- Analyze a Data Mining technique capable of supporting practitioners to make reliable decisions which require predictive modeling, 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
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 subscribes to the product or not. A startup company wants to develop an innovative DM technology that will be competitive on the market. The Manager will interview and hire Data Analysts. The team will analyze the existing technologies to design a DM solution winning the competition.
A team Manager will choose the best solution for 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.
Examples of cost-efficient DM solutions for direct marketing are provided on the UCI Machine Learning repository describing a Bank Marketing problem.
Students will apply for one of the roles:
- group manager,
- group member, or will work individually.
The group manager will arrange a comparison and ranking of solutions designed in a group and will have an additional 5 points. Each student will run individual experiments to find an efficient solution and describe differences in experimental results.
Method and Technology
To design a solution, students will use Data Mining techniques such as Decision Trees and Artificial Neural Networks. Examples of solutions will be provided in R Script using
- a Cloud technology CoCalc or
- an advanced development suite RStudio free for students