There can is no extensions to this date. All assignments for
This semester must is submitted by the 29th March. Subject No Exceptions.
Previously, management has been quite directed what's they were looking for and what they
wanted to predict. Now, they is asking you what kind of things can is ' discovered ' from the data.
In particular they is interested in whether there is any kind of natural groupings that exist within
Their customer base.
You had access to the same data as assignment 1.
Your goal is to find and explain any natural grouping you find within the data. Need to
Concentrate on finding one explainable "to" group customers, and then explain this grouping in
Business terms.
Deliverables:
Your final deliverables'll be 2 PDF files, both produced by the same. RMD script (with different
Code chunk options). You must submit:
Pdf1-all code and results shown (like your would share with a colleague on the Data science team)
Pdf2-only show those things necessary to help support management decision making
One you send to management!)
These'll both be submitted online through Turnitin.
Guidance:
? Document
o Focus on a good document structure and layout (revisit Week 2 on repeatable
()
o Hint:think about the headings in the document you produce
? Focus on letting the visualizations do the talking. Only include explanatory text where it is
Really necessary ... although you should remember that management does not really
Understand data science, so you'll need to find a tradeoff between understandability and
verbosity. Verbose assignments'll be penalized.
O you'll need to use visualizations
O You'll need to explain at least 1 of the clusters-and it must is useful from a
Business Perspective
Note:
As is the case with all assignments I set, if you do the minimum (correctly) and then you'll receive half
Marks. Additional marks is awarded for those assignments where you had clearly put in
Additional thought, whether it is in visualization, modelling, succinctness, or coding elegance.
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Write Java applet: Finding customer clusters at the Telco