Description

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 confidentiality. 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, explorative analytics, 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 policy 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. You will need to join the files (companies and complaints) to be able to address the tasks above. 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:

i)             the final report according to the submission template as a PDF file

ii)            all RapidMiner process files, combined into a single ZIP file.

iii)          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.

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.

Extensions

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.