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This is the ANL252 Python for Data Analytics end-of-course assessment brief for the SUSS July 2026 semester. The paper uses a payment delay dataset (ECAPaymentDelay) and asks for five linked answers in one Word report: data pre-processing, three figures with insights, a linear regression on Days_Overdue_Delay, model evaluation with the regression equation, and further analytics techniques. The report is due on 30 October 2026 at 12 noon.
Section A (100 marks) – Answer all questions
The dataset used in this paper contains information about payment delay, and its data dictionary is provided in the 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 of the tasks/questions listed in the assignment (Question 1, Question 2, and so on). To avoid a high Turnitin score, do not copy the assignment questions into the report. Some questions may not have absolutely right or wrong answers; for such questions you may express your views about the problem, and you may engage in independent research to demonstrate higher-order thinking skills.
Python code in text format in a monospaced font (for example, Courier New or Lucida Sans Typewriter) must be provided in the main report (the main report excludes the appendices). Less relevant details may be placed in the Appendix. The word limit for each question includes in-text citations and the corresponding content in the appendix (if any), but excludes Python code and the reference list.
Question 1 (30 marks)
Propose at least three (3) data pre-processing methods and apply them using Python to ensure the given payment delay dataset is properly cleaned and prepared. Provide explanations for each task. [Word limit: 300 words]
Question 2 (30 marks)
Use Python to generate three (3) figures based on the processed payment delay dataset from Question 1. Discuss the key insights derived from each figure. Each figure, together with its corresponding Python code and discussion of insights, is worth 10 marks. The figures and their corresponding Python code must be included in the main body of the report as part of the answer. [Word limit: 450 words]
Question 3 (15 marks)
Use linear regression with Python to model the processed dataset obtained from Question 1, with ‘Days_Overdue_Delay’ as the target variable. Explain the modelling approach taken and the decisions made in developing the model. [Word limit: 300 words]
Question 4 (15 marks)
Using Python, evaluate the performance of the linear regression model developed in Question 3, state the resulting regression equation, and discuss the relevant insights. [Word limit: 300 words]
Question 5 (10 marks)
Discuss additional data analytics techniques or modelling approaches that could enhance the insights obtained from the linear regression model above. You may make reasonable assumptions to support your discussion. [Word limit: 300 words]
Appendix: Data Dictionary (ECAPaymentDelay dataset)
| Variable | Description |
| Record_ID | Unique identifier of each payment delay record |
| Amount | Transaction amount in USD |
| Region | Customer region code |
| City | Customer city code |
| Age_Of_Customer_Months | Customer age in months |
| Payment_Term | Number of days allowed for the customer to pay an invoice (the standard credit period) |
| No_of_orders_by_customer | Total number of orders in the customer’s purchase history |
| Rank_of_order_by_customer | Sequential number of the order in the customer’s purchase history (1 = first order, 2 = second order, etc.) |
| Weekday_clearing | Weekday of payment clearance |
| Weekday_due | Weekday of due date |
| Quarter_clearing | Quarter in which payment was cleared |
| Days_Overdue_Delay | Number of days payment was overdue (target variable) |
Submission Requirements
- Submit one report in Microsoft Word (.docx, Word 2016 or higher) to Canvas via Turnitin by 12 noon on Friday, 30 October 2026.
- Include the course code, ECA title, SUSS PI number, name, submission date and T/TG group on the cover page.
- Python code must be shown in monospaced text for each part of the question; charts are inserted as images in the Word document.
- Generative AI tools are allowed with full attribution (tool name, prompts used, full output and which parts were adapted); SUSS may use a viva voce to confirm authorship.
- Late submissions are subject to the SUSS mark-deduction scheme; references follow APA.
Get Expert Help with the ANL252 ECA
The hard part of this ECA is explaining your choices in very few words: why a cleaning step suits the payment delay records, what each chart reveals about late payers, and how to read the regression output without overclaiming. Many students also run out of words in Question 2, where code, figures and insights must sit together in the main report.
Our tutors can check your Python workflow and help you write clear, concise answers. Ask for coursework writing help for SUSS ECAs, use our do my assignment service for guided support, or look at another SUSS tech brief, the ICT340 ECA July 2026 question.