Sas Enterprise Miner -- 2

You have been hired by a non-profit organization that wishes to develop predictive models to improve the cost-effectiveness of their direct marketing campaigns to prior donors. According to their recent records, the typical overall response rate is approximately 10%. Out of those who respond and donate to the mailing, the average donation is $14.50. Each mailing costs $2.00 to produce and send; the mailing includes a gift of personalized address labels. It is not cost-effective to mail everyone because the expected profit from each mailing is 14.50 x 0.10 – 2 = -$0.55.
Your client would like to develop a classification model using data from the most recent campaign that can effectively capture likely donors so that the expected net profit is maximized (i.e., ideally maximize the number of donors who receive a flyer and minimize the recipients who are non-donors). The entire dataset consists of 3,984 training observations, 2018 validation observations, and 2007 test observations.
Note that weighted sampling has been used, over-representing the responders so that the training and validation samples have approximately equal numbers of donors and non-donors (e.g., the dataset is balanced). The response rate in the test sample has the more typical 10% response rate. Your client would also like a prediction model to predict expected gift amounts from donors – the data for this will consist of the records for donors only.
Your client’s objective is to achieve a maximum return on their capital; thus, they would like to know what the expected lift in net proceeds is (the difference between the expected profits using your model and their expected profits using their typical response rate) to their organization. See the rubric in Canvas for a detailed discussion of the project requirements.