Reference no: EM133029570

IMAT3613 Data Mining - De Montfort University

Organics Data Set Report Assignment

Learning outcome 1: To discuss what is meant by the term data mining, to be able to express a business problem within a data mining framework and identify an appropriate target variable.

Learning outcome 2: To be able to identify necessary prequisites for the application of a data mining framework and discuss the limitations of data mining methods with regard to data and methods in the context of the business problem.

Learning outcome 3: To describe and analyse the organisational structure of large data sets to facilitate effective data mining and to be able to correctly interpret and critically evaluate the results to make informed decisions within a data mining framework

Learning outcome 4: To be express the data mining framework for a particular business problem through the correct application of data mining tools.

Learning outcome 5: To interpret the results produced from data mining tools and evaluates the effectiveness of data mining methods and where necessary making appropriate recommendations for use in the virtuous cycle of data mining.

Data Mining Report A Patchwork Assignment

Structure of the coursework.

The coursework is an individual piece of assessment, requiring you to analyse the ORGANICS dataset within SAS Enterprise Miner or Weka, using the directed data mining techniques covered in the IMAT3613 module, and detailing your results, interpretations, conclusions and recommendations in a well-structured technical report. You are provided with:

- This Brief
- A sample of data from the ORGANICS dataset is shown in Appendix A.
- The ORGANICS dataset contains 10,000 observations and 13 variables. The variables in the dataset are shown in Appendix B.
- The coursework will be assessed according to the marking grid in Appendix C
- Self/Peer Assessment Rubric Appendix D
- Template Report in Appendix E

Lab Journal and Reflection

To help your produce this report in a timely manner, the report is built up from four biweekly activities. You have an opportunity to modify your work from each activity in light of your own reflection and self-assessment feedback. Ten percent of the marks are awarded for a reflection on how you have developed your report over the term. The length of this reflection should be at least 200 words and is to be included in your appendix. To help you produce this reflection you may make use of the journal feature on blackboard and the self-assessment grid to record your progress.

The last and fifth activity is to produce an integrated report with conclusions and recommendations you will complete independently.

In the odd weeksit is suggested that you upload your answer to the activity on the lab journal.

In the even weeks you are expected to comment on your work using the self-assessment rubric and assign a grade A,B,C,D or F. At the beginning of the even weeks a rubric will be produced for each activity to guide you in the assessment of your self-assessment. These marks are not used to make up the final mark however your engagement with the process is. This has been designed to help you structure your work and pace the development of the report over the term.

You may modify your weekly contributionsthrough your engagement in the lab journal. In fact you are encouraged to do so. You should treat the lab journal as a notebook of your activities for the week.

In the final exercise you will integrate all four activities and the final activity into a report.

In each activity you are expected to produce a piece of writing from between 200 and 400 words, producing a final report to a maximum of 2000words excluding, table of contents, diagrams and appendices.You are provided a template report to complete, existing words in the template do not count to the report maximum.

Scenario: The ORGANICS dataset

1. A supermarket is beginning to offer a line of organic products. The supermarket's management would like to determine which customers are likely to purchase these products.

2. The supermarket has a customer loyalty program. As an initial buyer incentive plan, the supermarket provided coupons for the organic products to all of their loyalty program participants and have now collected data that includes whether or not these customers have purchased any of the organic products.

You are a data miner and have been commissioned by the supermarket's manager to analyse the ORGANICS data and to provide the manager with the best model that s/he should use to identify the customers who are likely to buy the supermarket's new line of organic products.

The analysis you are conducting will represent the first flow of the virtuous cycle of data mining.

You will be assessed on producing a technical, well-structured, comprehensive but concise report to the manager of the supermarket. This report is broken up into five activities, four of which you are encouraged to do biweekly and self-assess your work using the lab journal. The final activity integrates the pieces into one report detailing:

Activity 1
- Develop a description of the business problem and appropriate data mining problem and describe a data mining framework that is appropriate for your brief. Identify the target variable.

- Make appropriate use of Exploratory Data Analysis on your data set to develop insights that will inform your data mining process suggest any transformations which might be appropriate.

Activity 2
- Apply regression analyses to your dataset including the full model and the Selection Methods: Forward, Backward and Stepwise. Develop a regression equation which includes only significant parameters at the 95% confidence interval.

Activity 3
- Conduct a Decision Tree analysis on the data set, vary the default parameters and present an interpretation of your results. If appropriate develop a tree by hand. Identify the target path(s) and critical path.

Activity 4
Conduct a Neural Network analysis on the data set, vary the default parameters and present an interpretation of your results. You may choose to try different neural network architectures. Identify the most important weights together with a diagram identifying the neural network architecture.

Activity 5 Remaining time
- Justification of your final selected model, by considering appropriate data mining strategies: Cumulative Lift Charts, Non-Cumulative Lift Charts and Diagnostic Charts.
- Conclusions
- Recommendations on how to improve the quality of the supermarket's data collection process in the future, to enable you as a data miner the opportunity to improve on the accuracy of the data mining model in further flows of the data mining cycle. Develop and integrate your activities into a full technical report.

In the Appendix of the report you need to include:

1. A table of the model roles and measurement levels of the variables (to produce sensible analyses).
2. A view of the random seed generator illustrating the digits of your DMU student id number that you have used (to produce sensible analyses).
3. A copy of the process flow diagram.
4. A reflectionof at least 200 words describing how your interaction with the discussion board modified or shaped the development of your report during the patchwork process.

Attachment:- Data Mining.rar