2019S2 BUSS6002 Assignment 1
Value: 15% of the total mark
Instructions
1. Required Submission Items:
1. ONE written report (PDF format). submitted via Canvas.
• Assignments > Report Submission (Assignment 1)
2. ONE Jupyter Notebook .ipynb submitted via Canvas.
• Assignments > Upload Your Code File (Assignment 1)
2. The assignment is due at 12:00pm (noon) on Friday, 27 Sep 2019. The late
penalty for the assignment is 5% of the assigned mark per day, starting after
12:00pm on the due date. The closing date Friday, 4 Oct 2019, 12:00pm
(noon) is the last date on which an assessment will be accepted for marking.
3. As per anonymous marking policy, please include your Student ID only in the
report and do NOT include your name. The name of the report and code file
must follow: SID_BUSS6002_Assignment1. Failing to name your submitted
files correctly would incur a penalty.
4. Your answers should be provided as a final report giving full explanation and
interpretation of any results you obtain. Output without explanation will receive
BUSS6002作业代做、代做Python编程设计作业、Canvas课程作业代写
zero marks. You are required to also submit code that can reproduce your
reported results, as reproducibility is a key component to data science. Not
submitting your code will lead to a loss of 50% of the assignment mark.
5. Be warned that plagiarism between individuals is always obvious to the
markers of the assignment and can be easily detected by Turnitin.
6. Presentation of the assignment is part of the assignment. There will be 10
marks for the presentation of your report and code submission.
7. The report should be NOT more than 10 pages including text, figures, tables,
small sections of inserted code etc. Think about the best and most structured
way to present your work, summarise the procedures implemented, support
your results/findings and prove the originality of your work. You will provide
your code as a separate submission to the report; however, you may insert
small sections of your code into the report when necessary.
8. Your code submission has no length limit, however marks are assigned for
code presentation, so make your code as concise as possible and add
comments when necessary to explain your logic and the purpose of each
code segment. Make sure to remove any unnecessary code and ensure that
your code can be run without error.
9. Numbers with decimals should be reported to the third-decimal point.
20
Project Description and Dataset
Suppose you are working as a Data Scientist for a real estate investment firm. The
firm is assessing locations for investing in housing redevelopment in the United
States. For this purpose, the firm has identified several potential locations in Seattle
to purchase existing houses, which would be demolished to make space for the
redevelopment.
In order to estimate the costs involved the firm needs to know the current market
value of the houses that it needs to purchase. You are working on a project that aims
to build a model to estimate the house prices.
Seattle’s Department of Assessments has been collecting data since 2014 on house
sale prices and the characteristics of each house that was sold. You have been
given access to a copy of original database “house.db”, which is an SQLite file, as
well as a data dictionary file “house_dict.txt”. You can download the dataset and
detailed dataset description from the BUSS6002 Canvas site.
Hint: To list all tables in the database you can use the following query
SELECT name FROM sqlite_master WHERE type='table' ORDER BY name;
Task 1
To start your analysis, you wish to perform a thorough EDA to help you better
understand the given datasets. The results you obtain in this task will be used to
inform your modelling choice.
Requirements:
a. Check and deal with any missing data (if any) in the given dataset.
b. Look for and remove any potential outliers (if any) that would possibly affect
your modelling. Justify your answer.
c. Visualise the relationships between explanatory variables and the target
variable through appropriate plotting. Report your analysis and findings.
Task 2
Suppose now you want to build a prototype model to predict house sale prices, which
will be demonstrated to a wider team. Therefore, it needs to be easily understood by
non-experts, meaning that you can only use a few variables in your model as a starting
point.
In order to make informed decisions on your modelling choices, you need to answer
the following questions:
a. Suppose you would like to build a linear regression model to predict house sale
prices, do you wish to include an intercept term in your model? Carefully explain
your answer.
b. Do you think multicollinearity could be a potential problem on the given dataset?
Use your understanding of variables to justify your answer and verify your
hypothesis using appropriate numeric measures. Explain your decisions to
proceed based on your findings.
c. If you wish to use only three variables to predict house sale prices, which three
variables would you choose? Carefully justify your choice and explain your
selection criterion.
d. Build a linear regression model using the three variables you have chosen (Use
original, i.e. not engineered, variables for this task). Report and interpret your
regression results.
e. Perform residual diagnostics to measure the goodness of fit. Report your
findings.
Task 3
The model you have built so far provides an approximate estimate of house prices.
However, to accurately estimate the costs of the redevelopment plan you must be
able to estimate house prices as accurately as possible.
Your goal is now to improve your model as much as you can through feature
engineering and feature selection. You may consider all variables and apply
appropriate transformation to the variables as necessary.
Requirements:
a. Your model should have a minimum adjusted R-Squared of 75%. If your
modelling cannot achieve an adjusted R-Squared of 75%, report the best
model you can obtain.
b. Justify your choice of feature engineering strategies using EDA and present
your results.
c. Compare your new model with the model you have built in Task 2 with respect
to Adjusted R-Squared. Explain why you should use Adjusted R-Squared here
to compare the two models.
d. Provide residual analysis to justify why your new model is more reasonable.
Task 4
Suppose you have finished your analysis, now you need to report to your manager
and reflect on what you have experimented with in your project:
a. Provide a reflection of how you have utilised the data science process model
to arrive at modeling and model evaluation based on how you answered the
previous three questions. Choose only one process model (CRISP-DM or
Snail Shell) to answer this question. Explain how each part of the questions
aligns with the different phases of the process model.
b. The firm is also considering redevelopment projects in other locations.
Comment on whether the model you have built can or cannot be applied in
other locations. Justify your answer.
Marking Outline
Tasks Marks
Task 1 20 marks
Task 2 30 marks
Task 3 30 marks
Task 4 10 marks
Report and Code Presentation 10 marks
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