Who Buys Organic Food? Generating, Assessing and Scoring Predictive Models The Full Food supermarket wants to offer a new line of organic products. The supermarket's management would like to determine which customers are likely to purchase these products so that they can develop their marketing campaign. The supermarket has a customer loyalty program. As an initial buyer incentive plan, the supermarket provided coupons for the organic products to all of its loyalty program participants and has collected data that includes whether or not these customers purchased any of the organic products. For this assignment, please use the organic data set. The organic data set contains 22,223 observations about the costumers and reports 13 attributes about each. The variables in the data set are shown below with the appropriate roles and levels. Your goal for this assignment is to use the data to develop an analytical report for the management to take analytical based actions. To do that, you need to develop a report summarizing your analytical activities in way that is understandable and actionable by the management. Your analytical report should use at least t different modeling techniques (e.g., decision tree, regression and neural network) to predict which customers are likely to purchase organic products (TargetBuy =1 ) or predict how many organic products they would purchase (TargetAmt). Again, how the report is formatted and presented is up to you, but please note that your write up is a professional analytical report and should be formatted accordingly. Your report should be around 10-15 pages not including title page and exhibits. In your report you need to have an executive summary of not more than one page summarizing your findings and then expand on these findings in the body of the report. Include all the relevant EM screen captures and model descriptions in support of your recommendations. To develop your report and make recommendations, you need to perform at least the following activities and summarize and comment on the results. 1. Perform a data exploration to get a sense of what data you have and interpret your findings. 2. Overall, what is your impression of usefulness of this data for analytics? Are we missing anything that would enhance your predictive analytics