BUS5PA: Predictive Analytics - Building and Evaluating Predictive Models - Business Assignment Help

Assignment Task:

Objective

a) Demonstrate knowledge of building different types of predictive models using SAS Enterprise Miner

b) Demonstrate skill and knowledge in applying predictive models in a real-life predictive analytics task

c) Relate theoretical knowledge of predictive models and best practices to application scenarios

1. Setting up the project and exploratory analysis

a. Create a new project and use the Customer_Purchase data set as a data source. Use the data source in a diagram.

b. As noted above, only TargetBuy is used for this analysis, and it should have a role of Target. Can TargetAmt be used as an input for a model that is used to predict TargetBuy? Why or why not? Explain with justifications.

c. Carry out a data exploration by using a StatExplore Node. Explain your findings.

d. Create a Data Partition with 70% of the data for training and 30% for validation.

2. Decision tree based modeling and analysis 

a. Create two Decision Tree models. Use two-way and three-way splits to create the two separate decision tree models. For each decision tree, I. How many leaves are in the optimal tree? II. Which variable was used for the first split? III. What were the competing splits for this first split?

b. Which of the decision tree models appears to be better? Justify your answer.

c. Refer to the selected decision tree model and

I. Identify leaf nodes which have good predictive performance (two leaf nodes) and poor predictive performance (two leaf nodes).

II. Provide justifications for your selections

III. Write down the rules for the pathways leading up to each selected leaf node.

3. Regression based modeling and analysis

In preparation for regression, is any missing values imputation needed? If yes, should you do this imputation before generating the decision tree models? Why or why not?

a. Use an Impute node connected to Data Partition node. Set the node to impute U for unknown class variable values and the overall mean for unknown interval variable values. Create imputation indicators for all imputed inputs.

b. Conduct data exploration to select the best variables for the model. Explain your findings. 

c. Create a Regression model using the set of variables you identified as suitable in part c. You can choose stepwise selection and use validation error as the selection criterion.

d. Run the Regression node and view the results.

I. Which variables are included in the final model? Explain what this means to the supermarket management (very briefly).

II. What is the validation ASE? What does this mean?

4. Model Comparison and Scoring  

a. Compare and contrast the results from the decision tree and regression based analysis. Describe and justify how you ascertained the better model.

b. Would it have been sufficient to use only one modeling techniques (decision tree or regression)? Provide justifications for your answer.

c. What are the advantages of using a decision tree model? What advantages would a regression model provide? Students are expected to use the lecture discussions on features of decision trees and regression and apply this understanding to the data analysis. Give examples from the models you have built.

d. Use Customer_Purchase_Score data set to score the best model. Explain the output using plots.

5. Extending current knowledge with additional reading – SEMMA

This section is based on your tutorial 6 case study (customer retention and churn). Read the case study again and study the SAS diagram you created – and understand the flow of the process diagram.

Relate the predictive analytics life cycle from your lectures, SAS diagram created on tutorial 6 and the SEMMA analytics methodology proposed by SAS. You can use diagrams with brief explanations.

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