SUSS ANL252 Python for Data Analytics ECA July 2026: Payment Delay Pre-processing, Visualisation and Linear Regression, Singapore
About the SUSS ANL252 ECA, July 2026
In this end-of-course assessment for SUSS ANL252 Python for Data Analytics, you work with a payment delay dataset to show a complete analytics workflow: three or more pre-processing methods, three figures with insights, a linear regression predicting Days_Overdue_Delay, an evaluation with the regression equation, and ideas for better models. Everything goes into one Word report due on Canvas by 30 October 2026 at 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.
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The ANL252 marking rewards clear reasoning more than long code. Students often lose marks for unexplained cleaning steps, charts with no real insight about overdue payments, or regression results reported without discussing fit, assumptions and limits, all while staying inside short word limits.
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