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COSC 2670/2738 Practical Data Science
创建日期：2024-08-06 14:25:18
标签： COSC 2670
Practical Data Science

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COSC 2670/2738 Practical Data Science (with Python)

Project Assignment 

Marks : This assignment is worth 30% of the overall assessment for this course.

 Late penalties

apply. A penalty of 10% of the total project score will be deducted per day. No

submissions will be accepted 5 days beyond the due date.

Objective

The key objectives of this assignment are to learn how to compare and contrast

several recommendation system algorithms. There are three major components of the

assignment – a completed Jupyter notebook used to run your experiments, a written

report, and a short video presentation where you describe what you did and your key

findings.

The dataset you will use will be a sample of the Netflix Prize data. The problem is

movie recommendation, and the data is already split into a training and validation set

that you can use to run all of your experiments.

Provided files

The following template files are provided:

SXXXXX-A3.ipynb : The primer Jupyter notebook file you should use to stage

and run all of your experiments.

netflix-5k.movie-titles.feather : The movie title dataframe that can be used to

map a movieID to a title, as well as a list of genres.

netflix-5k.train.feather : The training tuples for 5,000 users, where each tuple

is userID,movieID,rating?.

netflix-5k.validation.feather : A predefined set of validation tuples for the

same users that can be used by you to benchmark the performance of various

algorithms.

A3.pdf : This specification file.

Creating Your Workspace

Once again, you should rename the file SXXXXX-A3.ipynb appropriately based on

your student ID.

Creating Your Anaconda Environment

In order to create your anaconda environment for this project, you should run the

following command in a terminal shell:

conda create -n PDSA3 python=3.8

conda activate PDSA3

pip install jupyterhub notebook numpy pandas

pip install matplotlib scikit-learn seaborn

1

pip install kneed scikit-surprise

Note that both kneed and scikit-surprise can be finicky on some systems. For

example on my machine, an error was thrown during the compile of scikit-surprise

(Macbook Pro M1) but the install still worked when it tried a fallback install method.

So if you find that it really fails for you using pip, then and only then resort to conda.

This would break requirements.txt, but it should work reliably for everyone albeit not

very reproducible. The magic commands would be:

conda install -c conda-forge kneed

conda install -c conda-forge scikit-surprise

You can type “pip freeze” to see a list of the packages that are correctly installed

in your environment. You can also install scikit-learn-intelex and/or psutil if

you want to use the Intel-based optimisations or debug memory management as shown

in the sample Jupyter notebook.

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