Analyse a Data Mining technique capable of supporting practitioners to make reliable decisions which require predictive modelling, for example, in a Business scenario

Demonstrate results of using an efficient technique which is capable of finding a 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

will develop a Data Mining (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 accepts or rejects the offer. A company wants to develop an innovative DM technology which will be competitive on the market. The team will analyse the existing technologies to design a DM solution winning the competition. A team Manager will choose the best solution for the market competition in terms of cost efficiency. The evaluation of the developed solutions will be made on the test data. The costs will be defined for both the false positive and false negative predictions.

Method and Technology

Examples of cost-efficient technologies for direct marketing are provided on the UCI Machine Learning repository describing a Bank Marketing problem.

To design a solution, students will use Data Mining techniques such as Decision Tree. Examples of solutions will be provided within an R Cloud technology CoCalc available online. An alternative is RStudio which can be used freely