BUSS6002 - Individual Assignment Semester 1, 2024

发布时间：2024-05-06

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BUSS6002 - Individual Assignment

Semester 1, 2024

Due Date

. Due: before 23:59 on Wednesday 22 May 2024 (week 13).

. A late penalty of 5% per day applies if you submit your assignment late without a suc- cessful special consideration or simple extension.

Rubric Overview

This assignment is worth 30% of the unit’s marks. The assessment is designed to test your technical ability and statistical knowledge in modelling a real-world dataset, as well as your communication skills in writing a concise and coherent report presenting your approach and results. Refer to the Rubric later in this document for specific details.

Submission Instructions

You must submit:

. a written report (.PDF) with the following filename format, replacing 1234134 with your own student ID: BUSS6002 Report SID1234134.pdf .

. a Jupyter Notebook (.ipynb) file with the following filename format, replacing 1234134 with your own student ID: BUSS6002 Notebook SID12341234.ipynb .

You may submit multiple times before the due date. Your latest submission before the due date will be marked. If you wish to re-submit after the due date please send an email to [email protected] so that markers are notified of your new submission.

Overview

On September 19, 2023 twitter user @purplepingers (Jordan vandenBerg) launched shitrentals.org. The site allows tenants to submit testimonies about landlords, property managers and rental properties. The reviews are then publicly viewable and searchable with the address of the property and the name of the agency visible. You have been given access to the data from shitrentals.org.

As a data-scientist-in-training, your task is to create a publishable research report that investigates and reports on the factors that drive the perceived quality of a rental property. The effect of each factor must be captured in a Generalized Linear Model (GLM) of your choice. All analysis and model building must be performed using Python and collated into a single Jupyter Notebook, which is to be submitted at the same time as your report.

Report

Sections

A template for the report is provided in the assignment pack. Your report must contain:

. Abstract

. Introduction

. Methods

. Results and Discussion

. Conclusion and Limitations

. Bibliography

You may also include Appendices with additional details, figures and tables.

Requirements

. There is a limit of 2500 words for the report excluding tables, captions, bibliography and appendices.

. Assume the reader of your report is a competent and trained data scientist or analyst. They are familiar with the content of BUSS6002.

. All plots, computational tasks, and results must be completed using Python.

. Do not include any Python code as part of your report.

. All figures must be appropriately sized and have readable axis labels and legends (where applicable).

Latex

Using LaTeX is highly recommended, though not required for this assignment. If you do not have LaTeX installed locally we recommend that you use overleaf.com. All students can sign up for an Overleaf Pro+ account via resource portal. If you’renew to Overleaf and LaTeX, help is available via their free introductory course and tutorial video.

Notebook

The submitted .ipynb file must

. contain all the code used in the development of your report,

. be runnable on an Ed environment, and

. must be free of any errors.

Data Description

The dataset contains 1000 property reviews collected between 1/1/2023 and 31/12/2023. To simplify analysis the properties have been restricted to:

. Flats and Units

. 1 and 2 bedroom properties

. 3 suburbs close to the university (Camperdown, Redfern and Newtown).

Refer to the data dictionary for descriptions of the variables.

File Pack