Jupyter Notebook on Lab PCs - IT Assignment Help

Assignment Task

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.

Where to Develop Your Code
You are encouraged to develop and test your code in two environments: Jupyter Note-book 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:nUsersnsXXXXXXX
{ 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

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.

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