CIS111-6 Assignment Brief: Advanced Data Mining Techniques for Direct Marketing Campaigns Task 2

Assignment Title:	Task 2: Advanced Data Mining Techniques for Direct Marketing Campaigns
Task 2 count words:	2500

Assignment Task 2: Advanced Data Mining Techniques for Direct Marketing Campaigns

Task

Students will develop one or more Advanced Data Mining (ADM) techniques for saving thecost of a direct marketing campaign by reducing false positive (wasted call) and falsenegative (missed customer) predictions. Students could consider the scenario of theAssignment 1 to optimise a given marketing campaign with ADM techniques. Examples ofADM techniques such as Artificial Neural Networks and Gradient Boosting Machines will begiven in related tutorials.

Method and Technology

To design a solution, students will apply ADM techniques discussed in lectures and will use related packages available on the R repository. Using the ADM techniques, students will run individual experiments to find a solution providing the best accuracy of predicting client profiles on the bank marketing used for designing a solution to the Assignment 1. The useof this data allows students to compare different DM techniques in terms of prediction accuracy.

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Project Data

Download the Bank marketing data (.csv) required for individual experiments.

Individual Report

Each solution will be evaluated in terms of the prediction accuracy determining theefficiency of the marketing campaign. All submissions are made via BREO. A template canbe used for reports. Exclude paste&copy fragments to avoid plagiarism.

What do I need to do to pass? (Threshold Expectations from UIF)

1. Apply an ADM technique to solve the Bank Marketing task presented by a benchmarkdata (20%)
2. Analyse problems required to develop a solution providing a high prediction accuracyon a given data set (22%)
3. In total 42% to pass

How do I produce high quality work that merits a good grade?

1. Identify a set of parameters which are required to be adjusted within an ADMtechnique in order to optimise the solution
2. Explain how the parameters of an ADM technique influence the prediction accuracy
3. Run experiments in order to verify the solution on a given data set
4.  Analyse and compare the results of the experiments in a group and with the knownfrom the literature.
5. Optionally make a 5-min video presentation and include a link to the Appendix inreport