Question.5663 - In Milestone One, you cleaned and transformed the data sets. You will now, use your cleaned, transformed and merged data set in this the necessary updates to get an error-free data set before you start using this in this milestone activity. Using the cleaned and transformed dataset from Milestone One, you will run the data through descriptive analysis models in Tableau are problems. Specifically, you must address the following rubric criteria: 1. Using the Stakeholder Requirements Document, identify stakeholder requirements. A. How will these requirements shape your analysis? Explain. 2. Compare various descriptive analysis methods. A. Compare and contrast a k-means cluster analysis method to at least two of the following models: i. Regression ii. Decision tree iii. Random forest B. Which model is best suited for your analysis? i. How does the output of the model support your analysis? Explain. ii. Why are the other models not suited for your analysis? Explain. 3. Conduct a k-means cluster analysis. A. Conduct at least three different cluster models and explain the differences. B. Explain which model best fits the data. Justify your reasoning. i. Which cluster created the most discrete groupings? Explain. ii. How do these groupings inform your analysis? 4. Based on your analysis, produce summary statistics for each cluster. A. Explain the subgroups that are present. B. Compare and contrast the various subgroups. C. Are there any areas where the clusters overlap one another? D. How well does your analysis support or meet stakeholder needs?
Answer Below:
Milestone TwoQSO 560 - Descriptive Business Analysis Milestone TwoAfter cleaning and transforming the data in Milestone One, I now move to more advanc...
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 PearsonPaying someone to do your statistics assignment has become a practical solution for students managing tight deadlines, academic pressure, and personal responsibilities. 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