ECS784U代写、代做python编程

日期：2023-08-05 11:02

Coursework 1 specification for 2022-23

Data Analytics ECS784U/P,

Revised on 08/02/2023,

Dr Anthony Constantinou, Dr Neville Kitson.

1. Important Dates

Release date: end of Week 2, Friday 3rd February 2023 at 12:00 noon.

Submission deadline: mid-Week 8, Wednesday 15th March 2023 at 10:00AM.

Late submission deadline (cumulative penalty applies): Within 7 days after deadline.

General information:

i. Students will sometimes upload their coursework and not hit the submit button. Make sure you fully complete the submission process.

ii. A penalty will be applied automatically by the system for late submissions.

a. Lecturers cannot remove the penalty!

b. Penalties can only be challenged via submission of an Extenuating

Circumstances (EC) form which can be found on your Student Support page. All the information you need to know is on that page, including how to submit an EC claim along with the deadline dates and full guidelines.

c. Deadline extensions can only be granted through approval of an EC claim

d. If you submit an EC form, your case will be reviewed by a panel. When the

panel reaches a decision, they will inform both you and the module organiser.

e. If you miss both the submission deadline and the late submission deadline, you

will automatically receive a score of 0.

iii. Submissions via e-mail are not accepted.

iv. The School requires that we set the deadline during a weekday at 10:00 AM.

v. For more details on submission regulations, please refer to your relevant student handbook.

2. Coursework overview and deliverables

The submission involves two files: a data analytic report (see Deliverable 1) and a Jupyter notebook (see Deliverable 2).

You should address ayes';font-family:'Arial Unicode MS';mso-fareast-font-family:MicrosoftYaHei; font-size:11.0000pt;" >

Once you determine the area that interests you the most, you should search for a suitable data set or collate the data set yourself (see Section 5 for possible data sources).

You should apply a minimum of TWO data analytic techniques (i.e. machine learning algorithms) of your choice to your data, from those covered in this course up to and including Week 5. The aim is to learn two models and contrast their performance on your input data. You are allowed to test more than TWO data analytic techniques if you wish (e.g., using multiple techniques to learn a model, or learning more than two models), but this is not a requirement and will not necessarily improve your mark. Remember to use the page limit wisely against the marking criteria (see below). The algorithms you can choose from are:

o Linear, non-linear and logistic regression, o Support vector classification or regression, o Decision trees,

o KNN,

o k-means, o GMMs.

Deliverable 1: Technical report.

The report shall have a maximum length of 7 pages including references. Pages

exceeding the 7-page limit will NOT be marked.

Font size should be not lower than 11, and page margins should be not lower than 2cm. There are no other formatting requirements; e.g., it can have a single-column or a two-column format.

Reports should be written with a technical audience in mind. It should be concise and clear, adopting the same style you would use in writing a scientific report or project dissertation.

Some of the components your report should include:

i. Problem statement and hypothesis.

ii. A review of relevant literature

iii. Description of your data set and how it was obtained, including a sample of the data presented in a figure, along with pointers to your data sources.

iv. Description of any data pre-processing steps you took (if any). 2

v. What you have learnt by exploring the data; you may include some visualisations if necessary.

vi. How you chose which features to use in your analysis.

vii. Details of your modelling process, including how you selected your data analytic

methods, as well as how you determined the optimal model through validation.

viii. Your challenges and successes.

ix. Key findings.

x. Possible extensions or business applications of your project.

Deliverable 2: Jupyter notebook:

You must submit your Jupyter notebook Python code as a separate PDF file. This is needed so that we can quickly refer to your code outputs while marking your report. Please do not forget to add some section headings and comments around your code, similar to those added to the notebooks used in the labs.

a. In Windows, you can generate a PDF file by right clicking and selecting ‘Print’ the Jupyter notebook loaded in your browser, and then you should be given an option to save it as a PDF file.

b. Do NOT copy-and-paste your notebook’s code into a word document, as this approach will not preserve the notebook’s format.

c. You do NOT need to submit your data set nor the actual .ipynb file. These might be requested at a later stage, if and only if we would like to review your code and/or data in greater depth.

3. Marking criteria

This coursework contributes 60% towards your total module mark.

   Criterion

#3 #4

#6

#8

Part of report

Weighting

Evidenced by (at least)

  #1

 Introduction to the project and background information

   10%

  Problem statement and hypothesis; project aims; concepts communicated; clarity.

  #2

Literature review

  5%

 Subject placed in the context of literature; a minimum of 5 references to journal papers, conference papers, or books (web references do not count).

Data processing Learning methods

Concluding remarks

Jupyter notebook

10% Data source; description of data; any pre- processing steps; any feature selection or dimensionality reduction methods.

10% Brief description of the two data analytic methods; justification of the methods

selected.

20% Discuss limitations and achievements; possible future improvements or

directions; conclusion based on results.

10% Jupyter notebook presented clearly with some comments.

      #5

 Analysis, testing, results

   15%

  Includes possible cross-validation or any other approach to assess learning accuracy; documentation of testing; analysis of the strengths and weaknesses.

   #7

 Quality of report

   20%

  Clarity; organisation; quality of the writing; quality and clarity of tables and figures; ease of understanding of the presentation of ideas.

    Please note: We do realise that each project is different. The marking scheme shown above represents a simplified version of the marking scheme we use to assess project dissertations, and applies to any data analytic project.

4

4. Timetable

The coursework lasts for almost 6 weeks; from end of Week 2 to mid-Week 8. To ensure the coursework runs smoothly, be careful not to deviate much from the timetable below:

Weeks 2 and 3: Determine your project area and data set.

What is the question you hope to answer? What data are you planning to use to answer that question? What do you know about the data so far? Why did you choose this topic?

You may discover during your data exploration that you do not have the data necessary to answer your project’s question. You may decide to change the research question to address in the project. You should aim to finalise any changes as soon as possible.

Our advice is to spend your time during the first week wisely doing some research on the data sources and the data analytic methods covered in the labs, depending on the problem you are trying to address. Researching appropriate data sets and determining what data analytic method to use is part of the coursework. Various data sources are provided for reference at the end of this document.

Week 4: Data processing.

What data have you gathered, and how did you gather it? What steps have you taken to explore the data? Which areas of the data have you ‘cleaned’ (if any)? If your data may not need cleaning, explain why. What insights have you gained from your exploration (visualisations are optional here – consider page limit)? Will you be able to answer your question with these data, or do you need to gather more data (or perhaps adjust the project aims)? How might you use modelling to answer your question?

Weeks 5 and 6: Apply two data analytic methods and analyse results.

Which are the two data analytic methods you have selected and why? Is this a supervised or an unsupervised learning problem? Is this a classification or a regression problem? What do you understand about the two data analytic methods and why are they appropriate in answering the project question/s? Apply them to your data and start exploring the results. Generate plots that help explain the results.

Week 7: Have produced your first draft.

At a minimum, this should include a) literature review and background information on your selected topic, b) narrative of what you have done so far, c) visualisations of the results.

Week 8: Finalise draft.

Remember we have no lectures or labs on Week 7, so use that week to complete the coursework.

5. Data sources

Using public data is the most common choice. If you have access to private data, that is also an option, though you will have to be careful about what results you can release to us. Some sources of publicly available data are listed below (you don`t have to use these sources).

? Kaggle