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BUS5PA : Assignment on Predictive Analytics
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Predictive Analytics (BUS5PA)
Added on 2020-03-01
BUS5PA : Assignment on Predictive Analytics
Predictive Analytics (BUS5PA)
Added on 2020-03-01
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Building and Evaluating Predictive ModelsAssignment 1 (BUS5PA Predictive Analytics – Semester 2, 2017)By<Student Name>(19152818) La Trobe Business School Australia
List of FiguresFigure 1 Project: BUS5PA_Assignment1_19152818....................................................................AFigure 2 New Library.....................................................................................................................AFigure 3 New data source: Organics...............................................................................................AFigure 4 Column Metadata.............................................................................................................BFigure 5 Organics purchase indicator (%age).................................................................................BFigure 6 Organics diagram workspace: Organics data source........................................................CFigure 7 Data partition....................................................................................................................CFigure 8 Data set Allocations..........................................................................................................CFigure 9 Addition of Decision Tree................................................................................................CFigure 10 Autonomously created decision tree model...................................................................DFigure 11 Assessment measure.......................................................................................................DFigure 12 Subtree Assessment Plot................................................................................................DFigure 13 Decision Tree (Tree 1)....................................................................................................EFigure 14 Decision Tree after adding Tree 2..................................................................................EFigure 15 Three-way Split...............................................................................................................FFigure 16 Assessment Measure : Decision Tree 2..........................................................................F2
Figure 17 Average square error : Tree 2.........................................................................................FFigure 18 StatExplore tool..............................................................................................................GFigure 19 Default input method updation.......................................................................................GFigure 20 Indicator variables..........................................................................................................GFigure 21 Addition of Regression model........................................................................................HFigure 22 Regression Model Selection...........................................................................................HFigure 23 Result of Regression model.............................................................................................IFigure 24 Summary of Stepwise Selection......................................................................................IFigure 25 Odd ratio Estimates..........................................................................................................JFigure 26 Average squared error (ASE)..........................................................................................JFigure 27 Model Comparison.........................................................................................................KFigure 28 Model Comparison Result..............................................................................................KFigure 29 ROC Chart......................................................................................................................LFigure 30 Cumulative Lift...............................................................................................................LFigure 31 Fit Statistics...................................................................................................................MList of TablesTable 1 Model performance comparison83
1.Setting up the project and exploratory analysisa]New project named BUS5PA_Assignment1_19152818 has been created, which has beenshown in Figure 1.a.1]New SAS library named has been created named As52818, and data source has beencreated using SAS dataset ORGANICS, which has been mentioned above in Figure 2 andFigure 3.a.2]As mentioned in the business case assignment, all the roles have been set, Figure 4 showsthe roles which have been defined for the data source ORGANICS.a.3]“TargetBuy” has been defined as target variable. 24.77% individuals have purchasedorganic products and rest i.e. 75.23% have not purchased organic products, which has beendepicted in Figure 5. a.4]As mentioned in Figure 4, Demcluster has been set rejected.a.5]Data source named organics has been defined, which has been shown in Figure 3a.6]ORGANICS data source has been added to Organics diagram workspace, which has beenshown in Figure 6.b]TargetAmt cannot be used as an input for a model that is used to predict TargetBuy,TargetBuy indicates if the individuals have purchased the organic item or not, whereasTargetAmt indicates the number of organic amounts bought. TargetAmt will only be4
recorded for those who have purchased any organic products i.e. when Targetbuy is Yes.Hence, TargetAmt can never be the predictor of TargetBuy. In this business case as an initialbuyer incentive plan, the supermarket’s objective is to develop loyalty model by whethercustomers have purchased any of the organic products. So, TargetBuy is the perfectlysuitable as target variable2.Decision tree based modeling and analysisa]Data partition node has been added to the diagram from Sample Tab, and it has beenconnected to the data source node i.e. ORGANICS. 50% of the data have been assigned intraining and rest 50% have been added in validation, which has been depicted in Figure 7 andFigure 8.b]Decision Tree node has been added to the workspace and it has been connected to the Datapartition node, which has been depicted in Figure 9.c]Decision Tree has been built autonomously, not interactively, and sub tree model assessmentcriteria has been chosen as Use average square error which has been shown in Figure 10 and11.c.1]As per average square error, there are 29 leaves in the optimal tree, subtree assessment plothas been shown in Figure 12.c.2]Age has been used for the first split, it has partitioned the training data in two parts, firstsubset was for the age less than 44.5, for this subset TargetBuy = 1 has higher than average5
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