxx with xxxxxxxx often xxxxxxxx during xx hours xxxxxxxx B x Moderate xxxx total xxxx but xxx quantity xxxxx are xxxxxxxxx single xxxxxx often xxxx visiting xxxxxx AM xxxxx hours xxxxxxxx C x Low xxxx total xxxx and xxxx frequency xxxxx customers xxx low- xxxxxx items xxx are xxxxxx sensitive xx price xxxxxxx Compare xxx Contrast xxxxxxxxx comparing xxx subgroups xxxx us xxxx interesting xxxxxxxx Revenue xx volume x Subgroup x drives xxx highest xxxxxx size xxxxxxx Check xxx may xxxxx less xxxxxxxxxx than xxxxxxxx C xxxxxxxxxxx divide xxxxxxxx are xxxxxxx customers xxxxx subgroup x are xxxxxxx customers xxxxxxxxxxx Clusters xxxxxxx in xxxxxxxxxx happen xxxx data xxxxxx share xxxxxxx characteristics xx multiple xxxxxxxxx Some xxxx customers xxx have xxxx a xxxxxxxxxxx purchase xxx might xxxx been xxxxx to xxx family xxxxx Families xxxxxxxx above xxx average xxx not xx necessarily xxxxxx children xxxxxxxxxx Stakeholder xxxxx My xxxxxxxx has xxxxxxxxxxx raw-data xxxx actionable xxxxxx helping xxx stakeholders xx developing x three-year xxxxxxxx plan xx defining xxxxxxxxx and xxxxxxxxxxx personas xxx marketing xxxx can xxxxxx better xxxxxxxxx By xxxxxxxxxxxx menu xxxxx that xxxxx volume xxx margins xxxxxxxxx team xxx focus xx product xxxxxxxxxxxx and xxxxxxxxxxxxxxx volume xx different xxxx of xxx can xxxx resource xxxxxxxx ReferencesIBM xxxxxxxxx What xx clustering xxx https xxx ibm xxx topics xxxxxxxxxxxxxxxx n x What xx a xxxxxxxx tree xxxxxx Data xxxxxxx https xxx oracle xxx artificial-intelligence xxxxxxxxxxxxxxxx what-is-a-decision-tree xxxxxx R xxxxx D xxxxxx E xxxxxxxx intelligence xxxxxxxxx and xxxx science x managerial xxxxxxxxxxx th xx Pearson Read More

Ans. After xxxxxxxx the xxxxx discussing xxxxxx cognitive xxxxxx I xxxxxxxxxx that x am xxxx likely xx be xxxxx to xx my xxxxxxxx are xxxxxxxxx bias xxx confirmation xxxx Practical xxxxxxxxxx Anchoring xxx... ....... After xxxxxxxx the xxxxx discussing xxxxxx cognitive xxxxxx I xxxxxxxxxx that x am xxxx likely xx be xxxxx to xx my xxxxxxxx are xxxxxxxxx bias xxx confirmation xxxx Practical xxxxxxxxxx Anchoring xxxx occurs xxxx I xx dependent xx the xxxxx piece xx information xxxxx I xxxxxxxxx such xx forecast xx prior xxxx s xxxxxxxxxxxx This xxx limit xx ability xx adjust xxxxxxxxxxx even xxxx new xx more xxxxxxxx data xxxxxxx available xx a xxxxxxxx context xxxx may xxxx to xxxxxxxxxxxx or xxxxxxxx recommendations xxxxxxxxxx in xxxxxxx environments xxxxx conditions xxxxxx rapidly xxxxxxxxx Psychology xxxxxxxxxxxx bias xx another xxx of xx biases xxxx has xx impact xx my xxxxxxxx It xxx influence xx work xx making xx concentrate xxxx on xxx data xxxx supports xx initial xxxxxxxxxx and xx the xxxx time x may xxxxxxxxxxxxxxx neglect xxx evidence xxxx contradicts xxx instance xx I xxxxxxx that x certain xxxxxxxxx strategy xxxx work x may xxxxx more xx the xxxxxxx that xxxxxxxx positive xxxxxxx and xxxxxx the xxxxxxxx ones xxxxxxxxx Psychology xxxx in xxxx can xxxx to xxxxxx up xxxxxxx fully xxxxxxxxx the xxxxxxxxxxx of xxx data xxxx I xxxxx the xxxxxxx with xx manager xxxx bias xxx cause xx to xxxxxxx a xxx sided xxxx that xxxxx sufficient xxxxxxxxxx of xxxxx and xxxxx interpretations x believe xxxx just xxxxxxx numbers xx graphs xx enough xxx the xxxxxxx to xxxxxxxxx the xxxx as x do xxxxxxx managers xxxx with xxxxx own xxxxxxxxxxx assumptions xxx biases xx there xx no xxxxxx context xxxxxxxxxxx and xxxxxxx accurate xxxxxxx can xx misunderstood xx misused xx well xx order xx compensate xxxxx biases x firstly xxxxxx the xxxxxxxx can xxxxxxxxx my xxx assumptions xxx actively xxxx for xxxxxxxxxxxxx evidence xxxxxxxx instead xx a xxxxxx conclusion x can xxxxxxxxxx various xxxxxxxxx or xxxxxxx alternative xxxxxxxxxxxx When xxxxxxxxxxx and xxxxxxx I xxxx choose xxxxx appropriate xxxxxxx to xxxxx misleading xxxxxx and xxxxxxxxxx explain xxxx the xxxxxx shows xxx why xx matters xxxxxxxxxxxxxxxxxxx Psychology xxxxxxxxx Biases xxxxxxxxx - xxx to xxxxx Better xxx More xxxxxxxxx Removing xxxx YouTube xxxxx In xxxxxxx https xxx youtube xxx watch x wEwGBIr xxx Read More