BUS5PA Predictive Analytics Part B

Part B: Market Basket Analysis and Association Rules (30%)

To plan innovative promotions to move items that are often purchased together, a store is interested in market basket analysis of items purchased from the Health and Beauty Aids Department and the Stationery Department. You are a member of the analytics team assigned to the task.

The store chose to conduct a market basket analysis of specific items purchased from these two departments. The TRANSACTIONS data set contains information about more than 400,000 transactions made over the past three months. The following products are represented in the data set:

There are four variables in the data set:

a) Create a new diagram. Name the diagram Retail.

b) Create a new data source using the data set RETAIL.

c) Assign the variables Outlet and Amount to the model role Rejected. These variables are not used in this analysis. Assign the ID model role to the variable Purchaseld and the Target model role to the variable Item. Change the data source role to Transaction.

d) Add the RETAIL data set and an Association node to the diagram.

e) Change the setting for the Export Rule by ID property to Yes.

f) Leave the remaining default settings for the Association node and run the analysis. Examine the results of the association analysis. Your team leader has indicated that the answer to the following questions will be useful to the management. You must answer the questions and provide evidence to support your answers (e.g., screenshots, numeric values, etc.).

1. What is the highest lift value for the resulting rules?
    
2. Which rules have this value?
    
3. What is the significance of the lift value of a rule? Explain using an example from the case study.
    
4. Based on the association rules, briefly describe 3 example product bundles and promotions that you might suggest.