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A predictive algorithm for loan repayment likelihood using the CRISP-DM method The report in the link above needs rewriting according to the criteria mentioned below, and in the attached documents. Business Understanding (take this seriously) Identify, define, and motivate the business problem that you are addressing. How (precisely) will a data mining solution address the business problem? (NB: I’d like to see a good definition/motivation of the business problem and a precise statement of how a data mining solution will address the problem. It’s not so important that the hands-on results match perfectly. It’s more important that you have the experience of working through a realistic problem definition.) Data Understanding • Identify and describe the data (and data sources) that will support data mining to address the business problem. Include those aspects of the data that we talk about in class and/or in the quizzes. This should include some exploration of the data, such as: o Summarystatistics o Visualisationusinggraphs/charts Data Preparation Specify how these data are integrated and prepared to produce the format required for data mining. (NB: data preparation can be time consuming. Get started early. Talk to me if you need advice.) Modelling Specify the type of model(s) built and/or patterns mined. Discuss choices for data mining algorithm: what are alternatives, and what are the pros and cons? How did you evaluate each of the models? Discuss why and how this model should “solve” the business problem (i.e., improve along some dimension of interest to the firm). Evaluation • Discuss how the result of the data mining is/should be evaluated. How should a business case be developed to project expected improvement? ROI? If this is impossible/very difficult, explain why and identify any viable alternatives. Deployment Discuss how the result of the data mining will be deployed. Discuss any issues the firm should be aware of regarding deployment. Are there important ethical considerations? Identify the risks associated with your proposed plan and how you would mitigate them.