INSTRUCTIONS TO STUDENTS:

1. This End-of-Course Assessment paper comprises 7 pages (including the cover page).

2. You are to include the following particulars in your submission: Course Code,
Title of the ECA, SUSS PI No., Your Name, and Submission Date.

3. Late submission will be subjected to the marks deduction scheme. Please refer to the Student Handbook for details.

Section A (100 marks)

Answer all questions in this section.

The dataset used in this paper contains information about flight price, and its data dictionary is provided in Appendix. Please refer to Canvas for details of this dataset. Notes on assignment writing: Your writing should be succinct but not at the expense of excluding relevant details. The topics in the main report should be presented in the order according to the sequence of the tasks/questions listed in the assignment; that is, in the order of Question 1, Question 2, …, etc. To avoid high Turnitin score, do not copy the assignment questions into the report. Some questions may not come with absolutely right or wrong answers. For such questions, you have the liberty to express your views about the problem. You are also permitted and encouraged to engage in
independent research to demonstrate higher-order thinking skills when answering the questions.

Question 1

Propose and conduct at least three (3) data pre-processing tasks to clean and prepare the given dataset on flight price using Python. Provide relevant explanations. [No more than 300 words (including in-text citation, excluding Python code)] (30 marks)

Question 2

Use Python to plot three (3) figures based on the processed flight price dataset obtained from Question 1. Discuss the insights for each figure accordingly. Each figure and its corresponding Python codes and insights collectively carry 10 marks. The figures and Python codes are to be provided as part of the answer in the main report. [No more than 450 words (including in-text citation, excluding Python code)] (30 marks)

Question 3

Use linear regression to further explore the processed dataset obtained from Question1, where the target variable is ‘price’. Explain the modelling approach taken. [No more than 300 words (including in-text citation, excluding Python code)] (20 marks)

Question 4

Evaluate the model’s performance and state the linear regression equation obtained from Question 3. Discuss the relevant insights. [No more than 200 words (including intext citation, excluding Python code)] (10 marks)

Question 5

Discuss the limitations of the linear regression model above and suggest alternative data analytics methods or models that could be employed to complement or enhance the model obtained from Question 3. Assumptions can be made to support the discussion. [No more than 300 words (including in-text citation)] (10 marks)