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CSCU9M5 Data Mining Assignment This assignment is designed to reproduce a commercial data mining consultancy project following the CRISP-DM methodology

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CSCU9M5 Data Mining Assignment

This assignment is designed to reproduce a commercial data mining consultancy project following the CRISP-DM methodology. You are provided with a file of data and required to build a series of machine learning models and then report on your findings.

The client is the owner of a company, and the project aim is to build a classifier capable of improving the company’s engagement campaigns. The data provides details about existing customers, including whether they engaged with the company's most recent campaign. You can download the file from Canvas.

You can use any software of your choice (for example, Orange or Scikit-Learn in Python) and

you will not be required to submit any code, just a report. You should employ best practice

for both the project management and the machine learning aspects of the project.

Your report should follow the CRISP-DM methodology and should also document the correct

methodology for training a machine learning model – particularly the use of train, validate

and test data and the method of searching hyperparameters.

Your report should have the following sections:

Business Understanding [10 Marks]

Describe the task you were given, the data you received and the requirements of the finished

system. Explain why it is a suitable task for a data mining approach. Define any terminology

that you will use in the report (for example, model, variable, task, etc.). Describe the project

methodology you will use.

Data Understanding [10 Marks]

List the variables that you found in the file. For each one, say whether it should be treated as

nominal or numeric, continuous, or discrete and whether or not it should be considered for

building the solution. Identify the inputs and outputs to the model. Explain your decisions.

Data Preparation [10 Marks]

Describe what you did with the data prior to the modelling process. Show histograms of one

example variable before and after any pre-processing that you carried out. If you corrected

any mis-typed entries in the data, report what you changed. Describe what scaling you

performed and how much test data you separated out.

Modelling [50 Marks]

Now you must build some models from the data. Pick three suitable techniques and build a

number of models using each one. For each technique, explore different values for

hyperparameters using an appropriate validation technique. Describe how you used that

technique.Describe what hyperparameters were explored and what effect this had. A summary table is

the best way to present these results. Be methodical and record each result. This stage is a

little like scientific research – you are carrying out experiments in your search for the best

solution.

Once you have chosen the best model, train a final solution using all the training data and

report the appropriate performance metrics.

Results and Errors [20 Marks]

Now test the final model on your test data. Analyse and describe the level of accuracy the

model achieves and the errors your model makes. Show a confusion matrix for the model.

Are there any areas of the data where it performs worse than in others?

Submission

The deadline for submission is 28th November. Upload your report via canvas by the deadline.

Your report should not be more than 2500 words long. Marks will be allocated in line with

the University Common Marking Scheme. That means that to gain a first, you must show

creative thinking and insight. Reflect on the decisions you make, and justify them, ideally with

references to the literature (or other sources). Simply following the correct procedure is

sufficient for a 2:1 grade at most.

You do not need to submit the models that you built, just the report.

You can assume that the client has a good technical understanding of data mining and

statistics, so do not shy away from technical terms in your report. Where you use them,

however, explain what they mean in plain language too.

This assignment is worth 50% of the overall grade for the course and is subject to the usual

grade penalties for late submission. This assignment is set by Keiller Nogueira. You can email

questions about it to keiller.nogueira@stir.ac.uk.

Feedback

You will receive feedback and a grade by December 19th.

Plagiarism

Work which is submitted for assessment must be your own work. All students should note

that the University has a formal policy on plagiarism which can be found at

http://www.quality.stir.ac.uk/ac-policy/assessment.php.