Reference no: EM133067896 , Length: word count:5000

CIS111-6 Intelligent Systems and Data Mining - University of Bedfordshire

Assignment Task: Data Mining Solutions for Direct Marketing Campaign

Learning outcome 1: Analyse Data Mining techniques capable of supporting practitioners to make reliable decisions which require predictive modelling, for example, in a Business scenario

Learning outcome 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

Learning outcome 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.

How students will work

Each student is expected to run individual experiments to find an efficient solution and describe experimental results in an individual report. Students could work on the assignment task as: (i) a group manager, (ii) a group member, or (iii) an individual. If students will work in a group, the group manager arranges the comparison and ranking of designed solutions.

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, (ii) a development suite RStudio or an RStudio Cloud free for students. Other scripting languages such as Python supported e.g. by Google Colab online platform 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.

1. Follow a CoCalc tutorial to create an individual account (or install RStudio)
2. Create an R project containing the given project script and data set
3. Apply a Decision Tree technique to solve the Bank Marketing task
4. Work on scripting problems is evaluated and students are expected to demonstrate the knowledge on how to find a solution by using related manuals and google search
5. Analyse problems of designing a solution which will provide a high prediction accuracy
6. Identify a set of parameters required to be adjusted within DM techniques in order to optimise a solution in terms of prediction accuracy
7. Explain how the parameters of a DM technique influence the prediction accuracy
8. Run experiments in order to verify the solution designed on the given data set
9. Analyse and compare the results of the experiments in a group and with results known from the literature

Assignment Task: Advanced Data Mining Techniques for Direct Marketing Campaigns

Task

Students will develop one or more Advanced Data Mining (ADM) techniques for saving the cost of a direct marketing campaign by reducing false positive (wasted call) and false negative (missed customer) predictions. Students could consider the scenario of the Assignment 1 to optimise a given marketing campaign with ADM techniques. Examples of ADM techniques such as Artificial Neural Networks and Gradient Boosting Machines will be given in related tutorials.

Method and Technology
To design a solution, students will apply ADM techniques discussed in lectures and will use related packages available on the R repository. Using the ADM techniques, students will run individual experiments to find a solution providing the best accuracy of predicting client profiles on the bank marketing used for designing a solution to the Assignment 1. The use of this data allows students to compare different DM techniques in terms of prediction accuracy.

Project Data
Download the Bank marketing data (.csv) required for individual experiments.

Individual Report
Each solution will be evaluated in terms of the prediction accuracy determining the efficiency of the marketing campaign. All submissions are made via BREO. A template can be used for reports.

1. Apply an ADM technique to solve the Bank Marketing task presented by a benchmark data
2. Analyse problems required to develop a solution providing a high prediction accuracy on a given data set
3. Identify a set of parameters which are required to be adjusted within an ADM technique in order to optimise the solution
4. Explain how the parameters of an ADM technique influence the prediction accuracy
5. Run experiments in order to verify the solution on a given data set
6. Analyse and compare the results of the experiments in a group and with the known from the literature.
7. Optionally make a 5-min video presentation and include a link to the Appendix in reports

Attachment:- Intelligent Systems and Data Mining.rar