Practical Data Science with Python
COSC 2670/2738
Assignment 3
Assessment Type Individual
Due Date 23:59 on the 11th of June, 2021
Marks 30
Please read this carefully before attempting
This is an individual assignment. You may not collude with any other people, or plagiarise
their work. You are expected to present the results of your own thinking and writing.
Never copy other student’s work (even if they “explain it to you first”) and never give your
written work to others. Keep any conversation high-level and never show your solution
to others. Never copy from the Web or any other resource. Remember you are meant to
generate the solution to the questions by yourself. Suspected collusion or plagiarism will
be dealt with according to RMIT policy.
In the submission (your PDF file) you will be required to certify that the submitted
solution represents your own work only by agreeing to the following statement:
I certify that this is all my own original work. If I took any parts from
elsewhere, then they were non-essential parts of the assignment, and they
are clearly attributed in our submission. I will show I agree to this honor
code by typing “Yes”:
Introduction
In this assignment, you are given a specific data science problem and a related research
paper. You are required to present critical analysis about how to deploy the techniques in
the related research paper to tackle the given data science problem, and then implement
it.
The “Practical Data Science” Canvas contains further announcements and a discussion
board for this assignment. Please be sure to check these on a regular basis – it
is your responsibility to stay informed with regards to any announcements or changes.
Login through https://rmit.instructure.com/.
Where to Develop Your Code
You are encouraged to develop and test your code in two environments: Jupyter Notebook
on Lab PCs and Anaconda 3 that you installed on your own computer.
Jupyter Notebook on Lab PCs
On Lab Computer, you can find Jupyter Notebook via:
Start → All Programs → Anaconda3 (64-bit) → Jupyter Notebook
Then,
• Select New → Python 3
• The new created ‘*.ipynd’ is created at the following location:
– C:\Users\sXXXXXXX
– where sXXXXXXX should be replaced with a string consisting of the letter
“s” followed by your student number.
Academic integrity and plagiarism (standard warning)
Academic integrity is about honest presentation of your academic work. It means acknowledging
the work of others while developing your own insights, knowledge and ideas.
You should take extreme care that you have:
• Acknowledged words, data, diagrams, models, frameworks and/or ideas of others
you have quoted (i.e. directly copied), summarised, paraphrased, discussed or mentioned
in your assessment through the appropriate referencing methods
• Provided a reference list of the publication details so your reader can locate the
source if necessary. This includes material taken from Internet sites. If you do not
acknowledge the sources of your material, you may be accused of plagiarism because
you have passed off the work and ideas of another person without appropriate
referencing, as if they were your own.
RMIT University treats plagiarism as a very serious offence constituting misconduct.
Plagiarism covers a variety of inappropriate behaviours, including:
• Failure to properly document a source
• Copyright material from the internet or databases
• Collusion between students
For further information on our policies and procedures, please refer to the following:
https://www.rmit.edu.au/students/student-essentials/rights-and-responsibilities/
academic-integrity.
General Requirements
This section contains information about the general requirements that your assignment
must meet. Please read all requirements carefully before you start.
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• You must include a plain text file called “readme.txt” with your submission. This
file should include your name and student ID, and instructions for how to execute
your submitted script files. This is important as automation is part of the 6th step
of data science process, and will be assessed strictly.
• Please ensure that your submission follows the file naming rules specified in the
tasks below. File names are case sensitive, i.e. if it is specified that the file name is
gryphon, then that is exactly the file name you should submit; Gryphon, GRYPHON,
griffin, and anything else but gryphon will be rejected.
Overview
It is well-known that missing values are one of the biggest challenges in data science
projects.
You might know that k nearest neighbour based Collaborative Filtering is also called
“memory-based” Collaborative Filtering. Luckily, data scientists and researchers have
been working hard to solve the missing value problem in k-neighbourhood-based Collaborative
Filtering, and have got solutions there.
In this assignment, you are required to tackle the missing value problem in Collaborative
Filtering by predicting them. Specifically, an existing solution about how to
predict the missing values in Collaborative Filtering is provided, which is a report named
“Effective Missing Data Prediction for Collaborative Filtering”. Please read this report
carefully, then complete the following tasks.
Tasks
Task 1: Implementation
In this task, you are required to implement the solution in the provided report so as to
predict the missing values in Collaborative Filtering.
Note, you are required to implement your own implementation, and please do not use
any other libraries that are related to Recommender Systems or Collaborative Filtering.
If you use any of these libraries, your implementation part will be invalid.
We provide Python framework code (named assignment3 framework.ipynb) to help
you get started, and this will also automate the correctness marking. The framework
also includes the training data and the test data.
Please only put your own code in the provided cell in the framework as shown in
Figure 1, Please DO NOT CHANGE anything else in the rest cells of the framework,
otherwise they might cause errors during the automatic marking.
Please provide detailed comments to explain your implementation. To what level of
details should you provide in your solution? Please take the comments in the ipynb files
in Week 10 (knn based cf updated.zip) as examples for the level of detailed comments you
are expected to put for your solution. You might find the following information uesful:
https://www.w3schools.com/python/python_comments.asp
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Figure 1: Where to put your implementation in the provided framework (assignment3
framework.ipynb)
Task 2: Presentation
• The presentation should
– Explain how the solution in the provided report predicts the missing values in
the Collaborative Filtering by using your own language clearly and completely.
– Explain why the solution in the provided report can tackle the missing value
problem in Collaborative Filtering clearly and completely.
– Explain how you implement the solution clearly and completely.
• The presentation should be no more than 10 minutes.
• Your presentation slides should be:
– Microsoft PowerPoint slides (with audio inserted for each slide by using: Insert
− > Audio − > Record Audio).
– or you can create your own presentation slides (e.g. PDF version) and please
submit your own recording (in the format of mp4 or avi) of your presentation
as well.
What to Submit, When, and How
The assignment is due at
23:59 on the 11th of June, 2021.
Assignments submitted after this time will be subject to standard late submission penalties.
The following files should be submitted:
• Notebook file containing your python implementation, ‘Assignment3.ipynb’.
# For the notebook file, follow these steps before submission:
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1. Main menu → Kernel → Restart & Run All
2. Wait till you see the output displayed properly. You should see all the data
printed and graphs displayed.
• One of the following:
– Your Slides.pdf file and your presentation recording in the required format.
Or,
– Your Microsoft PowerPoint slides (with audio inserted for each slide).
• The “readme.txt”: includes your name and student ID, and instructions for how to
execute your submitted script files.
• Please note: there is no need to submit the data sets, as you are not allowed to
change them.
They must be submitted as ONE single zip file, named as your student number (for
example, 1234567.zip if your student ID is s1234567). The zip file must be submitted in
Canvas:
Assignments/Assignment 3.
Please do NOT submit other unnecessary files.