A local bank, BestBank, runs frequent marketing campaigns to promote their fixed deposit account. These marketing campaigns are based on phone calls made to random customers. The overall take-up rate for the fixed deposit account, especially though these marketing campaigns, has been on a decline and BestBank would like to use data mining to improve the rate. The dataset, Bank_FD.csv, relates to the campaigns ran in 2016 and the description of the variables is shown in Table 1.
(a) With reference to the context of the situation and the Bank_FD.csv dataset, explain the business problem. (10 marks)
(b) Translate the business problem (in 1 above) into an appropriate data mining objective to be used for predictive modelling. (10 marks)
(c) Provide the screenshot of the settings for the variable type in the source node and discuss the role of the variables. (25 marks)
(d) Construct a data mining model to achieve the data mining objective stated in 2. Show clearly in the stream the nodes that you have used to view the dataset and check the data quality of the dataset. Please provide a screenshot of the stream. Evaluate the stability of the model built based on the accuracy of the model. (20 marks)
(e) The CEO of BestBank suggested collecting more data (in terms of records) to improve the accuracy of the model built in 4. Discuss if more data will necessaily lead to an improvement in the accuracy of the model by giving three reasons to support your answer. (25 marks)
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