Assignment 1: Data Mining Solutions for Direct Marketing Campaign Unit learning outcomes of CIS111-6 Data Mining Solutions For Direct Marketing Campaign Assignment
- 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.
What am I required to do in this CIS111-6 Data Mining Solutions For Direct Marketing Campaign Assignment
- Task
Students will develop a DM solution for saving 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 which will be competitive
on the market. The Manager will interview and hire Data Analysts. 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.
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 roles: (i) group manager, (ii) group member, or will work individually. The group manager will arrange comparison and ranking of solutions designed in a group, and will have additional 5 points. Each student will run individual experiments to find an efficient solution and describe differences in experimental results.
2. 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 Scripting using (i) a Cloud technology CoCalc or (ii) an advanced IDE RStudio free for students.
3. Data
The Assignment 1 Bank Marketing data set is available as a csv file.
4. 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 level of reports must be < 20%.
What do I need to do to pass? (Threshold Expectations from UIF)
- Apply Decision Tree technique to solve the Bank Marketing task presented by a set of customer profiles
- Analyse problems which are required to be resolved in order to develop a solution providing a high prediction accuracy on a given data set.
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