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STAT2020 PREDICTIVE ANALYTICS – PROJECT S2/2019
OVERVIEW
This assessment involves writing a report that summarises a statistical learning related investigation that you have
conducted on data that you have chosen yourself. The investigation must involve the main topics covered in the
course, most noticeably supervised learning and or unsupervised learning using R/RStudio.
It builds upon the practical knowledge that you must have acquired in the labs and other activities throughout the
course, however neither the dataset nor the detailed steps to be carried out will be provided here, you have to
make independent choices and decisions.
You will need to find your own data using good practices. Your dataset cannot be smaller than 1000 observations
of 5 variables.
Do not use data from textbooks or from R packages. Do not use data for which statistical learning results and
analyses can be found online; this will likely be the case for many datasets from websites such as UCI Machine
Learning Repository, Kaggle, OpenML, and other popular data science repositories alike, so these should be
avoided unless you make sure that something similar or equivalent to your project is not readily available, fully
or partially. You can use public data, but the data should be appropriate for addressing a relevant statistical
learning problem, and a solution to a similar problem for the same data should not be available.
You don’t need to solve this entire statistical learning problem in your investigation, but you need to clearly
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indicate what the targeted problem would be about and how your project can reasonably contribute towards
addressing it.
You have to write a report with details about the problem in question, the data, the methods, results, analyses and
findings. You might like to look online for research papers for examples of how to shape your report. Obviously
many of these papers will have undergone extensive work to collect their data, we don’t expect that for you.
We also don’t expect you to win a Nobel prize with this assessment. Ideally, you will be able to demonstrate that:
(a) you have grasped important concepts associated with this course, most noticeably supervised and unsupervised
learning; and (b) you can communicate your investigation in a formal written manner.
Regarding (a), we expect that your investigation will include at least three of the following topics:
1. Decision trees for classification
2. Ensembles of decision trees for regression (bagging and/or random forests)
3. Principal Component Analysis (PCA)
4. Cluster Analysis
5. Unsupervised Outlier Detection
6. Support Vector Machines
7. Formal Quantitative Assessment of Results (e.g., cross-validation for model assessment and selection in
regression or classification, clustering evaluation and model selection, etc.)
8. Qualitative Assessment through Visualisation of Results (e.g. visualisation and interpretation of
clustering hierarchies, visual interpretation of PCA, etc.)
Regarding (b), we strongly encourage you to prepare your report using R Markdown, with all relevant code
chunks disclosed. If you do, you have to deliver (in Blackboard) both the R Markdown source file (.Rmd) and
the resulting file (.html or .PDF) that is produced by compiling/knitting the source code. If you don’t use R
Markdown, you will have to provide both your report in PDF format and your complete R code as an R script
source file (.R). Either way, make sure you include set.seed(0) in the beginning of your code, alongside with the
information about the version of R/RStudio that you have used. The markers are not supposed to run your code
or R Markdown file, but they may refer to these files while marking your report, if some clarification is needed.
REPORT FORMAT
The main body of the report (containing title, abstract, introduction, data, methods, results and discussion, and
conclusions) cannot exceed 8 (eight) single-column pages when printed in A4 format using R Markdown
default settings for font, font size, line spacing, margins, etc. A maximum of 2 (two) additional pages are
allowed for bibliographic references and appendices containing any supporting material that you may want to
include. Therefore, your report cannot exceed 10 (ten) A4 printed single-column pages in total. If you prepare
your report using a text editor such as MS Word or Latex, make sure you follow as close as possible (visual
inspection and common sense should suffice) the R Markdown default format in terms of font, spacing, margins,
etc., respecting the same aforementioned limits on number of pages. NOTE: Only the main body and references
will be formally assessed for grading, though additional material in appendices (if any) may help clarify issues
that can possibly arise during the marking process. Notice that relevant pieces of R code can and should be
displayed throughout the report, interwoven with the presentation, discussions and corresponding results, just like
in the weekly course materials. Further details about the report structure are provided in the following section.
REPORT STRUCTURE
The report should have the following sections marked clearly:
• Title: In today’s busy world, it is very important to make the most of your title. Make the title ‘eye-catching’,
informative and an accurate representation of the contents of the report.
• Abstract: The abstract provides a short sharp overview of the contents in the report and will be around 200 –
300 words. The abstract has five parts:
i. Introductory statement: background to the study, important issue(s) the report addresses.
(approximately 1 to 2 sentences)
ii. Purpose of the report: state the objectives (1-2 sentences)
iii. Methodological approach: overview the data and methods (2-3 sentences)
iv. Findings or Achievements: list one or two of the main findings or achievements from your
investigation (1-2 sentences)
v. Conclusions and Implications: what conclusions can be drawn from your investigation? How can
the findings/achievements in your report deliver a benefit to people, things, systems or processes?
