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BUSS6002 Assignment
Instructions
• You must submit a written report (in PDF) with the following filename format, replacing
STUDENTID with your own student ID: BUSS6002 A2 STUDENTID.pdf.
• You must also submit a Jupyter Notebook (.ipynb) file with the following filename format,
replacing STUDENTID with your own student ID: BUSS6002 A2 STUDENTID.ipynb.
• There is a limit of 2000 words for your report (excluding equations, tables, and captions).
• All plots, computational tasks, and results must be completed using Python.
• Each section of your report must be clearly labelled with a heading.
• 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).
• The submitted .ipynb file must contain all the code used in the development of your report.
• The submitted .ipynb file must be free of any errors, and the results must be reproducible.
• You may submit multiple times but only your last submission will be marked.
• A late penalty applies if you submit your assignment late without a successful special con-
sideration. See the Unit Outline for more details.
1
Rubric
This assignment is worth 20% 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.
Assessment Item Goal Marks
Section 1 Introduction 3
Section 2 Candidate models 10
Section 3 Model estimation and selection 12
Section 4 Model evaluation 8
Section 5 Conclusion 3
Overall Presentation Clear, concise, coherent, and professional 4
Total 40
Table 1: Assessment Items and Mark Allocation
Overview
Being able to accurately predict the sale prices of residential properties is crucial to many aspects
of the economy. Some companies base their entire business models on providing their clients with
predictions of property sale prices. As a data scientist, you are asked to build a model to predict
sale prices using data on residential home sales in Ames, a city in the state of Iowa of the United
States. The dataset contains sale prices between 2006 and 2010 of all residential properties in
Ames, as well as many numerical and categorical features (i.e., variables) associated with each
dwelling. The following downloadable files are available on Canvas.
File Description
AmesHousing.txt Data file containing 2,930 observations and 82 variables
DataDocumentation.txt Data dictionary containing description of each variable
AmesResidential.pdf A map of Ames
Table 2: Files Provided
Data