1. Analyse a Data Mining technique capable of supporting practitioners to make
reliable decisions which require predictive modelling, for example, in a
2. Demonstrate results of using an efficient technique which is capable of finding
a solution to a given predictive problem represented by a data set
3. Evaluate the accuracy of the technique in terms of differences between the
predicted values and the given data
Students will 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.
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 or (ii) a development
suite RStudio free for students. Other scripting languages such as Python could be also
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.
Report submission and report template
Each solution will be evaluated in terms of the costs of false decisions made on the
validation data. Reports will be submitted via BREO. Reports can be prepared with a
template. BREO similarity in reports must be < 20% (scripting is not counted).
min. 2000 word