(1-2 sentences)
• Introduction: The introduction sets the scene for the investigative efforts. It provides motivation for the work
and relevant background information and references that will enable the reader to put in context the key
objectives and achievements in your report. Address the important issues that have motivated your
investigation. At the end of the introduction clearly state the objectives of the report. Do not put any results
from your investigation in the introduction. Do not discuss details about the data and methods in this section.
Do not discuss your conclusions or key findings in the introduction.
• Data: This section should provide details about how the data was obtained, pre-processed (if applicable) and
what the data represent. You should include information such as:
i. What the source of the data is.
ii. How the data was originally collected (e.g. from an experiment or observational study).
iii. The sample size.
iv. The number and types of variables.
v. Any known interventions or pre-processing that precede the ones described in your report.
vi. Any interventions or pre-processing that you did as part of your report. NOTE: it is part of your
work to consider and possibly make interventions (e.g. variable rescaling / normalisation, etc.)
that may be required or recommended prior to application of a given statistical learning method.
vii. Any other information that is relevant to the understanding and assessment of your work/report.
• Methods: This section should summarise the statistical learning methods that were used to process and to
analyse the data, as well as the software version used to generate the results and report. To cite R-Studio type
RStudio.Version() from the command line. The methods should be appropriate to ensure that the objectives
of the report are met. You are strongly encouraged to interleave your text with key calls to R functions that
generated relevant results that you may want to highlight, just like the weekly course notes and labs. This can
be achieved straightforwardly using R Markdown. You can use R Markdown display control settings (e.g.
echo = FALSE) to hide chunks of code that you judge less relevant, but these must still be present in the
source code verification, if necessary. In the textual description, it is important to provide the sufficient level
of details so that your methodology could be repeated by an independent person, while being clearly and
objectively presented so that it can be understood without the need to check your complete R code.
• Results and Discussion: This section presents and discusses the results. The discussion centres on the outputs
from the statistical learning procedures that you have performed. For example, what are the main outcomes?
Why are they useful and what for? How are they interesting and why? Etc. In particular, how do the results
align with the goals set in the introduction? What are the main achievements and their implications?
• Conclusions: Final remarks about the key achievements of the investigations and what makes them
“interesting” or “useful”, right now or for future work. Achievements or findings should be contrasted with
the original objectives or hypotheses of the project. Make sure that you mention any limitations of your work
here. Limit the conclusions to no more than two or three paragraphs.
• References. List the sources your investigation has drawn from. Note that all references should be referred
to in the text.
• Appendices (optional): Add any supporting materials that might be useful to help assess your work.
FORMAT SUMMARY
The main body of the report must be presented in HTML or PDF using R Markdown default settings or
equivalent for font, font size, line spacing, margins, etc., on no more than 8 (eight) A4 single-column pages.
References and appendices can be listed on at most 2 (two) additional pages.
In total, the report cannot exceed 10 (ten) A4 printed single-column pages, to be uploaded in Blackboard.
WARNING: only the main body and the references will be formally assessed and graded.
IMPORTANT NOTE
The entire project must be accomplished using R/RStudio. Any calculations, visualisations, results, etc.
produced using software other than R/RStudio (e.g. Excel, Tableau, etc.) is not accepted and therefore will not
be assessed. Failure to comply with these requirements will incur in your work being considered as not
delivered. Use of R Markdown is not compulsory but you are encouraged to adopt it to prepare your report.
A WORD ON PLAGIARISM AND SELF-PLAGIARISM:
Plagiarism is the act of using another’s words, works or ideas from any source as one’s own. Plagiarism has no
place in a University. Student work containing plagiarised material will be subject to formal university processes.
In case significant portions of your own previous work (e.g. a report for a related course you did in this or any
other university) is recycled in a way that it could be fully or partially graded twice (“double-dipping”), this is
considered self-plagiarism and will not be tolerated.
MARKING SCHEME
Please adhere to the strict formatting requirements. The report will not be assessed if it is not formatted appropriately.
Total marks possible 120.
Dimension Sophisticated [100% marks] Competent [50% marks] Needs Work [0% marks]
Title
[2 marks]
The title is a concise (less than 20 words)
and accurate reflection of the contents of the
report. Author is listed below the title.
The title is a concise (less than 20 words)
and moderately reflects the contents of
the report. Author is listed.
The title is not informative or
exceeds the word length or Author
not listed.
Abstract
[6 marks]
Clearly addresses the five parts of the
abstract so that the reader has a clear
overview of the reports.
