CIS111-6: Intelligent Systems and Data Mining - Marketing Assignment Help

Assignment Task:

Unit learning outcomes
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.

1. 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 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 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: 
(i) group manager, 
(ii) 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.

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        
Assignment 1 Bank Marketing data set is available as a CSV file.

What do I need to do to pass?
1. Apply the Decision Tree technique to solve the Bank Marketing task presented by a set of customer profiles
2. Analyse problems which are required to be resolved in order to develop a solution providing a high prediction accuracy on a given data set.

How do I produce high-quality work that merits a good grade?
3. Identify a set of parameters which are required to be adjusted within DM techniques in order to optimise the solution in terms of prediction accuracy
4. Explain how the parameters of a DM technique influence the prediction accuracy
5. Run experiments in order to verify the solution designed on the given data set
6. Analyse and compare the results of the experiments in a group and with the known from the literature.

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