CIS111-6 Assignment Task 1: Data Mining Solutions for Direct Marketing Campaign Question

Assignment Task 1: Data Mining Solutions for Direct Marketing Campaign

Learning Outcomes

• Analyse Data Mining techniques capable of supporting practitioners to makereliable decisions which require predictive modelling, for example, in a Business scenario
• Demonstrate results of using an efficient technique which is capable of findinga solution to a given predictive problem represented by a data set
• Evaluate the accuracy of the technique in terms of differences between the predicted values and the given data.

Task

Students will develop a DM solution for saving the cost of a direct marketing campaign byreducing false positive (wasted call) and false negative (missed customer) decisions.Working on this assignment, students can consider the following scenario. A Bank hasdecided to save the cost of a direct marketing campaign based on phone calls offering aproduct to a client. A cost efficient solution is expected to support the campaign withpredictions for a given client profile whether the client buys the product or not.Examples of cost-efficient DM solutions for direct marketing are provided on the UCIMachine Learning repository describing a Bank Marketing problem.

How students will work 

Each student is expected to run individual experiments to find an efficient solution anddescribe experimental results in an individual report. Students could work on theassignment task as: (i) a group manager, (ii) a group member, or (iii) an individual. Ifstudents will work in a group, the group manager arranges the comparison and ranking ofdesigned solutions.

Method and Technology

To design a solution, students will use Data Mining techniques such as Decision Trees.Students are recommended to use R scripting: (i) a Cloud CoCalc, (ii) a development suiteRStudio or an RStudio Cloud free for students. Other scripting languages such as Pythonsupported e.g. by Google Colab online platform could be also used.

Project Code and Data

The assignment project code is available as an R Script.The Bank Marketing data set isavailable as a csv file. Other data sets (Kaggle or UCI) could also be used.Report submission and report templateEach solution will be evaluated in terms of the costs of false decisions made on thevalidation data. Reports will be submitted via BREO. Reports can be prepared with atemplate. BREO similarity in reports must be < 20% (scripting is not counted).

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

1. Follow a CoCalc tutorial to create an individual account (or install RStudio)
2. Create an R project containing the given project script and data set
3. Apply a Decision Tree technique to solve the Bank Marketing task
4. Work on scripting problems is evaluated and students are expected to demonstrate the knowledge on how to find a solution by using related manuals and google search
5. Analyse problems of designing a solution which will provide a high prediction accuracy
6. In total 42% to pass

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

1. Identify a set of parameters required to be adjusted within DM techniques in order tooptimise a solution in terms of prediction accuracy
2. Explain how the parameters of a DM technique influence the prediction accuracy
3. Run experiments in order to verify the solution designed on the given data set
4. Analyse and compare the results of the experiments in a group and with resultsknown from the literature
5. In total