MidWest University Foundation (MWUF) wishes to improve the cost- effectiveness of their direct marketing campaigns to previous donors. According to their recent mailing records, the typical overall response rate is 10%. Out of those who respond (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 and assortment of cards and envelopes. It is not cost-effective to mail everyone because the expected profit from each mailing is 14.50 x 0.10 - 2 = -$0.55. We 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. We would also like to build a prediction model to predict expected gift amounts from donors - the data for this will consist of therecords for donors only. The data are available in the file "MWUF.csv" (available in Canvas):

3. In Rstudio, Conduct exploratory data analysis on the data set prior to building classification and prediction models. a. Look at the correlations between donation amount (DAMT) and potential input variables for predicting DAMT and also present these correlations in a table. b. Use proper data visualization tools to explore relations between potential input variables to predict DONR and DONR (do not include more than 5 visualizations).