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Tutoring/Assistance in solving this assignment. Predictive Models in Business Analytics – Project

INSTRUCTIONS TO CANDIDATES
ANSWER ALL QUESTIONS

Tutoring/Assistance in solving this assignment.

Task 1: Selection

In this task, you need to find a set of 1,000 customers in the test set that contains as many potential

buyers of a caravan policy as possible. In other words, use your most accurate model that you

developed on the training set to select 1,000 most likely policy buyers. Instead of submitting 1,000

selected records, you should submit only a list of 1,000 integers that represent positions of the

selected records in the test data. Thus, if your selection contains the first, third, …, and the last record

in the test data set, you should submit a list of integers that looks like this:

OPMA 419 – Predictive Models in Business Analytics HASKAYNE School of Business | 3

1

3

4000

In other words, your submission should be a file with 1,000 indices - integers between 1 and 4,000. Put

each index on a separate line (without comma's!). Do not put any other text in the file, so your file

should have exactly 1,000 lines.

Task 2: Profit

Analyze the performance of your best model (which can/should be a different model than for task 1, as

the objective is different) and decide which records from the test set should be selected in order to

maximize the profit. As said before, each selected record costs $1, while each selected record that is a

policy buyer provides a profit of $15. This time, the number of selected records has to be determined

by you (if you submit an empty list your profit will be $0; if you submit all 4,000 cases your profit will

be about $15*240 - $4,000 = $-400 - we know that there are about 240 policy buyers in the test set).

Again, only submit the indices of the selected records (as in the previous task).

Expectations and Deliverables

The two tasks have the form of a competition: you should submit your selections by e-mail to

marco.bijvank@haskayne.ucalgary.ca. The maximal number of submissions is three (for each task) and

the best of these three submissions counts as final submission.

There are three deliverables for this project:

• The RapidMiner files associated with the best model for Task 1 and Task 2

• A short PowerPoint presentation (around 4-5 minutes) that highlights your approach that you

will need to present on Wednesday December 8 during the lecture time.

• A report that provides a detailed description how you have addressed the two individual tasks,

what approach/process you have taken, which learning algorithms you have selected to analyze

(including the parameter values), how the learning algorithms performed on your training and

validation dataset, and which learning algorithm (including parameter values) you have selected

for your final model.

 

 

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