BUS5PA: Building and Evaluating Predictive Models | Assignment

Building and Evaluating Predictive Models
Assignment 1
(BUS5PA Predictive Analytics – Semester 2, 2017)
By
<Student Name>
(19140689)
La Trobe Business School
Australia

Table of Contents
1. Setting up the project and exploratory analysis 4
2. Decision tree based modeling and analysis 5
3. Regression based modeling and analysis 6
4. Open ended discussion 8
5. Extending current knowledge with additional reading 10
References 12
Annexure I-XII
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List of Figures
Fig. 1 Project: BUS5PA_Assignment1_ 19140689.........................................................................I
Fig. 2 New Library...........................................................................................................................I
Fig. 3 New data source: Organics....................................................................................................I
Fig. 4 Column Metadata..................................................................................................................II
Fig. 5 Organics purchase indicator (%age).....................................................................................II
Fig. 6 Organics diagram workspace: Organics data source...........................................................III
Fig. 7 Data partition.......................................................................................................................III
Fig. 8 Data set Allocations.............................................................................................................III
Fig. 9 Addition of Decision Tree...................................................................................................IV
Fig. 10 Autonomously created decision tree model......................................................................IV
Fig. 11 Assessment measure..........................................................................................................IV
Fig. 12 Subtree Assessment Plot...................................................................................................IV
Fig. 13 Decision Tree (Tree 1)........................................................................................................V
Fig. 14 Decision Tree after adding Tree 2......................................................................................V
Fig. 15 Three-way Split.................................................................................................................VI
Fig. 16 Assessment Measure : Decision Tree 2............................................................................VI
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Fig. 17 Average square error : Tree 2............................................................................................VI
Fig. 18 StatExplore tool...............................................................................................................VII
Fig. 19 Default input method updation........................................................................................VII
Fig. 20 Indicator variables............................................................................................................VII
Fig. 21 Addition of Regression model........................................................................................VIII
Fig. 22 Regression Model Selection...........................................................................................VIII
Fig. 23 Result of Regression model..............................................................................................IX
Fig. 24 Summary of Stepwise Selection........................................................................................IX
Fig. 25 Odd ratio Estimates............................................................................................................X
Fig. 26 Average squared error (ASE).............................................................................................X
Fig. 27 Model Comparison............................................................................................................XI
Fig. 28 Model Comparison Result.................................................................................................XI
Fig. 29 ROC Chart.......................................................................................................................XII
Fig. 30 Cumulative Lift................................................................................................................XII
Fig. 31 Fit Statistics....................................................................................................................XIII
List of Tables
Table 1 Model performance comparison 8
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1. Setting up the project and exploratory analysis
a] A new project BUS5PA_Assignment1_19140689 has been created (Fig. 1).
a.1] New SAS Library, has been created named As40689, and using SAS dataset ORGANICS
data source has been created (Fig. 2 and Fig. 3).
a.2] All the roles have been defined for the analysis variables for ORGANICS data source (Fig.
4).
a.3] “TargetBuy” has been defined as target variable. As mentioned in Fig. 5 (Percentage
distribution of “TargetBuy”), 24.77% individuals have purchased organic products and rest
i.e.75.23% have not purchased organic products.
a.4] Demcluster has been rejected (Fig. 4).
a.5] ORGANICS Data source has been created (Fig. 3).
a.6] ORGANICS data source has been added to Organics diagram workspace (Fig. 6).
b] Whether Individuals have purchased the organic item or not, is indicated by TargetBuy, and
TargetAmt indicates the number of organic amounts bought. TargetAmt will only be noted
when Targetbuy is Yes i.e. for those who have purchased any organic products. Hence, in
this model, to predict TargetBuy, TargetAmt cannot be used as the input. The objective of
supermarket’s is to develop a loyalty model by understanding whether customers have
purchased any of the organic products. So, TargetBuy is the suitable as target variable.
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2. Decision tree based modeling and analysis
a] A node name Data partition has been inserted to the diagram and it has been linked to the
data source node (ORGANICS). 50% of the data for training and rest 50% for validation,
have been assigned in data partition (Fig. 7 and Fig. 8.)
b] In Fig. 9, it has been shown that the Decision Tree node has been inserted to the workspace
and it has been linked to the Data partition node (Fig. 9).
c] Decision Tree model has been created autonomously, and sub tree model assessment criteria
has been chosen by using average square error (Fig. 10 and 11).
c.1] There are 29 leaves in the optimal tree (Fig. 12).
c.2] For the first split, variable age has been used. It has divided the training data in two
subsets, first one is for the age less than 44.5. In this case, TargetBuy = 1 has higher than
average concentration. Second one is for age greater than or equals to 44.5, In this case,
TargetBuy = 0 has higher than average concentration. Using average square error, Decision
Tree model has been created autonomously (Fig. 13).
d] First Decision Tree has been renamed as Tree 1 and Second Decision Tree node which has
been renamed as Tree, has been added to the diagram workspace, and it has been connected
with the Data Partition (Fig. 14).
d.1] In the splitting rule of the new Decision Tree, maximum number of branches have been set
to 3 to permit three-way splits (Fig. 15).
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