BUSS6002代做、代做Python编程设计、Canvas代写、Python设计代做

日期：2019-09-16 11:12

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

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