ICT505 Data Analytics

Assignment 3 – Individual Assignment

Overview

This assignment involves comprehensive research on the use of machine learning methods for predicting customer churn. Students are required to find various machine learning algorithms, explore their application in customer retention strategies, and evaluate their effectiveness using academic literature. The assignment concludes with a detailed report that combines all the research findings, organized in a clear way to demonstrate a thorough understanding of machine learning in customer churn prediction.

Problem Statement

This is an individual assessment task. Each student is required to submit a report of approximately 2000 words. This report should consist of:

• Abstract: Summarizes your findings. (2 marks)

• Introduction: Explains machine learning algorithms, customer churn prediction, why this topic is interesting and important for other researchers, and the history of the topic. (4 marks)

• Literature Review: Surveys the latest techniques from academic research papers regarding customer churn prediction. The aim of this part of the report is to demonstrate a deep and thorough understanding of existing machine learning techniques for customer churn prediction. (6 marks)

• Dataset Description: Describes one of the datasets that other researchers used for customer churn prediction, including the construction of datasets and the features identified for classification. (3 marks)

• Methodology: Depicts the workflow of customer churn prediction, describing the process of conducting customer churn prediction. (2 marks)

• Conclusion: Summarizes the key findings of your research and suggests potential future work. (2 marks)

• This unit requires you to use APA system of referencing. See Sydney International’s quick reference guide. It should be used in conjunction with the online tool Academic Writer: https://extras.apa.org/apastyle/basics-7e/#/. All reports must include at least 5 academic references which must be done using APA7 reference style. (1 mark)

Timelines and Expectations

• Case Study Report and Presentation (30%): 20% for the Report and 10% for a 10min recorded video of presentation.
  • Record your presentation using PowerPoint, ensuring that it includes both voice and video and upload on Moodle.
  • Individual Report and recorded video of presentation: Due date: 1st of September by 11:59 pm.
  • Expected word count 2000 words

Learning Outcomes Assessed

The following course learning outcomes are assessed by completing this assessment task:

ULO1: Exhibit comprehension of the fundamental principles of data analysis, including theoretical frameworks and methodologies applicable to business and social contexts.

ULO2: Exhibit a high level of expertise in assessing data analytics methods critically to solve real-world problems.

ULO3: Exhibit the ability to critically draw from and evaluate research and data at an industry and organizational level to formulate effective strategies and plans.

ULO4: Exhibit a high level of written and verbal communication skills relevant to the planning, design, and implementation of a technical solution.

Submission

All assessments must be submitted through Turnitin on Moodle.

Marking Criteria

Refer to the attached marking guide.