BUSS6002留学生代做、代写via Canvas、代做R、R编程设计调试

日期：2019-04-02 09:20

BUSS6002 Assignment 1

Due Date: Tuesday 16 April 2019

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 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.

3. 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.

4. 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.

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 the functionality of your code segments.

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.

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