BUSS6002

Posted 2021-09-06 rt1lj4fp

BUSS6002 Assignment 1
Due Date: Tuesday 16 April 2019
Value: 15% of the total mark
Instructions

1. Required Submission Items:
2. ONE written report (PDF format). submitted via Canvas.
   Assignments > Report Submission (Assignment 1)
3. ONE Jupyter Notebook .ipynb submitted via Canvas.
   Assignments > Upload Your Code File (Assignment 1)
4. The assignment is due at 12:00pm (noon) on Tuesday, 16 April 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 Tuesday, 23 April 2019,
   12:00pm (noon) is the last date on which an assessment will be accepted for
   marking.
5. As per anonymous marking policy, please include your Students ID only in the
   report and do NOT include your name. The name of the report and code file
   must follow: SID_BUSS6002_Assignment1_S12019.
6. Your answers shall be provided as a word-processed report giving full
   explanation and interpretation of any results you obtain. Output without
   explanation will receive zero marks. You are required to also submit your
   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.
7. Be warned that plagiarism between individuals is always obvious to the
   markers of the assignment and can be easily detected by Turnitin.
8. Presentation of the assignment is part of the assignment. There will be 10
   marks for the presentation of your report and code submission.
9. 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.
10. 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 the functionality of your code segments.
    Make sure to remove any unnecessary code and ensure that your code can
    be run without error.
11. Numbers with decimals should be reported to the third-decimal point.
    Tasks
    Suppose the year is 2010 and you are working as a Data Scientist for an investment
    firm. The firm is assessing locations for investing in housing redevelopment in the
    United States. The firm has selected Ames, Iowa as a candidate location. As a
    consequence, the firm would need to purchase existing houses, which would be
    demolished to make space for the development.
    In order to estimate the costs involved the firm needs to know the current value of
    the houses that it needs to purchase. You are working on a data science project
    aiming to build a model to estimate the house prices.
    The Ames City Assessor’s Office has been collecting data since 2006 on house
    sales and the characteristics of each house that was sold. You have been given
    access to a copy of original database “housing.db”, which is an SQLite file. The
    Assessor’s Office have also provided you with a data dictionary
    “housing_data_description.txt”.
    You can download the dataset and detailed dataset description from the BUSS6002
    Canvas site.
    Question 1
    To start your analysis, you wish to build a prototype model that 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.
    To save you time, an experienced member of your team suggests to you that from
    their experience the above ground living area, basement size and the age of the
    house are most useful variables.
    Perform EDA to determine which two of these features are most useful. Carefully
    explain your selection criteria and present the results to justify your choice.
    Requirements:
    a. To most accurately reflect the conditions under which the firm will purchase
    the houses you should limit your analysis to houses that are sold under
    normal conditions.
    b. Remove observations that contain one or more missing variables.
    Question 2
    Suppose you are interested in using the above ground living area and basement size
    to estimate the price of a home.
    a. Build a linear regression model WITHOUT an intercept term (MODEL1), write
    down the mathematical model and report the regression output.
    b. Build a linear regression model WITH an intercept term (MODEL2), write down
    the mathematical model and report the regression output.
    c. Compare the performance of the two models and explain the role and impact
    of the intercept term
    d. Pick either MODEL1 or MODEL2 that you think is preferable and perform
    residual diagnostics to measure the goodness of fit. Report your findings.
    Question 3
    The models you have built so far provide 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 possible through feature
    engineering and feature selection.
    Instructions:
    a. Your model should have a minimum R-Squared of 77%. If your modelling
    cannot achieve a R-Squared of 77%, report the best model you obtain.
    b. Justify your choice of feature engineering strategies using domain knowledge
    or EDA and present your results.
    c. Compare your new model with the preferable model in Question 2 with
    respect to Adjusted R-Squared. Explain why you should use Adjusted RSquared
    here to compare the two models.
    d. Provide analysis to justify why your new model is more reasonable.
    Question 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 data science project:
    a. Provide a reflection of how you have utilized 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 you choose to answer
    the question.
    a. 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
    Questions Marks
    Question 1 20 marks
    Question 2 20 marks
    Question 3 40 marks
    Question 4 10 marks
    Report and Code Presentation 10 marks

WX：codehelp