Business data mining | Marketing homework help
Final Project/Exam Content
- Part 1
The final project objective is to develop a model which can be used to determine the shelf position of three new cereal brands (Brand A, B and C). The shelf position should either be a 1, 2 or 3. Use attached Cereal Shelf Data which is similar but not the exact same data used for the MidTerm assignment. Go through the model building steps such
A. cleaning the data such as removing missing records (definitely required) and normalizing (may or may not be necessary)
B. determining the applicable predictor variables that correlate to shelf position (predictive variable),
D. multiple linear regression and
E. scoring the three brands provided in the attached Cereal Brand Shelf Selection file (Review Chapter 2 folder content).
F. Evaluate the performance of your model (Review Chapter 5 folder content)
Universal Bank is a relatively young bank growing rapidly in terms of overall customer acquisition. The majority of these customers are liability customers (depositors) with varying sizes of relationship with the bank. The customer base of asset customers (borrowers) is quite small, and the bank is interested in expanding this base rapidly to bring in more loan business. In particular, it wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors).
A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department to devise smarter campaigns with better target marketing. The goal is to use k-NN to predict whether a new customer will accept a loan offer. This will serve as the basis for the design of a new campaign.
The file UniversalBank.xls contains data on 5000 customers. The data include customer demographic information (age, income, etc.), the customer’s relationship with the bank (mortgage, securities account, etc.), and the customer response to the last personal loan campaign (Personal Loan). Among these 5000 customers, only 480 (= 9.6%) accepted the personal loan that was offered to them in the earlier campaign.
Partition the data into training (60%) and validation (40%) sets.
A. Consider the following customer: Age=40, Experience = 10, Income = 84, Family = 2, CCAvg = 2, Education_1 = 0, Education_2 = 1, Education_3 = 0, Mortgage = 0, Securities Account = 0, CD Account = 0, Online = 1, and Credit Card = 1. Perform a k-NN classification with all predictors except ID and ZIP code using k = 1. Specify the success class as 1 (loan acceptance), and use the default cutoff value of 0.5. How would this customer be classified?
B. Show the classification matrix for the validation data that results from using the best k.
C. Using the same customer as part A, how would this customer be classified using the best k?