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Predictive analytics case study
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Length: word count:1500
Reference No.:- EM132161071




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Predictive Analytics Assignment - Customer Segmentation, Association Rule Mining, and MBA Case Studies

Part A - Cluster Analysis

A wholesale supply company sells four types of dungarees - fashion jeans, leisure jeans, stretch jeans and original jeans. The owner of the supply company is interested in identifying the groupings of stores which his products are supplied. In order to identify such groupings, the owner has selected the DUNGAREE data set gives the number of pairs of four different types of dungarees that were sold at stores over a specific time period.

In the DUNGAREE data set, each row represents an individual store. There are six columns in the data set. One column is the store identification number, and the remaining columns contain the number of pairs of each type of jeans that were sold.

The variables in the data set are shown below with the appropriate roles and levels.

Name

Model Role

Measurement Level

Description

STOREID

ID

Nominal

Identification number of the store

FASHION

Input

Interval

Number of pairs of fashion jeans sold at the store

LEISURE

Input

Interval

Number of pairs of leisure jeans sold at the store

STRETCH

Input

Interval

Number of pairs of stretch jeans sold at the store

ORIGINAL

Input

Interval

Number of pairs of original jeans sold at the store

SALESTOT

Rejected

Interval

Total number of pairs of jeans sold (the sum of FASHION, LEISURE, STRETCH, and ORIGINAL)

You, as the data analyst, is required to conduct a cluster analysis for the data set and provide an insightful report to the owner of the wholesale supply company in order for him to take timely actions to grow his revenue.

a. Create a new diagram in your project. Name the diagram Jeans.

b. Define the data set DUNGAREE as a data source.

c. Determine whether the model roles and measurement levels assigned to the variables are appropriate.

Examine the distribution of the variables.

  • Are there any unusual data values?
  • Are there missing values that should be replaced?

d. Assign the variable STOREID the model role ID and the variable SALESTOT the model role Rejected. Make sure that the remaining variables have the Input model role and the Interval measurement level. Why should the variable SALESTOT be rejected?

e. Add an Input Data Source node to the diagram workspace and select the DUNGAREE data table as the data source.

f. Add a Cluster node to the diagram workspace and connect it to the Input Data node.

g. Select the Cluster node.

Leave the default setting as Internal Standardization → Standardization

What would happen if inputs were not standardized? Explain using knowledge from discussions in the class.

h. Run the diagram from the Cluster node and examine the results.

Does the number of clusters created seem reasonable? Discuss using knowledge from class discussions - what is a cluster/how many clusters should you have, etc....

i. Specify a maximum of six clusters and re-run the Cluster node.

How does the number and quality of clusters compare to that obtained in part h?

j. Use the Segment Profile node to summarize the nature of the clusters. Describe the profiles.

k. The company management would like to consider a stock distribution strategy based on this cluster analysis. Discuss how the clustering and store profiles you have carried out could be used in such a strategy.

Part B - Market Basket Analysis and Association Rules

In order 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 Stationary 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:

bar soap

markers

prescription medications

Bows

pain relievers

shampoo

candy bars

pencils

toothbrushes

Deodorant

pens

toothpaste

greeting cards

perfume

wrapping paper

magazines

photo processing

 

You have access to SAS Enterprise Miner data analytics tools and decided to carry out a market basket and association rule based analysis of the data. The following instructions will help you to set up the SAS diagram for the analysis.

There are four variables in the data set:

Name

Model Role

Measurement Level

Description

Outlet

Rejected

Nominal

Identification number of the store

PurchaseId

ID

Nominal

Transaction identification number

Item

Target

Nominal

Product purchased

Amount

Rejected

Interval

Quantity of this product purchased

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 the model role Rejected. These variables are not used in this analysis. Assign the ID model role to the variable PurchaseId 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 have to answer the questions and prepare a report giving evidence to support your answers - (e.g.: Screen shots, 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?

Part C - Predictive Analytics Case Study

Read the article "Seven reasons you need predictive analytics today - Eric Siegel". Your task is to understand the 7 reasons discussed by Siegel and discuss how each of these:

a) Apply to

b) Add value

In the finance domain.

You are expected to write a report (max 1500 words) using the week 11 guest lecture - "Model management" and the following sources:

1. Analytics in banking: Time to realize the value (Mckinsey Report)

2. 3rd party company case study.

Note - Need to answer just the part C with less than 1500 words by reading the article 7 reasons and the lecture 11 (Model management).

Attachment:- Assignment Files.rar




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    len2161071

    I have the question sheet, you need to answer just the 3rd question with less than 1500 words by reading the article 7 reasons and the lecture 11 (Model management). Note: You MUST refer to the content in the week 11 guest lecture and the articles in your report. The objective of this exercise is for you to learn how to build up your base knowledge for a new project – especially when you have not worked in that area.



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