In the fall of 2019, the administration of a large private university requested that the Office of ! Management and the Office of Institutional Research work together to identify prospective stu would most likely enroll as new freshmen in the Fall 2020 semester. Historically, inquiries num about 90, 000+ students, and the university enrolled from 2400 to 2800 new freshmen each Fall It was decided that inquiries for Fall 2019 would be used to build the model to help shape the I freshman class. The data set IN Q2019 was built over a period of a several months in consul tati Enrollment Management. Please carefully explore all variables and build a predictive model fo enrollment management. Please apply regression and decision tree models to analyze the data. Variable and model naming requirements 0 Please include your name initials to the data frame names as well as model na your R. coding. 0 Please instance, in my coding, I would name the data frames as dfKZ, dfKZ.train dfKZ.valid I would also name the models as regressionKZ, treeKZ, etc. Please submit a Word document including; 1. A table showing the overall structure of the dataset, induding variable names, data typ whether the variables will be used in your analyses. Also, please answer questions c, d a. The nominal variables ACADEMIC_INTEREST_1, ACADEMIC_INTER IRSCHOOL were rejected because they were replaced by the interval variable INT1RAT, INT2RAT, and HSCRAT, respectively. For example, academic in codes 1 and 2 were replaced by the percentage of inquirers over the past five y indicated those interest codes and then enrolled. The variable IRSCHOOL is school code of the student, and it was replaced by the percentage of inquirers f high school over the last five years who enrolled. b . CONTACT_CODE1 and CONTACT_DATE1 are also rejected due to their i suggested by Enrollment Management. :. Should your model reject any other variables for your analyses? If so, please reasons for each additionally rejected variable. i. Which variable is your target variable? e. Do you need to change data types or measurement levels of your existing varie binary, numeric, factor)? Why? 2. Explain whether variable imputation and transformation are needed for the regression so, please explain which variables have been imputed, transformed and how