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MIS272 – Trimester 2 2021 – Predictive Analytics


MIS272 – Predictive Analytics ‐ Trimester 2 2021 Assignment 2 – Group Assignment

DUE DATE AND TIME:                   8 pm AEST, 21 September PERCENTAGE OF FINAL GRADE:     40%

WORD COUNT:                           3,000 words (written report)


The purpose of this assignment is to develop your ability to construct a predictive model (using regression and association analysis) to solve a problem based on understanding a specified business context.

The business context for this assignment relates to decision‐making based on consumer complaints. Many countries around the world set up regulatory bodies that receive and act on complaints lodged by consumers against companies under their jurisdiction. The frequency and severity of received complaints informs targeted decision‐making of the regulator (such as further investigations, regulatory directives, and in extreme cases, legal action against companies).

The specific dataset that you are given is from the regulatory body in a particular country. The regulator receives large volumes of complaints lodged against various companies that provide several types of services to customers. Every time a complaint is lodged against a company by a customer or by another business, the regulator assesses the company’s ongoing ‘fitness’ score in the business domain (based on several external factors). This aggregate fitness score is calculated for the company according to the assessment by the regulator as part of responding to the complaint (the better the company’s score, the fitter the company is in terms of their responsibility to address complaints). These scores are used by the regulator body to inform their decision‐making processes during and after responding to complaints.

The dataset contains a large number of complaints lodged against specific companies (each complaint includes details such as the specific company ID and complaint code). The description of what particular complaint codes refer to has been removed for reasons of conventionality. At the time when a complaint is lodged, additional company‐related data fields are also collected (from the complainant and external data sources) and recorded in the data set by the regulator.

You are asked to explore and analyse this data set. Specific tasks are:

Task 1: Use appropriate visualizations, descriptive statistics, and cluster analysis to demonstrate a thorough understanding of the data and extract informative data patterns for use by decision makers at the regulator

Task 2: Develop a predictive model to estimate the fitness score given to the company based on relevant data attributes. You must consider the significance of a variety of applicable attributes, and in particular also sector and location of the company involved.

Task 3: The regulator plans to develop a communication campaign aimed at addressing systemic co‐ occurring complaints across all companies. For this you are asked to identify the top 10 frequently co‐ occurring complaints.

Definitions of the data attributes are given in a separate data dictionary file in the assignment folder. It is recommended that you read the data definitions to better understand and consider the quality of the data prior to developing your analytics solutions. Your solutions should only draw on learning in the lectures and seminars of the unit.

Specific Requirements

This is a group assignment (2‐3 students maximum). Every group member must submit a declaration of their individual contribution as part of the submission. Students in each group will need to work together regularly on their assignment and submit their work as a group.

Groups should NOT discuss their work or collude with students of other groups. Please refer to the Deakin policy on Academic misconduct in this regard.

You must use the submission template for the assignment provided on Cloud Deakin for your report. Your final report must adhere strictly to the page limits in the template as only pages within the limits will be marked. It is essential that the executive summary section of your report is targeted at a non‐technical reader (e.g., a senior manager at the Regulator) and that the remaining parts of the report target a data/business analyst.

Your final deliverables must include:

  1. the final report according to the submission template as a PDF file
  2. all RapidMiner process files, combined into a single ZIP file.
  3. declaration of each student’s individual contribution to the submission (use provided form).

All submissions will need to be lodged via the CloudDeakin dropbox before the deadline. You should submit a partial submission of your work prior to the deadline. You should select and include relevant tables, charts, analysis processes, analysis results, models and the evaluation in your report.

You must use RapidMiner for this assignment. The use of Excel or any other analytics tools are therefore not permitted. You must include appropriate documentary notes within your RapidMiner process files make it easier to understand the logic. Use sub‐processes as appropriate to consolidate related analytical steps, and to improve the readability of your processes.

The consistency of your RapidMiner file(s) will be checked against the results in your report. You must NOT modify the data file provided for this assignment before importing it into RapidMiner.

MIS272 – Trimester 2 2021 - Predictive Analytics
MIS272 – Trimester 2 2021 – Predictive Analytics

Marking and feedback

The marking rubric for this assignment is available on the CloudDeakin unit site ‐ in the Assessment folder (under Assessment Resources).

You should familiarise yourself with the criteria before completing any part of the assessment. Criteria act as a boundary around the task and help identify what assessors are looking for specifically in your submission. The criteria are drawn from the unit’s learning outcomes ensuring they align with appropriate graduate attribute/s.

Identifying the standard you aim to achieve is also a useful strategy for success and to that end, familiarising yourself with the descriptor for that standard is recommended.

Students who submit their work by the due date will receive their marks and feedback on CloudDeakin 15 working days after the submission date.


There will be no extensions granted unless there are exceptional and most unusual circumstances outside the student’s control. Partial submissions will be considered as evidence of groups progress in this regard.

Students who require a time extension should submit a written request to the Unit Chair, supported with documentation (e.g., a medical certificate). Such requests should be e‐mailed to the Unit Chair. Requests for extensions will NOT be considered three days prior to submission.

Late submission

The following marking penalties will apply if you submit an assessment task after the due date without an approved extension: 5% will be deducted from available marks for each day up to five days, and work that is submitted more than five days after the due date will not be marked and will receive 0% for the task.

‘Day’ means working day for paper submissions and calendar day for electronic submissions. The Unit Chair may refuse to accept a late submission where it is unreasonable or impracticable to assess the task after the due date.

Calculation of the late penalty is as follows: this is based on the assignment being due on a Thursday

·         1 day late: submitted after 8pm on Thursday but before 8pm Friday – 5% penalty.

  • 2 days late: submitted after 8pm Friday but before Saturday 8pm – 10% penalty.
  • 3 days late: submitted after 8pm Saturday on due date but before Sunday 8pm – 15% penalty.
  • 4 days late: submitted after 8pm Sunday on due date but before Monday 8pm – 20% penalty.
  • 5 days late: submitted after 8pm Monday on due date but before Tuesday 8pm – 25% penalty.


The Division of Student Life (see link below) provides all students with editing assistance. Students who wish to take advantage of this service must be organized and plan ahead and contact the Division of Student Life in order to schedule a booking, well in advance of the due date of this assignment.‐deakin/administrative‐divisions/student‐life


Any material used in this assignment that is not your original work must be acknowledged as such and appropriately referenced. You can find information about plagiarism and other study support resources at the following website:‐support

Academic misconduct

For information about academic misconduct, special consideration, extensions, and assessment feedback, please refer to the document Your rights and responsibilities as a student in this Unit in the first folder next to the Unit Guide in the Resources area of the CloudDeakin unit site.

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