MIS772 Predictive Analytics Report
- Subject Code :
MIS772
- University :
Deakin University Exam Question Bank is not sponsored or endorsed by this college or university.
- Country :
Australia
Description
Purpose
This task provides you with opportunities to learn the knowledge and skills (ULO1 & GLO1) required in the study and practice of applying key statistical theories and data mining concepts to support evidence-based business decisions of an organisation. By completing this task, you will develop a specialised and integrated understanding of the application of business analytics to research, by designing and implementing projects with creativity and initiative.
The aim is to learn how to …
- Articulate problems and solutions in business terms
- Gain insights from data
- Prepare data for different models
- Develop certain models
- Assess and report model performance.
Context/Scenario
The business context for this assignment is the insurance sector, focusing on identifying fraudulent claims. Personal injury insurance fraud is defined as any act performed with the intention to cause an insurance company to compensate you for a non-existent, exaggerated or unrelated injury to the accident covered by your policy.
Specific Requirements
A legal firm, DAX Compensation Lawyers, approached you to assist them with identifying fraudulent claims.
The firm have provided you with a historic dataset of over 3000 claims.
The list includes the following information:
- some information about the claimant
- injured body part
- the nature and cause of injury
- adjustor notes taken by the insurance employees after contacting the claimants or their employers
- whether a witness was present
- whether the injury involved a vehicle2
- whether the case ended in the recovery of all paid entitlements and costs through subrogation
- whether the claim was detected as fraudulent
The firm would like you to use RapidMiner to address the following:
Task A:
Explore various aspects of the claims, e.g., is there a specific claimant type that is more likely to make a fraudulent claim? Or, are fraudulent claims more prevalent when certain body parts are indicated?
Task B:
Develop different classification models that can be used by the firm managers to predict which cases will likely be fraudulent, using appropriate attributes in the dataset. Evaluate the performance of each model, indicating the best predictive model (maximising the correct identification of fraudulent claims while minimising misclassification of fraud-free ones).
- The dataset, report template, and additional important notes (A1 Notes) for this assignment are available on the unit site (under Content->Assessment Resources).
- You must use the provided template for your report. Your final report must adhere to the page limits as only pages within the limits will be marked. It is essential that the executive summary section of your report is written for a non-technical reader (e.g., a senior manager) and that the remaining parts of the report are written for a technical reader (e.g., a business analyst or data scientist).
- You must only use RapidMiner for your analytical process modelling.
- 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.
Learning Outcomes
This task allows you to demonstrate your achievement towards the Unit Learning Outcomes (ULOs) which have been aligned to the Deakin Graduate Learning Outcomes (GLOs). Deakin GLOs describe the knowledge and capabilities graduates acquire and can demonstrate on completion of their course. This assessment task is an important tool in determining your achievement of the ULOs. If you do not demonstrate achievement of the ULOs you will not be successful in this unit. You are advised to familiarise yourself with these ULOs and GLOs as they will inform you on what you are expected to demonstrate for successful completion of this unit.
The learning outcomes that are aligned to this assessment task are:
| Unit Learning Outcomes (ULOs) | Graduate Learning Outcomes (GLOs) | |
|---|---|---|
| ULO1 | Understand and apply key statistical theories and data mining concepts. | GLO1: Demonstrate a specialised and integrated understanding of contemporary body of knowledge of business analytics to research, design and implement projects with creativity and initiative. |
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