Partially addresses the five parts of the
abstract and or addresses all five parts
but the writing is not clear in places.
Unclear, does not overview the
report, or the writing is poor
overall and mostly unclear
Introduction
[16 marks]
Position and exceptions, if any, are clearly
stated. Organization of the argument is
completely and clearly outlined and
implemented.
Position is clearly stated. Organization
of argument is clear in parts or only
partially described and mostly
implemented.
Position is vague. Organization of
argument is missing, vague, or not
consistently maintained.
Data
[20 marks]
Data are suitable, the report explains how
the data were obtained, and all of the
following information items (whenever
applicable) are clearly explained:
i. What the source of the data is.
ii. How the data was originally collected
(e.g. from an experiment or
observational study).
iii. The sample size.
iv. The number and types of variables.
v. Any known interventions or preprocessing
that precede the ones
described in your report.
Data are suitable, the report explains how
the data were obtained, and most of the
applicable data information items are
addressed and reasonably explained.
Little information/explanation
about the data is provided and/or
the grammar structure is difficult
to follow and/or the data do not
meet the minimum requirements.
vi. Any interventions or pre-processing that
you did as part of your report.
vii. Any other information that is relevant to
the understanding and assessment of
your work/report.
Methods
[28 marks]
Lists all the steps in order in which they
were performed to explore, analyse and
obtain patterns and/or models from the data.
These steps, if executed appropriately and
interpreted appropriately, will ensure that
the objectives of the report are clearly met.
At least 3 of the following targeted key
topics from the course have been explored
and explained in depth:
1. Decision trees for classification
2. Ensembles of decision trees for
regression (bagging and/or random
forests)
3. Principal Component Analysis (PCA)
4. Cluster Analysis
5. Unsupervised Outlier Detection
6. Support Vector Machines
7. Formal Quantitative Assessment of
Results
8. Qualitative Assessment through
Visualisation of Results
Most of the steps are listed and
explained, but some details are a little
hazy or questionable. At least 2 of the
targeted key topics from the course
(listed in the leftmost column) have been
reasonably explored and explained.
The methods clearly will not allow
the objectives of the report to be
met and/or the details of
methodological steps and
procedures are very difficult to
follow and/or the listed key topics
from the course have been poorly
or not appropriately explored.
Results and
Discussion
[22 marks]
The results and discussion are explained
correctly, clearly, and in sufficient detail.
The results and discussion clearly follow
from the data collection and the methods.
The results and discussion are explained
correctly, clearly and in sufficient detail
most of the time. There exists a
connection of some type between the
results/discussion and the data collection
and methods.
One or more of the items
discussed in the middle column
are missing.
Conclusion
[10 marks]
The original objectives and/or hypotheses
are restated and contrasted against the
obtained achievements and/or findings.
The conclusion summarizes and draws a
clear, effective conclusion of the
investigation and enhances the impact of the
report – e.g., it provides a recommendation
or action that should be undertaken in the
future. It may also highlight unavoidable
limitations of the investigation.
Conclusion is clearly stated and
connections to the original objectives
and/or hypotheses are mostly clear.
Conclusion may not be clear
and/or the connections to the work
reported are incorrect or unclear or
just a repetition of the findings
without a suitable summarisation
and interpretation and/or the
underlying logic has major flaws.
Writing
[16 marks]
Report is coherently organized and the logic
is easy to follow. There are no spelling or
grammatical errors and terminology is
clearly defined. Writing is clear and concise
and persuasive.
Each Figure/Table will be numbered,
followed by a caption, and referred to in the
body of the text, most noticeably in the
results and/or discussion section. The
Figures/Tables provided reinforce the most
relevant achievements of the work.
All references have been listed and referred
to in the appropriate places in the body of
the text and listed at the end of the report. At
least 3 references have been provided.
Report is generally well organized and
most of the argument is easy to follow.
There are only a few minor spelling or
grammatical errors, or terms are not
clearly defined. Writing is mostly clear
but may lack conciseness.
Each Figure/Table will be numbered,
followed by a caption, and referred to in
the body of the text, most noticeably in
the results and/or discussion section.
Most references have been listed and
referred to in the appropriate places in
the body of the text and listed at the end
of the report. At least 3 references have
been provided.
Report is poorly organized and
difficult to read – does not flow
logically from one part to another.
There are several spelling and/or
grammatical errors; technical
terms may not be defined or are
poorly defined; figures/tables
and/or references are sloppy or
missing. Writing lacks clarity and
conciseness.
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标签：mat top sign margin discuss chunk settings val eal
原文地址：https://www.cnblogs.com/python34/p/11622613.html
