Data Mining Solutions for Direct Marketing Campaign

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