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Practical Data Science with Python

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COSC 2670/

assignment 3

Assessment Type Individual
Marks 30

Please read this carefully before attempting

This is anindividualassignment. 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 students 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 solutionrepresents your own work onlyby 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:


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 discus- sion 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 throughhttps://rmit.instructure.com/.

Where to Develop Your Code

You are encouraged to develop and test your code in two environments:Jupyter Note- book on lab PCsandAnaconda 3 that you installed on your own computer.

Jupyter Notebook on Lab PCs

On Lab Computer, you can find Jupyter Notebook via:

StartAll ProgramsAnaconda3 (64-bit)Jupyter Notebook
  • 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 ac- knowledging 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 men- tioned 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.

  • Youmustinclude 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 asautomationis 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 butgryphonwill be rejected.


It is well-known that missing values are one of the biggest challenges in data science projects. You might know thatknearest 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 ink-neighbourhood-based Collab- orative Filtering, and have got solutions there. In this assignment, you are required to tackle the missing value problem in Collab- orative 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.


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 (namedassignment3framework.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 theipynbfiles in Week 10 (knnbasedcfupdated.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

Figure 1: Where to put your implementation in the provided framework (assign- ment3framework.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 penal- ties. The following files should be submitted:

  • Notebook file containing your python implementation, Assignment3.ipynb.
# For the notebook file, follow these steps before submission:
  1. Main menuKernelRestart & 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:
  • YourSlides.pdffile 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.