Data Mining Framework Report: Learning Outcomes and Activities

The learning outcomes that are assessed by this coursework are:

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.

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.

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

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

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.

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 are linked to group discussion board which you are encouraged to engage in formative feedback with your peers and share your experiences of using SAS Enterprise Miner.  The final activity integrates the pieces into one report detailing:

Activity 1 (upload your post on the discussion board by the end of week 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 (upload your post on the discussion board by the end of week 3)

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 (upload your post on the discussion board by the end of week 5)

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 (upload your post on the discussion board by the end of week 7) 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.