Python代写 | CMPT 459 Course Project

CMPT 459 Course Project

As the project progresses, more materials may be added to this master document. Please check
out the latest version of this document from time to time.
In this course project, you are going to develop an end-to-end data mining application. The
application includes the following components.
Data sets
You need to choose one or multiple data sets that you are interested in. You can choose one or
more data sets. If two or more data sets are chosen, they have to be used in your data mining
task in a meaningful way, such as the two data sets are integrated, or results are mined from all
data sets.

Application scenario
Describe your data mining task, why it is important and interesting, who may find the mining
results useful and in what way. It would be helpful to use some of your mining results to
demonstrate the interestingness and significance of your data mining task.
Data mining method development
Design your data mining method. You can use any existing tools as long as you make proper
references. You can also write the necessary codes to implement specific functions that you
cannot find available.
Presentation and visualization
You need to deliver your data mining results properly. This may include but is not limited to
visualization, interpretation, case studies, and storytelling.
It is recommended that your report contains the following sections in the specified order.
• Project Title: Come up with an informative and attractive project title (see this page for
some tips)
• Motivation and Background: Who would be interested in the project? Why is it important?
Any related work?
• Problem Statement: What questions do you want to answer?
• Datasets: What are the datasets used? How are they related?
• Architecture/Pipeline: (If applicable) What’s the architecture or design of the project?
What is the data-mining pipeline like?
• Methodology: What is/are the data-mining method/s used (in detail)? What tools or
analysis methods are used? Why did you choose them? How did you apply them to tackle
each problem?
• Evaluation: Why is your solution good? Why does your result make sense?
• Results: What is the outcome of the project? What did you learn from this project?
• Summary: A high-level summary of your project. It should be self-contained and cover all
the important aspects of your project (in alignment with the marking guide).


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