Practical Data Science with Python | My Assignment Tutor

Practical Data Science with PythonCOSC 2670/2738Assignment 3 Assessment TypeIndividualDue Date23:59 on the 11th of June, 2021Marks30 Please read this carefully before attemptingThis is an individual assignment. You may not collude with any other people, or plagiarisetheir 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 yourwritten work to others. Keep any conversation high-level and never show your solutionto others. Never copy from the Web or any other resource. Remember you are meant togenerate the solution to the questions by yourself. Suspected collusion or plagiarism willbe dealt with according to RMIT policy.In the submission (your PDF file) you will be required to certify that the submittedsolution 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 fromelsewhere, then they were non-essential parts of the assignment, and theyare clearly attributed in our submission. I will show I agree to this honorcode by typing Yes”:IntroductionIn this assignment, you are given a specific data science problem and a related researchpaper. You are required to present critical analysis about how to deploy the techniques inthe related research paper to tackle the given data science problem, and then implementit.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 { itis your responsibility to stay informed with regards to any announcements or changes.Login through to Develop Your CodeYou 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 PCsOn Lab Computer, you can find Jupyter Notebook via:Start ! All Programs ! Anaconda3 (64-bit) ! Jupyter NotebookThen,• Select New ! Python 3• The new created ‘*.ipynd’ is created at the following location:{ C:nUsersnsXXXXXXX{ where sXXXXXXX should be replaced with a string consisting of the letters” 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 othersyou 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 thesource if necessary. This includes material taken from Internet sites. If you do notacknowledge the sources of your material, you may be accused of plagiarism becauseyou have passed off the work and ideas of another person without appropriatereferencing, 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 studentsFor further information on our policies and procedures, please refer to the following: RequirementsThis section contains information about the general requirements that your assignmentmust meet. Please read all requirements carefully before you start.2• You must include a plain text file called readme.txt” with your submission. Thisfile should include your name and student ID, and instructions for how to executeyour submitted script files. This is important as automation is part of the 6th stepof data science process, and will be assessed strictly.• Please ensure that your submission follows the file naming rules specified in thetasks below. File names are case sensitive, i.e. if it is specified that the file name isgryphon, then that is exactly the file name you should submit; Gryphon, GRYPHON,griffin, and anything else but gryphon will be rejected.OverviewIt is well-known that missing values are one of the biggest challenges in data scienceprojects.You might know that k nearest neighbour based Collaborative Filtering is also calledmemory-based” Collaborative Filtering. Luckily, data scientists and researchers havebeen 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 topredict the missing values in Collaborative Filtering is provided, which is a report namedEffective Missing Data Prediction for Collaborative Filtering”. Please read this reportcarefully, then complete the following tasks.TasksTask 1: ImplementationIn this task, you are required to implement the solution in the provided report so as topredict the missing values in Collaborative Filtering.Note, you are required to implement your own implementation, and please do not useany 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 helpyou get started, and this will also automate the correctness marking. The frameworkalso includes the training data and the test data.Please only put your own code in the provided cell in the framework as shown inFigure 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 ofdetails should you provide in your solution? Please take the comments in the ipynb filesin Week 10 (knn based cf as examples for the level of detailed comments youare expected to put for your solution. You might find the following information uesful: 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 inthe Collaborative Filtering by using your own language clearly and completely.{ Explain why the solution in the provided report can tackle the missing valueproblem 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 pleasesubmit your own recording (in the format of mp4 or avi) of your presentationas well.What to Submit, When, and HowThe assignment is due at23:59 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:41. Main menu ! Kernel ! Restart & Run All2. Wait till you see the output displayed properly. You should see all the dataprinted 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 toexecute your submitted script files.• Please note: there is no need to submit the data sets, as you are not allowed tochange them.They must be submitted as ONE single zip file, named as your student number (forexample, if your student ID is s1234567). The zip file must be submitted inCanvas:Assignments/Assignment 3.Please do NOT submit other unnecessary files.5


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