代写Java小程序： Finding customer clusters at the Telco

Posted 2020-11-01 helpcode

There can be no extensions to this date. All assignments for

this subject for this semester MUST be submitted by the 29th March. No Exceptions.

Previously, management have been quite directed about what they were looking for and what they

wanted to predict. Now, they are asking you what kind of things can be ‘discovered’ from the data.

In particular they are interested in whether there are any kind of natural groupings that exist within

their customer base.

You have access to the same data as Assignment 1.

Your goal is to find and explain any natural grouping you find within the data. You only need to

concentrate on finding one explainable way to group customers, and then explain that grouping in

business terms.

Deliverables:

Your final deliverables will 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 you would share with a colleague on the Data Science team)

PDF2 - only show those things necessary to help support management decision making (this is the

one you send to management!)

These will both be submitted online through Turnitin.

Guidance:

? Document

o Focus on a good document structure and layout (revisit week 2 on repeatable

research)

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 do not really

understand data science, so you will need to find a tradeoff between understandability and

verbosity. Verbose assignments will be penalized.

o You will need to use visualizations

o You will need to explain at least 1 of the clusters - and it must be useful from a

business perspective

Note:

As is the case with all assignments I set, if you do the minimum (correctly), then you will receive half

marks. Additional marks are awarded for those assignments where you have clearly put in

additional thought, whether it be in visualization, modelling, succinctness, or coding elegance.

 

 

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