Case Study Description This case is about a Portuguese banking institution, which has a growing customer base. The bank manager would like to employ data analytics and machine learning to analyse its customer data for bank direct marketing campaign. The BANK marketing campaigns were based on phone calls. Often, more than one contact to the same client was required. The data set contains approximately 45,211 observations with 17 Image source: http://www.completecontroller.com/6-things-you-must- comilder-when-chanting-your-banking-Institution/ variables as described in the below: bank client data: 1 - age (numeric) 2 - job: type of job (categorical) 3 - marital: marital status (categorical) 5 - default: has credit in default? (binary) 6 - balance: average yearly balance, in euros (numeric) 7 - housing: has housing loan? (binary) 8 - loon: has personal loan? (binary) related with the last contact of the current campaign: 9 - contact: contact communication type (categorical) 10 - day: last contact day of the month (numeric) 11 - month: last contact month of year (categorical) 12 - duration: last contact duration, in seconds (numeric) other attributes: 13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted) 15 - previous: number of contacts performed before this campaign and for this client (numeric) 16 - poutcome: outcome of the previous marketing campaign (categorical) 17 - y - has the client subscribed a term deposit? (binary) Financial Al are interested in generating some insights about the clients, especially answering the below questions: A. What is the distribution of customer age by marital status? B. What are the (top 5) most popular occupations among the bank customers? Among them, which occupation has the highest average yearly balance? Which occupations has the most people completed tertiary education? C. How to reliably predict if the client will subscribe to the term deposit? Define appropriate measures and compare the performance of different classifiers to predict client's subscription. Financial Al wants you to use RapidMiner to process and explore the provided data, and then develop and evaluate classifiers to predict the customer's subscriptions to the term deposit, and to minimise misclassifications. The data set is available on Cloud Deakin site, named MIS771 A1 data.zip. you will need to unzip the file before importing into RapidMiner