Order Description
Having covered the CRISP-DM methodology at length at the university, you decide to apply it to this project.
You are free to make reasonable assumptions about possible data sources. However, keep in mind that some data may not be allowed to be used in the Netherlands. You need not know the precise laws in the Netherlands or elsewhere, just highlight your legal/ethical concerns if any arise.
(a) Elaborate on the Business Understanding: determine business objectives and possible ways to achieve them. Assess the situation, making assumptions where necessary, and determine data mining goals. [35%]
(b) DiscussthenextstagesofDataUnderstandingandDataPreparation.Howdoesyour plan of these stages look like? Think of additional data sources that might be useful for this problem. Be creative but realistic. Describe all data sources in terms of their expected properties (structured, unstructured, 4Vs). Comment on practical challenges that may arise from using these sources. [30%]
(c) What variable do you expect to use as target? What specific challenges your predictive analytics on detecting fraudulent claims might face using the past data? Why will you need to partition the data for predictive modelling? Will over-sampling be needed? [35%]

Order Description
Having covered the CRISP-DM methodology at length at the university, you decide to apply it to this project.
You are free to make reasonable assumptions about possible data sources. However, keep in mind that some data may not be allowed to be used in the Netherlands. You need not know the precise laws in the Netherlands or elsewhere, just highlight your legal/ethical concerns if any arise.
(a) Elaborate on the Business Understanding: determine business objectives and possible ways to achieve them. Assess the situation, making assumptions where necessary, and determine data mining goals. [35%]
(b) DiscussthenextstagesofDataUnderstandingandDataPreparation.Howdoesyour plan of these stages look like? Think of additional data sources that might be useful for this problem. Be creative but realistic. Describe all data sources in terms of their expected properties (structured, unstructured, 4Vs). Comment on practical challenges that may arise from using these sources. [30%]
(c) What variable do you expect to use as target? What specific challenges your predictive analytics on detecting fraudulent claims might face using the past data? Why will you need to partition the data for predictive modelling? Will over-sampling be needed? [35%]