Develop a DM solution for saving the cost of a direct marketing campaign by reducing false positive (wasted call) and false negative (missed customer) decisions. Working on this assignment, students can consider the following scenario. A Bank has decided to save the cost of a direct marketing campaign based on phone calls offering a product to a client. A cost efficient solution is expected to support the campaign with predictions 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 UCI Machine Learning repository describing a Bank Marketing problem.

How students will work

Each student is expected to run individual experiments to find an efficient solution and describe experimental results in an individual report. Students could work on the assignment task as: (i) a group manager, (ii) a group member, or (iii) an individual. If students will work in a group, the group manager arranges the comparison and ranking of designed 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 suite RStudio or an RStudio Cloud free for students. Other scripting languages such as Python supported 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 is available as a csv file. Other data sets (Kaggle or UCI) could also be used.