Python机器学习代写 | Practical Data Science with Python COSC 2670/2738 Assignment 3

本次澳洲代写主要为python机器学习协同过滤的assignment

Introduction
In this assignment, you are given a speci c 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 through https://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 PCs and Anaconda 3 that you installed on your own computer.

Jupyter Notebook on Lab PCs
On Lab Computer, you can nd 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.

General Requirements
This section contains information about the general requirements that your assignment
must meet. Please read all requirements carefully before you start.

• You must include a plain text le called \readme.txt” with your submission. This
le should include your name and student ID, and instructions for how to execute
your submitted script les. 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 le naming rules speci ed in the
tasks below. File names are case sensitive, i.e. if it is speci ed that the le name is
gryphon, then that is exactly the le 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 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. Speci cally, an existing solution about how to
predict the missing values in Collaborative Filtering is provided, which is a report named
\Eective Missing Data Prediction for Collaborative Filtering”. Please read this report
carefully, then complete the following tasks.