ANL252 Python for Data Analytics End-of-Course Assessment 2026 | SUSS, Singapore

ANL252 End-of-Course Assessment

Instruction 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.

Important Note

ECA Submission Deadline: Friday, 03 April 2026 12:00 pm 

Section A (100 marks)

Answer all questions in this section.

The dataset used in this paper contains information about customer churn, 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 to engage in independent research to demonstrate higher-order thinking skills when answering the questions. You are suggested to include less relevant details in your Appendix, if any.

Question 1

Propose and conduct at least three (3) data pre-processing tasks to clean and prepare the given dataset on customer churn using Python. Provide relevant explanations. [No more than 300 words (including the corresponding content in appendix and in-text citation; excluding Python code and reference list)]

(30 marks)

Question 2

Use Python to plot three (3) figures based on the processed customer churn 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 the corresponding content in appendix and in-text citation; excluding Python code and reference list)]

(30 marks)

Question 3

Use Python to further analyse or model the processed dataset obtained from Question 1 using a decision tree, where the dependent variable is ‘Churn_Flag’. Explain the relevant steps involved in constrcucting the decision tree model. [No more than 200 words (including the corresponding content in appendix and in-text citation; excluding Python code and reference list). You do not need to plot the decision tree in this question.]

(20 marks)

Question 4

Plot the decision tree model obtained from Question 3 with Python. Discuss the relevant insights based on the tree plot. [No more than 200 words (including the corresponding content in appendix and in-text citation; excluding Python code and reference list)]

(10 marks)

Question 5

Discuss other data analytics methods or models that could be used to strengthen the insights gained from the decision tree model above. Assumptions can be made to support the discussion. [No more than 300 words (including the corresponding content in appendix and in-text citation; excluding reference list)]

(10 marks)

Appendix

DATA DICTIONARY

—– END OF ECA PAPER —–